diff --git a/Eigen/Core b/Eigen/Core
index 985fea7..8255815 100644
--- a/Eigen/Core
+++ b/Eigen/Core
@@ -14,16 +14,6 @@
 // first thing Eigen does: stop the compiler from committing suicide
 #include "src/Core/util/DisableStupidWarnings.h"
 
-/// This will no longer be needed after the next release of the computecppCE
-#ifdef EIGEN_USE_SYCL
-#undef min
-#undef max
-#undef isnan
-#undef isinf
-#undef isfinite
-#include <SYCL/sycl.hpp>
-#endif
-
 // Handle NVCC/CUDA/SYCL
 #if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)
   // Do not try asserts on CUDA and SYCL!
diff --git a/cmake/EigenTesting.cmake b/cmake/EigenTesting.cmake
index 9ebd06a..a92a297 100644
--- a/cmake/EigenTesting.cmake
+++ b/cmake/EigenTesting.cmake
@@ -1,23 +1,23 @@
 
 macro(ei_add_property prop value)
-  get_property(previous GLOBAL PROPERTY ${prop})  
+  get_property(previous GLOBAL PROPERTY ${prop})
   if ((NOT previous) OR (previous STREQUAL ""))
     set_property(GLOBAL PROPERTY ${prop} "${value}")
   else()
     set_property(GLOBAL PROPERTY ${prop} "${previous} ${value}")
-  endif()  
+  endif()
 endmacro(ei_add_property)
 
 #internal. See documentation of ei_add_test for details.
 macro(ei_add_test_internal testname testname_with_suffix)
   set(targetname ${testname_with_suffix})
-  
+
   if(EIGEN_ADD_TEST_FILENAME_EXTENSION)
     set(filename ${testname}.${EIGEN_ADD_TEST_FILENAME_EXTENSION})
   else()
     set(filename ${testname}.cpp)
   endif()
-  
+
   if(EIGEN_ADD_TEST_FILENAME_EXTENSION STREQUAL cu)
     if(EIGEN_TEST_CUDA_CLANG)
       set_source_files_properties(${filename} PROPERTIES LANGUAGE CXX)
@@ -42,7 +42,7 @@
   else()
     add_executable(${targetname} ${filename})
   endif()
-  
+
   if (targetname MATCHES "^eigen2_")
     add_dependencies(eigen2_buildtests ${targetname})
   else()
@@ -56,20 +56,20 @@
       ei_add_target_property(${targetname} COMPILE_FLAGS "-DEIGEN_DEBUG_ASSERTS=1")
     endif(EIGEN_DEBUG_ASSERTS)
   endif(EIGEN_NO_ASSERTION_CHECKING)
-  
+
   ei_add_target_property(${targetname} COMPILE_FLAGS "-DEIGEN_TEST_MAX_SIZE=${EIGEN_TEST_MAX_SIZE}")
 
   ei_add_target_property(${targetname} COMPILE_FLAGS "-DEIGEN_TEST_FUNC=${testname}")
-  
+
   if(MSVC)
     ei_add_target_property(${targetname} COMPILE_FLAGS "/bigobj")
-  endif()  
+  endif()
 
   # let the user pass flags.
   if(${ARGC} GREATER 2)
     ei_add_target_property(${targetname} COMPILE_FLAGS "${ARGV2}")
   endif(${ARGC} GREATER 2)
-  
+
   if(EIGEN_TEST_CUSTOM_CXX_FLAGS)
     ei_add_target_property(${targetname} COMPILE_FLAGS "${EIGEN_TEST_CUSTOM_CXX_FLAGS}")
   endif()
@@ -95,12 +95,12 @@
       # notice: no double quotes around ${libs_to_link} here. It may be a list.
       target_link_libraries(${targetname} ${libs_to_link})
     endif()
-  endif() 
+  endif()
 
   add_test(${testname_with_suffix} "${targetname}")
-  
+
   # Specify target and test labels accoirding to EIGEN_CURRENT_SUBPROJECT
-  get_property(current_subproject GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT)  
+  get_property(current_subproject GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT)
   if ((current_subproject) AND (NOT (current_subproject STREQUAL "")))
     set_property(TARGET ${targetname} PROPERTY LABELS "Build${current_subproject}")
     add_dependencies("Build${current_subproject}" ${targetname})
@@ -128,14 +128,14 @@
     OUTPUT ${include_file}
     COMMAND ${CMAKE_COMMAND} -E echo "\\#include \\\"${host_file}\\\"" > ${include_file}
     COMMAND ${CMAKE_COMMAND} -E echo "\\#include \\\"${bc_file}.sycl\\\"" >> ${include_file}
-    DEPENDS ${filename}
+    DEPENDS ${filename} ${bc_file}.sycl
     COMMENT "Building ComputeCpp integration header file ${include_file}"
   )
   # Add a custom target for the generated integration header
-  add_custom_target(${testname}_integration_header_woho DEPENDS ${include_file})
+  add_custom_target(${testname}_integration_header_sycl DEPENDS ${include_file})
 
   add_executable(${targetname} ${include_file})
-  add_dependencies(${targetname} ${testname}_integration_header_woho)
+  add_dependencies(${targetname} ${testname}_integration_header_sycl)
   add_sycl_to_target(${targetname} ${filename} ${CMAKE_CURRENT_BINARY_DIR})
 
   if (targetname MATCHES "^eigen2_")
@@ -258,7 +258,7 @@
   else()
     set(filename ${testname}.cpp)
   endif()
-  
+
   file(READ "${filename}" test_source)
   set(parts 0)
   string(REGEX MATCHALL "CALL_SUBTEST_[0-9]+|EIGEN_TEST_PART_[0-9]+|EIGEN_SUFFIXES(;[0-9]+)+"
@@ -379,7 +379,7 @@
   elseif(EIGEN_TEST_NO_EXPLICIT_VECTORIZATION)
     message(STATUS "Explicit vectorization disabled (alignment kept enabled)")
   else()
-  
+
   message(STATUS "Maximal matrix/vector size: ${EIGEN_TEST_MAX_SIZE}")
 
     if(EIGEN_TEST_SSE2)
@@ -453,13 +453,13 @@
     else()
       message(STATUS "ARMv8 NEON:        Using architecture defaults")
     endif()
- 
+
     if(EIGEN_TEST_ZVECTOR)
       message(STATUS "S390X ZVECTOR:     ON")
     else()
       message(STATUS "S390X ZVECTOR:     Using architecture defaults")
     endif()
-   
+
     if(EIGEN_TEST_CXX11)
       message(STATUS "C++11:             ON")
     else()
@@ -505,7 +505,7 @@
 
   set_property(GLOBAL PROPERTY EIGEN_FAILTEST_FAILURE_COUNT "0")
   set_property(GLOBAL PROPERTY EIGEN_FAILTEST_COUNT "0")
-  
+
   # uncomment anytime you change the ei_get_compilerver_from_cxx_version_string macro
   # ei_test_get_compilerver_from_cxx_version_string()
 endmacro(ei_init_testing)
@@ -514,22 +514,22 @@
   # if the sitename is not yet set, try to set it
   if(NOT ${SITE} OR ${SITE} STREQUAL "")
     set(eigen_computername $ENV{COMPUTERNAME})
-	set(eigen_hostname $ENV{HOSTNAME})
+    set(eigen_hostname $ENV{HOSTNAME})
     if(eigen_hostname)
       set(SITE ${eigen_hostname})
-	elseif(eigen_computername)
-	  set(SITE ${eigen_computername})
+    elseif(eigen_computername)
+      set(SITE ${eigen_computername})
     endif()
   endif()
   # in case it is already set, enforce lower case
   if(SITE)
     string(TOLOWER ${SITE} SITE)
-  endif()  
+  endif()
 endmacro(ei_set_sitename)
 
 macro(ei_get_compilerver VAR)
     if(MSVC)
-      # on windows system, we use a modified CMake script  
+      # on windows system, we use a modified CMake script
       include(EigenDetermineVSServicePack)
       EigenDetermineVSServicePack( my_service_pack )
 
@@ -541,20 +541,20 @@
     else()
     # on all other system we rely on ${CMAKE_CXX_COMPILER}
     # supporting a "--version" or "/version" flag
-    
+
     if(WIN32 AND ${CMAKE_CXX_COMPILER_ID} EQUAL "Intel")
       set(EIGEN_CXX_FLAG_VERSION "/version")
     else()
       set(EIGEN_CXX_FLAG_VERSION "--version")
     endif()
-    
+
     execute_process(COMMAND ${CMAKE_CXX_COMPILER} ${EIGEN_CXX_FLAG_VERSION}
                     OUTPUT_VARIABLE eigen_cxx_compiler_version_string OUTPUT_STRIP_TRAILING_WHITESPACE)
     string(REGEX REPLACE "[\n\r].*"  ""  eigen_cxx_compiler_version_string  ${eigen_cxx_compiler_version_string})
-    
+
     ei_get_compilerver_from_cxx_version_string("${eigen_cxx_compiler_version_string}" CNAME CVER)
     set(${VAR} "${CNAME}-${CVER}")
-    
+
   endif()
 endmacro(ei_get_compilerver)
 
@@ -563,13 +563,13 @@
 # the testing macro call in ei_init_testing() of the EigenTesting.cmake file.
 # See also the ei_test_get_compilerver_from_cxx_version_string macro at the end of the file
 macro(ei_get_compilerver_from_cxx_version_string VERSTRING CNAME CVER)
-  # extract possible compiler names  
+  # extract possible compiler names
   string(REGEX MATCH "g\\+\\+"      ei_has_gpp    ${VERSTRING})
   string(REGEX MATCH "llvm|LLVM"    ei_has_llvm   ${VERSTRING})
   string(REGEX MATCH "gcc|GCC"      ei_has_gcc    ${VERSTRING})
   string(REGEX MATCH "icpc|ICC"     ei_has_icpc   ${VERSTRING})
   string(REGEX MATCH "clang|CLANG"  ei_has_clang  ${VERSTRING})
-  
+
   # combine them
   if((ei_has_llvm) AND (ei_has_gpp OR ei_has_gcc))
     set(${CNAME} "llvm-g++")
@@ -584,7 +584,7 @@
   else()
     set(${CNAME} "_")
   endif()
-  
+
   # extract possible version numbers
   # first try to extract 3 isolated numbers:
   string(REGEX MATCH " [0-9]+\\.[0-9]+\\.[0-9]+" eicver ${VERSTRING})
@@ -602,9 +602,9 @@
       endif()
     endif()
   endif()
-  
+
   string(REGEX REPLACE ".(.*)" "\\1" ${CVER} ${eicver})
-  
+
 endmacro(ei_get_compilerver_from_cxx_version_string)
 
 macro(ei_get_cxxflags VAR)
@@ -633,30 +633,30 @@
   elseif(EIGEN_TEST_SSE3)
     set(${VAR} SSE3)
   elseif(EIGEN_TEST_SSE2 OR IS_64BIT_ENV)
-    set(${VAR} SSE2)  
+    set(${VAR} SSE2)
   endif()
 
   if(EIGEN_TEST_OPENMP)
     if (${VAR} STREQUAL "")
-	  set(${VAR} OMP)
-	else()
-	  set(${VAR} ${${VAR}}-OMP)
-	endif()
+      set(${VAR} OMP)
+    else()
+      set(${VAR} ${${VAR}}-OMP)
+    endif()
   endif()
-  
+
   if(EIGEN_DEFAULT_TO_ROW_MAJOR)
     if (${VAR} STREQUAL "")
-	  set(${VAR} ROW)
-	else()
-	  set(${VAR} ${${VAR}}-ROWMAJ)
-	endif()  
+      set(${VAR} ROW)
+    else()
+      set(${VAR} ${${VAR}}-ROWMAJ)
+    endif()
   endif()
 endmacro(ei_get_cxxflags)
 
 macro(ei_set_build_string)
   ei_get_compilerver(LOCAL_COMPILER_VERSION)
   ei_get_cxxflags(LOCAL_COMPILER_FLAGS)
-  
+
   include(EigenDetermineOSVersion)
   DetermineOSVersion(OS_VERSION)
 
@@ -672,11 +672,11 @@
   else()
     set(TMP_BUILD_STRING ${TMP_BUILD_STRING}-64bit)
   endif()
-  
+
   if(EIGEN_TEST_CXX11)
     set(TMP_BUILD_STRING ${TMP_BUILD_STRING}-cxx11)
   endif()
-  
+
   if(EIGEN_BUILD_STRING_SUFFIX)
     set(TMP_BUILD_STRING ${TMP_BUILD_STRING}-${EIGEN_BUILD_STRING_SUFFIX})
   endif()
diff --git a/cmake/FindComputeCpp.cmake b/cmake/FindComputeCpp.cmake
index 3aab5b8..07ebed6 100644
--- a/cmake/FindComputeCpp.cmake
+++ b/cmake/FindComputeCpp.cmake
@@ -1,6 +1,21 @@
 #.rst:
 # FindComputeCpp
 #---------------
+#
+#   Copyright 2016 Codeplay Software Ltd.
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use these files except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
 
 #########################
 #  FindComputeCpp.cmake
@@ -8,6 +23,11 @@
 #
 #  Tools for finding and building with ComputeCpp.
 #
+#  User must define COMPUTECPP_PACKAGE_ROOT_DIR pointing to the ComputeCpp
+#   installation.
+#
+#  Latest version of this file can be found at:
+#    https://github.com/codeplaysoftware/computecpp-sdk
 
 # Require CMake version 3.2.2 or higher
 cmake_minimum_required(VERSION 3.2.2)
@@ -32,7 +52,6 @@
       message(FATAL_ERROR
         "host compiler - Not found! (clang version must be at least 3.6)")
     else()
-			set(COMPUTECPP_DISABLE_GCC_DUAL_ABI "True")
       message(STATUS "host compiler - clang ${CMAKE_CXX_COMPILER_VERSION}")
     endif()
 else()
@@ -48,11 +67,12 @@
 # Find OpenCL package
 find_package(OpenCL REQUIRED)
 
-# Find ComputeCpp package
-if(EXISTS ${COMPUTECPP_PACKAGE_ROOT_DIR})
-  message(STATUS "ComputeCpp package - Found (${COMPUTECPP_PACKAGE_ROOT_DIR})")
+# Find ComputeCpp packagee
+if(NOT COMPUTECPP_PACKAGE_ROOT_DIR)
+  message(FATAL_ERROR
+    "ComputeCpp package - Not found! (please set COMPUTECPP_PACKAGE_ROOT_DIR")
 else()
-  message(FATAL_ERROR "ComputeCpp package - Not found! (please set COMPUTECPP_PACKAGE_ROOT_DIR) (${COMPUTECPP_PACKAGE_ROOT_DIR})")
+  message(STATUS "ComputeCpp package - Found")
 endif()
 option(COMPUTECPP_PACKAGE_ROOT_DIR "Path to the ComputeCpp Package")
 
@@ -61,9 +81,9 @@
   ${COMPUTECPP_PACKAGE_ROOT_DIR} PATH_SUFFIXES bin)
 if (EXISTS ${COMPUTECPP_DEVICE_COMPILER})
   mark_as_advanced(COMPUTECPP_DEVICE_COMPILER)
-  message(STATUS "compute++ - Found (${COMPUTECPP_PACKAGE_ROOT_DIR})")
+  message(STATUS "compute++ - Found")
 else()
-  message(FATAL_ERROR "compute++ - Not found! (${COMPUTECPP_DEVICE_COMPILER}) (${COMPUTECPP_PACKAGE_ROOT_DIR})")
+  message(FATAL_ERROR "compute++ - Not found! (${COMPUTECPP_DEVICE_COMPILER})")
 endif()
 
 # Obtain the path to computecpp_info
@@ -71,9 +91,9 @@
   ${COMPUTECPP_PACKAGE_ROOT_DIR} PATH_SUFFIXES bin)
 if (EXISTS ${COMPUTECPP_INFO_TOOL})
   mark_as_advanced(${COMPUTECPP_INFO_TOOL})
-  message(STATUS "computecpp_info - Found (${COMPUTECPP_PACKAGE_ROOT_DIR})")
+  message(STATUS "computecpp_info - Found")
 else()
-  message(FATAL_ERROR "computecpp_info - Not found! (${COMPUTECPP_INFO_TOOL}) (${COMPUTECPP_PACKAGE_ROOT_DIR})")
+  message(FATAL_ERROR "computecpp_info - Not found! (${COMPUTECPP_INFO_TOOL})")
 endif()
 
 # Obtain the path to the ComputeCpp runtime library
@@ -85,15 +105,15 @@
   mark_as_advanced(COMPUTECPP_RUNTIME_LIBRARY)
   message(STATUS "libComputeCpp.so - Found")
 else()
-  message(FATAL_ERROR "libComputeCpp.so - Not found! (${COMPUTECPP_PACKAGE_ROOT_DIR})")
+  message(FATAL_ERROR "libComputeCpp.so - Not found!")
 endif()
 
 # Obtain the ComputeCpp include directory
 set(COMPUTECPP_INCLUDE_DIRECTORY ${COMPUTECPP_PACKAGE_ROOT_DIR}/include/)
 if (NOT EXISTS ${COMPUTECPP_INCLUDE_DIRECTORY})
-  message(FATAL_ERROR "ComputeCpp includes - Not found! (${COMPUTECPP_PACKAGE_ROOT_DIR}/include/)")
+  message(FATAL_ERROR "ComputeCpp includes - Not found!")
 else()
-  message(STATUS "ComputeCpp includes - Found (${COMPUTECPP_PACKAGE_ROOT_DIR})")
+  message(STATUS "ComputeCpp includes - Found")
 endif()
 
 # Obtain the package version
@@ -144,7 +164,7 @@
 #
 #  targetName : Name of the target.
 #  sourceFile : Source file to be compiled.
-#  binaryDir  : Intermediate output directory for the integration header.
+#  binaryDir : Intermediate directory to output the integration header.
 #
 function(__build_spir targetName sourceFile binaryDir)
 
@@ -176,12 +196,13 @@
     OUTPUT ${outputSyclFile}
     COMMAND ${COMPUTECPP_DEVICE_COMPILER}
             ${COMPUTECPP_DEVICE_COMPILER_FLAGS}
-            -I${COMPUTECPP_INCLUDE_DIRECTORY}
+            -isystem ${COMPUTECPP_INCLUDE_DIRECTORY}
             ${COMPUTECPP_PLATFORM_SPECIFIC_ARGS}
             ${device_compiler_includes}
             -o ${outputSyclFile}
             -c ${CMAKE_CURRENT_SOURCE_DIR}/${sourceFile}
     DEPENDS ${sourceFile}
+    WORKING_DIRECTORY ${binaryDir}
   COMMENT "Building ComputeCpp integration header file ${outputSyclFile}")
 
   # Add a custom target for the generated integration header
@@ -190,10 +211,6 @@
   # Add a dependency on the integration header
   add_dependencies(${targetName} ${targetName}_integration_header)
 
-  # Force inclusion of the integration header for the host compiler
-  #set(compileFlags -include ${include_file} "-Wall")
-  target_compile_options(${targetName} PUBLIC ${compileFlags})
-
   # Set the host compiler C++ standard to C++11
   set_property(TARGET ${targetName} PROPERTY CXX_STANDARD 11)
 
@@ -210,11 +227,11 @@
 #######################
 #
 #  Adds a SYCL compilation custom command associated with an existing
-#  target and sets a dependency on that new command.
+#  target and sets a dependancy on that new command.
 #
 #  targetName : Name of the target to add a SYCL to.
 #  sourceFile : Source file to be compiled for SYCL.
-#  binaryDir  : Intermediate output directory for the integration header.
+#  binaryDir : Intermediate directory to output the integration header.
 #
 function(add_sycl_to_target targetName sourceFile binaryDir)
 
diff --git a/unsupported/Eigen/CXX11/Tensor b/unsupported/Eigen/CXX11/Tensor
index 388976d..7ecb4c7 100644
--- a/unsupported/Eigen/CXX11/Tensor
+++ b/unsupported/Eigen/CXX11/Tensor
@@ -13,6 +13,18 @@
 
 #include "../../../Eigen/Core"
 
+#ifdef EIGEN_USE_SYCL
+#undef min
+#undef max
+#undef isnan
+#undef isinf
+#undef isfinite
+#include <SYCL/sycl.hpp>
+#include <map>
+#include <memory>
+#include <utility>
+#endif
+
 #include <Eigen/src/Core/util/DisableStupidWarnings.h>
 
 #include "../SpecialFunctions"
@@ -69,10 +81,6 @@
 #endif
 #endif
 
-#ifdef EIGEN_USE_SYCL
-#include <SYCL/sycl.hpp>
-#endif
-
 #include "src/Tensor/TensorMacros.h"
 #include "src/Tensor/TensorForwardDeclarations.h"
 #include "src/Tensor/TensorMeta.h"
@@ -81,7 +89,6 @@
 #include "src/Tensor/TensorDeviceDefault.h"
 #include "src/Tensor/TensorDeviceThreadPool.h"
 #include "src/Tensor/TensorDeviceCuda.h"
-#include "src/Tensor/TensorSycl.h"
 #include "src/Tensor/TensorDeviceSycl.h"
 #include "src/Tensor/TensorIndexList.h"
 #include "src/Tensor/TensorDimensionList.h"
@@ -128,6 +135,7 @@
 #include "src/Tensor/TensorAssign.h"
 #include "src/Tensor/TensorScan.h"
 
+#include "src/Tensor/TensorSycl.h"
 #include "src/Tensor/TensorExecutor.h"
 #include "src/Tensor/TensorDevice.h"
 
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
index bfd36f5..7c03989 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h
@@ -1,12 +1,11 @@
 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
-// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
 // Mehdi Goli    Codeplay Software Ltd.
 // Ralph Potter  Codeplay Software Ltd.
 // Luke Iwanski  Codeplay Software Ltd.
-// Cummins Chris PhD student at The University of Edinburgh.
 // Contact: <eigen@codeplay.com>
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
 
 //
 // This Source Code Form is subject to the terms of the Mozilla
@@ -17,106 +16,107 @@
 #define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
 
 namespace Eigen {
-/// \struct BufferT is used to specialise add_sycl_buffer function for
-//  two types of buffer we have. When the MapAllocator is true, we create the
-//  sycl buffer with MapAllocator.
-/// We have to const_cast the input pointer in order to work around the fact
-/// that sycl does not accept map allocator for const pointer.
-template <typename T, bool MapAllocator>
-struct BufferT {
-  using Type = cl::sycl::buffer<T, 1, cl::sycl::map_allocator<T>>;
-  static inline void add_sycl_buffer(
-      const T *ptr, size_t num_bytes,
-      std::map<const void *, std::shared_ptr<void>> &buffer_map) {
-    buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(
-        ptr, std::shared_ptr<void>(std::make_shared<Type>(
-                 Type(const_cast<T *>(ptr), cl::sycl::range<1>(num_bytes))))));
-  }
-};
-
-/// specialisation of the \ref BufferT when the MapAllocator is false. In this
-/// case we only create the device-only buffer.
-template <typename T>
-struct BufferT<T, false> {
-  using Type = cl::sycl::buffer<T, 1>;
-  static inline void add_sycl_buffer(
-      const T *ptr, size_t num_bytes,
-      std::map<const void *, std::shared_ptr<void>> &buffer_map) {
-    buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(
-        ptr, std::shared_ptr<void>(
-                 std::make_shared<Type>(Type(cl::sycl::range<1>(num_bytes))))));
-  }
-};
-
 struct SyclDevice {
   /// class members
   /// sycl queue
-  cl::sycl::queue &m_queue;
+  mutable cl::sycl::queue m_queue;
   /// std::map is the container used to make sure that we create only one buffer
-  /// per pointer. The lifespan of the buffer
-  /// now depends on the lifespan of SyclDevice. If a non-read-only pointer is
-  /// needed to be accessed on the host we should manually deallocate it.
+  /// per pointer. The lifespan of the buffer now depends on the lifespan of SyclDevice.
+  /// If a non-read-only pointer is needed to be accessed on the host we should manually deallocate it.
   mutable std::map<const void *, std::shared_ptr<void>> buffer_map;
-
-  SyclDevice(cl::sycl::queue &q) : m_queue(q) {}
+  /// creating device by using selector
+  template<typename dev_Selector> SyclDevice(dev_Selector s)
+  :
+#ifdef EIGEN_EXCEPTIONS
+  m_queue(cl::sycl::queue(s, [=](cl::sycl::exception_list l) {
+    for (const auto& e : l) {
+      try {
+        std::rethrow_exception(e);
+      } catch (cl::sycl::exception e) {
+          std::cout << e.what() << std::endl;
+        }
+    }
+  }))
+#else
+  m_queue(cl::sycl::queue(s))
+#endif
+  {}
   // destructor
   ~SyclDevice() { deallocate_all(); }
 
-  template <typename T>
-  void deallocate(const T *p) const {
+  template <typename T> void deallocate(T *p) const {
     auto it = buffer_map.find(p);
     if (it != buffer_map.end()) {
       buffer_map.erase(it);
+      internal::aligned_free(p);
     }
   }
-  void deallocate_all() const { buffer_map.clear(); }
+  void deallocate_all() const {
+    std::map<const void *, std::shared_ptr<void>>::iterator it=buffer_map.begin();
+    while (it!=buffer_map.end()) {
+      auto p=it->first;
+      buffer_map.erase(it);
+      internal::aligned_free(const_cast<void*>(p));
+      it=buffer_map.begin();
+    }
+    buffer_map.clear();
+  }
 
   /// creation of sycl accessor for a buffer. This function first tries to find
-  /// the buffer in the buffer_map.
-  /// If found it gets the accessor from it, if not, the function then adds an
-  /// entry by creating a sycl buffer
-  /// for that particular pointer.
-  template <cl::sycl::access::mode AcMd, bool MapAllocator, typename T>
-  inline cl::sycl::accessor<T, 1, AcMd, cl::sycl::access::target::global_buffer>
-  get_sycl_accessor(size_t num_bytes, cl::sycl::handler &cgh,
-                    const T *ptr) const {
-    auto it = buffer_map.find(ptr);
-    if (it == buffer_map.end()) {
-      BufferT<T, MapAllocator>::add_sycl_buffer(ptr, num_bytes, buffer_map);
-    }
-    return (
-        ((typename BufferT<T, MapAllocator>::Type *)(buffer_map.at(ptr).get()))
-            ->template get_access<AcMd>(cgh));
+  /// the buffer in the buffer_map. If found it gets the accessor from it, if not,
+  ///the function then adds an entry by creating a sycl buffer for that particular pointer.
+  template <cl::sycl::access::mode AcMd, typename T> inline cl::sycl::accessor<T, 1, AcMd, cl::sycl::access::target::global_buffer>
+  get_sycl_accessor(size_t num_bytes, cl::sycl::handler &cgh, const T * ptr) const {
+    return (get_sycl_buffer<T>(num_bytes, ptr)->template get_access<AcMd, cl::sycl::access::target::global_buffer>(cgh));
+  }
+
+  template<typename T> inline  std::pair<std::map<const void *, std::shared_ptr<void>>::iterator,bool> add_sycl_buffer(const T *ptr, size_t num_bytes) const {
+    using Type = cl::sycl::buffer<T, 1>;
+    std::pair<std::map<const void *, std::shared_ptr<void>>::iterator,bool> ret = buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(ptr, std::shared_ptr<void>(new Type(cl::sycl::range<1>(num_bytes)),
+      [](void *dataMem) { delete static_cast<Type*>(dataMem); })));
+    (static_cast<Type*>(buffer_map.at(ptr).get()))->set_final_data(nullptr);
+    return ret;
+  }
+
+  template <typename T> inline cl::sycl::buffer<T, 1>* get_sycl_buffer(size_t num_bytes,const T * ptr) const {
+    return static_cast<cl::sycl::buffer<T, 1>*>(add_sycl_buffer(ptr, num_bytes).first->second.get());
   }
 
   /// allocating memory on the cpu
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void *allocate(size_t num_bytes) const {
-    return internal::aligned_malloc(num_bytes);
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void *allocate(size_t) const {
+    return internal::aligned_malloc(8);
   }
 
   // some runtime conditions that can be applied here
   bool isDeviceSuitable() const { return true; }
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void *buffer) const {
-    internal::aligned_free(buffer);
-  }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src,
-                                                    size_t n) const {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src, size_t n) const {
     ::memcpy(dst, src, n);
   }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(
-      void *dst, const void *src, size_t n) const {
-    memcpy(dst, src, n);
+
+  template<typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(T *dst, const T *src, size_t n) const {
+    auto host_acc= (static_cast<cl::sycl::buffer<T, 1>*>(add_sycl_buffer(dst, n).first->second.get()))-> template get_access<cl::sycl::access::mode::discard_write, cl::sycl::access::target::host_buffer>();
+    memcpy(host_acc.get_pointer(), src, n);
   }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(
-      void *dst, const void *src, size_t n) const {
-    memcpy(dst, src, n);
+ /// whith the current implementation of sycl, the data is copied twice from device to host. This will be fixed soon.
+  template<typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(T *dst, const T *src, size_t n) const {
+    auto it = buffer_map.find(src);
+    if (it != buffer_map.end()) {
+      auto host_acc= (static_cast<cl::sycl::buffer<T, 1>*>(it->second.get()))-> template get_access<cl::sycl::access::mode::read, cl::sycl::access::target::host_buffer>();
+      memcpy(dst,host_acc.get_pointer(),  n);
+    } else{
+      eigen_assert("no device memory found. The memory might be destroyed before creation");
+    }
   }
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void *buffer, int c,
-                                                    size_t n) const {
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void *buffer, int c, size_t n) const {
     ::memset(buffer, c, n);
   }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
+  return 1;
+  }
 };
+
 }  // end namespace Eigen
 
 #endif  // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_SYCL_H
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h b/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
index 68d14a7..0698713 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorEvalTo.h
@@ -47,13 +47,13 @@
 template<typename XprType, template <class> class MakePointer_>
 struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense>
 {
-  typedef const TensorEvalToOp<XprType>& type;
+  typedef const TensorEvalToOp<XprType, MakePointer_>& type;
 };
 
 template<typename XprType, template <class> class MakePointer_>
 struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_> >::type>
 {
-  typedef TensorEvalToOp<XprType> type;
+  typedef TensorEvalToOp<XprType, MakePointer_> type;
 };
 
 }  // end namespace internal
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
index 6497b18..52b803d 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
@@ -33,7 +33,7 @@
 template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp;
 template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> class TensorCwiseTernaryOp;
 template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
-template<typename Op, typename Dims, typename XprType> class TensorReductionOp;
+template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_ = MakePointer > class TensorReductionOp;
 template<typename XprType> class TensorIndexTupleOp;
 template<typename ReduceOp, typename Dims, typename XprType> class TensorTupleReducerOp;
 template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp;
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
index d34ff98..41d0d00 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
@@ -2,6 +2,7 @@
 // for linear algebra.
 //
 // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2016 Mehdi Goli, Codeplay Software Ltd <eigen@codeplay.com>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
@@ -20,8 +21,8 @@
   */
 
 namespace internal {
-template<typename Op, typename Dims, typename XprType>
-struct traits<TensorReductionOp<Op, Dims, XprType> >
+  template<typename Op, typename Dims, typename XprType,template <class> class MakePointer_ >
+  struct traits<TensorReductionOp<Op, Dims, XprType, MakePointer_> >
  : traits<XprType>
 {
   typedef traits<XprType> XprTraits;
@@ -31,18 +32,24 @@
   typedef typename XprType::Nested Nested;
   static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
   static const int Layout = XprTraits::Layout;
+
+  template <class T> struct MakePointer {
+    // Intermediate typedef to workaround MSVC issue.
+    typedef MakePointer_<T> MakePointerT;
+    typedef typename MakePointerT::Type Type;
+  };
 };
 
-template<typename Op, typename Dims, typename XprType>
-struct eval<TensorReductionOp<Op, Dims, XprType>, Eigen::Dense>
+template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
+struct eval<TensorReductionOp<Op, Dims, XprType, MakePointer_>, Eigen::Dense>
 {
-  typedef const TensorReductionOp<Op, Dims, XprType>& type;
+  typedef const TensorReductionOp<Op, Dims, XprType, MakePointer_>& type;
 };
 
-template<typename Op, typename Dims, typename XprType>
-struct nested<TensorReductionOp<Op, Dims, XprType>, 1, typename eval<TensorReductionOp<Op, Dims, XprType> >::type>
+template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
+struct nested<TensorReductionOp<Op, Dims, XprType, MakePointer_>, 1, typename eval<TensorReductionOp<Op, Dims, XprType, MakePointer_> >::type>
 {
-  typedef TensorReductionOp<Op, Dims, XprType> type;
+  typedef TensorReductionOp<Op, Dims, XprType, MakePointer_> type;
 };
 
 
@@ -339,8 +346,8 @@
 }  // end namespace internal
 
 
-template <typename Op, typename Dims, typename XprType>
-class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType>, ReadOnlyAccessors> {
+template <typename Op, typename Dims, typename XprType,  template <class> class MakePointer_>
+class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType, MakePointer_>, ReadOnlyAccessors> {
   public:
     typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar;
     typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
@@ -371,18 +378,19 @@
 
 
 // Eval as rvalue
-template<typename Op, typename Dims, typename ArgType, typename Device>
-struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
+template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
+struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>
 {
-  typedef TensorReductionOp<Op, Dims, ArgType> XprType;
+  typedef TensorReductionOp<Op, Dims, ArgType, MakePointer_> XprType;
   typedef typename XprType::Index Index;
+  typedef ArgType ChildType;
   typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
   static const int NumInputDims = internal::array_size<InputDimensions>::value;
   static const int NumReducedDims = internal::array_size<Dims>::value;
   static const int NumOutputDims = NumInputDims - NumReducedDims;
   typedef typename internal::conditional<NumOutputDims==0, Sizes<>, DSizes<Index, NumOutputDims> >::type Dimensions;
   typedef typename XprType::Scalar Scalar;
-  typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device> Self;
+  typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self;
   static const bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess;
   typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
   typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
@@ -401,7 +409,7 @@
   static const bool RunningFullReduction = (NumOutputDims==0);
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device)
+      : m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device), m_xpr_dims(op.dims())
   {
     EIGEN_STATIC_ASSERT((NumInputDims >= NumReducedDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
     EIGEN_STATIC_ASSERT((!ReducingInnerMostDims | !PreservingInnerMostDims | (NumReducedDims == NumInputDims)),
@@ -471,25 +479,35 @@
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
 
-  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool evalSubExprsIfNeeded(CoeffReturnType* data) {
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool evalSubExprsIfNeeded(typename MakePointer_<CoeffReturnType>::Type data) {
     m_impl.evalSubExprsIfNeeded(NULL);
 
     // Use the FullReducer if possible.
-    if (RunningFullReduction &&
+    if ((RunningFullReduction && RunningOnSycl) ||(RunningFullReduction &&
         internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation &&
         ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) ||
-         !RunningOnGPU)) {
+         !RunningOnGPU))) {
       bool need_assign = false;
       if (!data) {
         m_result = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType)));
         data = m_result;
         need_assign = true;
       }
-
       Op reducer(m_reducer);
       internal::FullReducer<Self, Op, Device>::run(*this, reducer, m_device, data);
       return need_assign;
     }
+    else if(RunningOnSycl){
+      const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
+      const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
+      if (!data) {
+        data = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType) * num_coeffs_to_preserve));
+        m_result = data;
+      }
+      Op reducer(m_reducer);
+      internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve);
+      return (m_result != NULL);
+    }
 
     // Attempt to use an optimized reduction.
     else if (RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) {
@@ -572,7 +590,7 @@
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
   {
-    if ((RunningFullReduction || RunningOnGPU) && m_result) {
+    if ((RunningOnSycl || RunningFullReduction || RunningOnGPU) && m_result) {
       return *(m_result + index);
     }
     Op reducer(m_reducer);
@@ -644,7 +662,14 @@
     }
   }
 
-  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
+  EIGEN_DEVICE_FUNC typename MakePointer_<Scalar>::Type data() const { return m_result; }
+  /// required by sycl in order to extract the accessor
+  const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+  /// added for sycl in order to construct the buffer from the sycl device
+  const Device& device() const{return m_device;}
+  /// added for sycl in order to re-construct the reduction eval on the device for the sub-kernel
+  const Dims& xprDims() const {return m_xpr_dims;}
+
 
   private:
   template <int, typename, typename> friend struct internal::GenericDimReducer;
@@ -737,12 +762,18 @@
   // For full reductions
 #if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
   static const bool RunningOnGPU = internal::is_same<Device, Eigen::GpuDevice>::value;
+  static const bool RunningOnSycl = false;
+#elif defined(EIGEN_USE_SYCL)
+static const bool RunningOnSycl = internal::is_same<typename internal::remove_all<Device>::type, Eigen::SyclDevice>::value;
+static const bool RunningOnGPU = false;
 #else
   static const bool RunningOnGPU = false;
+  static const bool RunningOnSycl = false;
 #endif
-  CoeffReturnType* m_result;
+  typename MakePointer_<CoeffReturnType>::Type m_result;
 
   const Device& m_device;
+  const Dims& m_xpr_dims;
 };
 
 } // end namespace Eigen
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
new file mode 100644
index 0000000..3daecb0
--- /dev/null
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
@@ -0,0 +1,242 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TensorSyclPlaceHolderExpr.h
+ *
+ * \brief:
+ *  This is the specialisation of the placeholder expression based on the
+ * operation type
+ *
+*****************************************************************/
+
+#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
+#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
+
+namespace Eigen {
+namespace internal {
+
+template<typename CoeffReturnType, typename KernelName> struct syclGenericBufferReducer{
+template<typename BufferTOut, typename BufferTIn>
+static void run(BufferTOut* bufOut, BufferTIn& bufI, const Eigen::SyclDevice& dev, size_t length, size_t local){
+  do {
+          auto f = [length, local, bufOut, &bufI](cl::sycl::handler& h) mutable {
+            cl::sycl::nd_range<1> r{cl::sycl::range<1>{std::max(length, local)},
+                                    cl::sycl::range<1>{std::min(length, local)}};
+            /* Two accessors are used: one to the buffer that is being reduced,
+             * and a second to local memory, used to store intermediate data. */
+            auto aI =
+                bufI.template get_access<cl::sycl::access::mode::read_write>(h);
+            auto aOut =
+                bufOut->template get_access<cl::sycl::access::mode::discard_write>(h);
+            cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write,
+                               cl::sycl::access::target::local>
+                scratch(cl::sycl::range<1>(local), h);
+
+            /* The parallel_for invocation chosen is the variant with an nd_item
+             * parameter, since the code requires barriers for correctness. */
+            h.parallel_for<KernelName>(
+                r, [aOut, aI, scratch, local, length](cl::sycl::nd_item<1> id) {
+                  size_t globalid = id.get_global(0);
+                  size_t localid = id.get_local(0);
+                  /* All threads collectively read from global memory into local.
+                   * The barrier ensures all threads' IO is resolved before
+                   * execution continues (strictly speaking, all threads within
+                   * a single work-group - there is no co-ordination between
+                   * work-groups, only work-items). */
+                  if (globalid < length) {
+                    scratch[localid] = aI[globalid];
+                  }
+                  id.barrier(cl::sycl::access::fence_space::local_space);
+
+                  /* Apply the reduction operation between the current local
+                   * id and the one on the other half of the vector. */
+                  if (globalid < length) {
+                    int min = (length < local) ? length : local;
+                    for (size_t offset = min / 2; offset > 0; offset /= 2) {
+                      if (localid < offset) {
+                        scratch[localid] += scratch[localid + offset];
+                      }
+                      id.barrier(cl::sycl::access::fence_space::local_space);
+                    }
+                    /* The final result will be stored in local id 0. */
+                    if (localid == 0) {
+                      aI[id.get_group(0)] = scratch[localid];
+                      if((length<=local) && globalid ==0){
+                        aOut[globalid]=scratch[localid];
+                      }
+                    }
+                  }
+                });
+          };
+            dev.m_queue.submit(f);
+            dev.m_queue.throw_asynchronous();
+
+          /* At this point, you could queue::wait_and_throw() to ensure that
+           * errors are caught quickly. However, this would likely impact
+           * performance negatively. */
+          length = length / local;
+
+        } while (length > 1);
+
+
+
+}
+
+};
+
+/// For now let's start with a full reducer
+/// Self is useless here because in expression construction we are going to treat reduction as a leafnode.
+/// we want to take reduction child and then build a construction and apply the full reducer function on it. Fullreducre applies the
+/// reduction operation on the child of the reduction. once it is done the reduction is an empty shell and can be thrown away and treated as
+// a leafNode.
+template <typename Self, typename Op, bool Vectorizable>
+struct FullReducer<Self, Op, const Eigen::SyclDevice, Vectorizable> {
+
+  typedef typename Self::CoeffReturnType CoeffReturnType;
+  static const bool HasOptimizedImplementation = false;
+
+  static void run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output) {
+    typedef const typename Self::ChildType HostExpr; /// this is the child of reduction
+    typedef  typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
+    auto functors = TensorSycl::internal::extractFunctors(self.impl());
+    int red_factor =256; /// initial reduction. If the size is less than red_factor we only creates one thread.
+    size_t inputSize =self.impl().dimensions().TotalSize();
+    size_t rng = inputSize/red_factor; // the total number of thread initially is half the size of the input
+    size_t remaining = inputSize% red_factor;
+    if(rng ==0) {
+      red_factor=1;
+    };
+    size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2;
+    size_t GRange=std::max((size_t )1, rng);
+
+    // convert global range to power of 2 for redecution
+    GRange--;
+    GRange |= GRange >> 1;
+    GRange |= GRange >> 2;
+    GRange |= GRange >> 4;
+    GRange |= GRange >> 8;
+    GRange |= GRange >> 16;
+#if __x86_64__ || __ppc64__ || _WIN64
+    GRange |= GRange >> 32;
+#endif
+    GRange++;
+    size_t  outTileSize = tileSize;
+    /// if the shared memory is less than the GRange, we set shared_mem size to the TotalSize and in this case one kernel would be created for recursion to reduce all to one.
+    if (GRange < outTileSize) outTileSize=GRange;
+    // getting final out buffer at the moment the created buffer is true because there is no need for assign
+    auto out_buffer =dev.template get_sycl_buffer<typename Eigen::internal::remove_all<CoeffReturnType>::type>(self.dimensions().TotalSize(), output);
+    /// creating the shared memory for calculating reduction.
+    /// This one is used to collect all the reduced value of shared memory as we dont have global barrier on GPU. Once it is saved we can
+    /// recursively apply reduction on it in order to reduce the whole.
+    auto temp_global_buffer =cl::sycl::buffer<CoeffReturnType, 1>(cl::sycl::range<1>(GRange));
+    typedef typename Eigen::internal::remove_all<decltype(self.xprDims())>::type Dims;
+    Dims dims= self.xprDims();
+    Op functor = reducer;
+    dev.m_queue.submit([&](cl::sycl::handler &cgh) {
+      // create a tuple of accessors from Evaluator
+      auto tuple_of_accessors =  TensorSycl::internal::createTupleOfAccessors(cgh, self.impl());
+      auto tmp_global_accessor = temp_global_buffer. template get_access<cl::sycl::access::mode::read_write, cl::sycl::access::target::global_buffer>(cgh);
+
+      cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(outTileSize)), [=](cl::sycl::nd_item<1> itemID) {
+        typedef typename TensorSycl::internal::ConvertToDeviceExpression<const HostExpr>::Type DevExpr;
+        auto device_expr = TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
+        /// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour
+        /// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the
+        /// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here.
+        const auto device_self_expr= TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, functor);
+        /// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is
+        /// the device_evaluator is detectable and recognisable on the device.
+        auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice());
+        /// const cast added as a naive solution to solve the qualifier drop error
+        auto globalid=itemID.get_global_linear_id();
+
+        if(globalid<rng)
+          tmp_global_accessor.get_pointer()[globalid]=InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, red_factor*globalid, red_factor, const_cast<Op&>(functor));
+        else
+          tmp_global_accessor.get_pointer()[globalid]=static_cast<CoeffReturnType>(0);
+
+        if(remaining!=0 && globalid==0 )
+          // this will add the rest of input buffer when the input size is not devidable to red_factor.
+          tmp_global_accessor.get_pointer()[globalid]+=InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, red_factor*(rng), remaining, const_cast<Op&>(functor));
+      });
+    });
+  dev.m_queue.throw_asynchronous();
+
+/// This is used to recursively reduce the tmp value to an element of 1;
+  syclGenericBufferReducer<CoeffReturnType,HostExpr>::run(out_buffer, temp_global_buffer,dev, GRange,  outTileSize);
+  }
+
+};
+
+template <typename Self, typename Op>
+struct InnerReducer<Self, Op, const Eigen::SyclDevice> {
+
+  typedef typename Self::CoeffReturnType CoeffReturnType;
+  static const bool HasOptimizedImplementation = false;
+
+  static bool run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output, typename Self::Index , typename Self::Index num_coeffs_to_preserve) {
+    typedef const typename Self::ChildType HostExpr; /// this is the child of reduction
+    typedef  typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
+    auto functors = TensorSycl::internal::extractFunctors(self.impl());
+
+    size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2;
+
+    size_t GRange=num_coeffs_to_preserve;
+    if (tileSize>GRange) tileSize=GRange;
+    else if(GRange>tileSize){
+      size_t xMode = GRange % tileSize;
+      if (xMode != 0) GRange += (tileSize - xMode);
+    }
+    // getting final out buffer at the moment the created buffer is true because there is no need for assign
+    /// creating the shared memory for calculating reduction.
+    /// This one is used to collect all the reduced value of shared memory as we dont have global barrier on GPU. Once it is saved we can
+    /// recursively apply reduction on it in order to reduce the whole.
+    typedef typename Eigen::internal::remove_all<decltype(self.xprDims())>::type Dims;
+    Dims dims= self.xprDims();
+    Op functor = reducer;
+
+    dev.m_queue.submit([&](cl::sycl::handler &cgh) {
+      // create a tuple of accessors from Evaluator
+      auto tuple_of_accessors =  TensorSycl::internal::createTupleOfAccessors(cgh, self.impl());
+      auto output_accessor = dev.template get_sycl_accessor<cl::sycl::access::mode::discard_write>(num_coeffs_to_preserve,cgh, output);
+
+      cgh.parallel_for<Self>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), [=](cl::sycl::nd_item<1> itemID) {
+        typedef typename TensorSycl::internal::ConvertToDeviceExpression<const HostExpr>::Type DevExpr;
+        auto device_expr = TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
+        /// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour
+        /// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the
+        /// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here.
+        const auto device_self_expr= TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, functor);
+        /// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is
+        /// the device_evaluator is detectable and recognisable on the device.
+        typedef Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice> DeiceSelf;
+        auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice());
+        /// const cast added as a naive solution to solve the qualifier drop error
+        auto globalid=itemID.get_global_linear_id();
+        if (globalid< static_cast<size_t>(num_coeffs_to_preserve)) {
+          typename DeiceSelf::CoeffReturnType accum = functor.initialize();
+          GenericDimReducer<DeiceSelf::NumReducedDims-1, DeiceSelf, Op>::reduce(device_self_evaluator, device_self_evaluator.firstInput(globalid),const_cast<Op&>(functor), &accum);
+          functor.finalize(accum);
+          output_accessor.get_pointer()[globalid]= accum;
+        }
+      });
+    });
+  dev.m_queue.throw_asynchronous();
+    return false;
+  }
+};
+
+}  // end namespace internal
+}  // namespace Eigen
+
+#endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h
index da15f79..bb8800d 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h
@@ -22,6 +22,13 @@
   typedef typename cl::sycl::global_ptr<T>::pointer_t Type;
 };
 
+// global pointer to set different attribute state for a class
+template <class T>
+struct MakeLocalPointer {
+  typedef typename cl::sycl::local_ptr<T>::pointer_t Type;
+};
+
+
 namespace Eigen {
 namespace TensorSycl {
 namespace internal {
@@ -43,9 +50,7 @@
 // tuple construction
 #include "TensorSyclTuple.h"
 
-// This file contains the PlaceHolder that replaces the actual data
-#include "TensorSyclPlaceHolder.h"
-
+// counting number of leaf at compile time
 #include "TensorSyclLeafCount.h"
 
 // The index PlaceHolder takes the actual expression and replaces the actual
@@ -57,9 +62,6 @@
 // creation of an accessor tuple from a tuple of SYCL buffers
 #include "TensorSyclExtractAccessor.h"
 
-// actual data extraction using accessors
-//#include "GetDeviceData.h"
-
 // this is used to change the address space type in tensor map for GPU
 #include "TensorSyclConvertToDeviceExpression.h"
 
@@ -70,6 +72,9 @@
 // this is used to construct the expression on the device
 #include "TensorSyclExprConstructor.h"
 
+/// this is used for extracting tensor reduction
+#include "TensorReductionSycl.h"
+
 // kernel execution using fusion
 #include "TensorSyclRun.h"
 
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclConvertToDeviceExpression.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclConvertToDeviceExpression.h
index a94c304..8729c86 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclConvertToDeviceExpression.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclConvertToDeviceExpression.h
@@ -102,6 +102,18 @@
 KERNELBROKERCONVERT(const, true, TensorEvalToOp)
 KERNELBROKERCONVERT(, false, TensorEvalToOp)
 #undef KERNELBROKERCONVERT
+
+/// specialisation of the \ref ConvertToDeviceExpression struct when the node type is TensorReductionOp
+#define KERNELBROKERCONVERTREDUCTION(CVQual)\
+template <typename OP, typename Dim, typename subExpr, template <class> class MakePointer_>\
+struct ConvertToDeviceExpression<CVQual TensorReductionOp<OP, Dim, subExpr, MakePointer_> > {\
+  typedef CVQual TensorReductionOp<OP, Dim, typename ConvertToDeviceExpression<subExpr>::Type, MakeGlobalPointer> Type;\
+};
+
+KERNELBROKERCONVERTREDUCTION(const)
+KERNELBROKERCONVERTREDUCTION()
+#undef KERNELBROKERCONVERTREDUCTION
+
 }  // namespace internal
 }  // namespace TensorSycl
 }  // namespace Eigen
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h
index 833d5e2..7ed3a3a 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h
@@ -33,8 +33,7 @@
   EvalToLHSConstructor(const utility::tuple::Tuple<Params...> &t): expr((&(*(utility::tuple::get<N>(t).get_pointer())))) {}
 };
 
-/// \struct ExprConstructor is used to reconstruct the expression on the device
-/// and
+/// \struct ExprConstructor is used to reconstruct the expression on the device and
 /// recreate the expression with MakeGlobalPointer containing the device address
 /// space for the TensorMap pointers used in eval function.
 /// It receives the original expression type, the functor of the node, the tuple
@@ -49,7 +48,7 @@
 template <typename Scalar_, int Options_, int Options2_, int Options3_, int NumIndices_, typename IndexType_,\
 template <class> class MakePointer_, size_t N, typename... Params>\
 struct ExprConstructor< CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer>,\
-CVQual Eigen::internal::PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options3_, MakePointer_>, N>, Params...>{\
+CVQual PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options3_, MakePointer_>, N>, Params...>{\
   typedef  CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer>  Type;\
   Type expr;\
   template <typename FuncDetector>\
@@ -187,7 +186,7 @@
 #define FORCEDEVAL(CVQual)\
 template <typename OrigExpr, typename DevExpr, size_t N, typename... Params>\
 struct ExprConstructor<CVQual TensorForcedEvalOp<OrigExpr, MakeGlobalPointer>,\
-CVQual Eigen::internal::PlaceHolder<CVQual TensorForcedEvalOp<DevExpr>, N>, Params...> {\
+CVQual PlaceHolder<CVQual TensorForcedEvalOp<DevExpr>, N>, Params...> {\
   typedef CVQual TensorMap<Tensor<typename TensorForcedEvalOp<DevExpr, MakeGlobalPointer>::Scalar,\
   TensorForcedEvalOp<DevExpr, MakeGlobalPointer>::NumDimensions, 0, typename TensorForcedEvalOp<DevExpr>::Index>, 0, MakeGlobalPointer> Type;\
   Type expr;\
@@ -200,14 +199,41 @@
 FORCEDEVAL()
 #undef FORCEDEVAL
 
+template <bool Conds,  size_t X , size_t Y > struct ValueCondition {
+  static const size_t Res =X;
+};
+template<size_t X, size_t Y> struct ValueCondition<false, X , Y> {
+  static const size_t Res =Y;
+};
+
+/// specialisation of the \ref ExprConstructor struct when the node type is TensorReductionOp
+#define SYCLREDUCTIONEXPR(CVQual)\
+template <typename OP, typename Dim, typename OrigExpr, typename DevExpr, size_t N, typename... Params>\
+struct ExprConstructor<CVQual TensorReductionOp<OP, Dim, OrigExpr, MakeGlobalPointer>,\
+CVQual PlaceHolder<CVQual TensorReductionOp<OP, Dim, DevExpr>, N>, Params...> {\
+  static const size_t NumIndices= ValueCondition< TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::NumDimensions==0,  1, TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::NumDimensions >::Res;\
+  typedef CVQual TensorMap<Tensor<typename TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::Scalar,\
+  NumIndices, 0, typename TensorReductionOp<OP, Dim, DevExpr>::Index>, 0, MakeGlobalPointer> Type;\
+  Type expr;\
+  template <typename FuncDetector>\
+  ExprConstructor(FuncDetector &fd, const utility::tuple::Tuple<Params...> &t)\
+  : expr(Type((&(*(utility::tuple::get<N>(t).get_pointer()))), fd.dimensions())) {}\
+};
+
+SYCLREDUCTIONEXPR(const)
+SYCLREDUCTIONEXPR()
+#undef SYCLREDUCTIONEXPR
+
 /// template deduction for \ref ExprConstructor struct
 template <typename OrigExpr, typename IndexExpr, typename FuncD, typename... Params>
 auto createDeviceExpression(FuncD &funcD, const utility::tuple::Tuple<Params...> &t)
     -> decltype(ExprConstructor<OrigExpr, IndexExpr, Params...>(funcD, t)) {
   return ExprConstructor<OrigExpr, IndexExpr, Params...>(funcD, t);
 }
-}
-}
-}  // namespace Eigen
+
+} /// namespace TensorSycl
+} /// namespace internal
+} /// namespace Eigen
+
 
 #endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXPR_CONSTRUCTOR_HPP
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h
index ceec528..b1da685 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h
@@ -57,9 +57,9 @@
     return utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1),utility::tuple::append(ExtractAccessor<Arg2>::getTuple(cgh, eval2), ExtractAccessor<Arg3>::getTuple(cgh, eval3)));
   }
   template< cl::sycl::access::mode AcM, typename Arg> static inline auto getAccessor(cl::sycl::handler& cgh, Arg eval)
-  -> decltype(utility::tuple::make_tuple( eval.device().template get_sycl_accessor<AcM, true,
+  -> decltype(utility::tuple::make_tuple( eval.device().template get_sycl_accessor<AcM,
   typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()))){
-    return utility::tuple::make_tuple(eval.device().template get_sycl_accessor<AcM, true, typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()));
+    return utility::tuple::make_tuple(eval.device().template get_sycl_accessor<AcM, typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()));
   }
 };
 
@@ -73,14 +73,12 @@
   }
 };
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-///  TensorCwiseNullaryOp,  TensorCwiseUnaryOp and  TensorBroadcastingOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseNullaryOp,  TensorCwiseUnaryOp and  TensorBroadcastingOp
 template <template<class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<UnaryCategory<OP, RHSExpr>, Dev> >
 : ExtractAccessor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> > {};
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// const TensorCwiseBinaryOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorCwiseBinaryOp
 template <template<class, class, class> class BinaryCategory, typename OP,  typename LHSExpr, typename RHSExpr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> > {
   static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> eval)
@@ -88,9 +86,7 @@
     return AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl());
   }
 };
-
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// TensorCwiseBinaryOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseBinaryOp
 template <template<class, class, class> class BinaryCategory, typename OP,  typename LHSExpr, typename RHSExpr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >
 : ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >{};
@@ -105,8 +101,7 @@
   }
 };
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// TensorCwiseTernaryOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseTernaryOp
 template <template<class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >
 : ExtractAccessor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >{};
@@ -127,8 +122,7 @@
 struct ExtractAccessor<TensorEvaluator<TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> >
 : ExtractAccessor<TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> >{};
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// const TensorAssignOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorAssignOp
 template <typename LHSExpr, typename RHSExpr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> > {
   static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> eval)
@@ -137,14 +131,12 @@
  }
 };
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// TensorAssignOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is TensorAssignOp
 template <typename LHSExpr, typename RHSExpr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<TensorAssignOp<LHSExpr, RHSExpr>, Dev> >
 : ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> >{};
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// const TensorMap
+/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorMap
 #define TENSORMAPEXPR(CVQual, ACCType)\
 template <typename PlainObjectType, int Options_, typename Dev>\
 struct ExtractAccessor<TensorEvaluator<CVQual TensorMap<PlainObjectType, Options_>, Dev> > {\
@@ -157,8 +149,7 @@
 TENSORMAPEXPR(, cl::sycl::access::mode::read_write)
 #undef TENSORMAPEXPR
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// const TensorForcedEvalOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorForcedEvalOp
 template <typename Expr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> > {
   static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> eval)
@@ -167,14 +158,12 @@
   }
 };
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// TensorForcedEvalOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is TensorForcedEvalOp
 template <typename Expr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<TensorForcedEvalOp<Expr>, Dev> >
 : ExtractAccessor<TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> >{};
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// const TensorEvalToOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorEvalToOp
 template <typename Expr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<const TensorEvalToOp<Expr>, Dev> > {
   static inline auto getTuple(cl::sycl::handler& cgh,const TensorEvaluator<const TensorEvalToOp<Expr>, Dev> eval)
@@ -183,19 +172,33 @@
   }
 };
 
-/// specialisation of the \ref ExtractAccessor struct when the node type is
-/// TensorEvalToOp
+/// specialisation of the \ref ExtractAccessor struct when the node type is TensorEvalToOp
 template <typename Expr, typename Dev>
 struct ExtractAccessor<TensorEvaluator<TensorEvalToOp<Expr>, Dev> >
 : ExtractAccessor<TensorEvaluator<const TensorEvalToOp<Expr>, Dev> >{};
 
+/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorReductionOp
+template <typename OP, typename Dim, typename Expr, typename Dev>
+struct ExtractAccessor<TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> > {
+  static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> eval)
+  -> decltype(AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval)){
+    return AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval);
+  }
+};
+
+/// specialisation of the \ref ExtractAccessor struct when the node type is TensorReductionOp
+template <typename OP, typename Dim, typename Expr, typename Dev>
+struct ExtractAccessor<TensorEvaluator<TensorReductionOp<OP, Dim, Expr>, Dev> >
+: ExtractAccessor<TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> >{};
+
 /// template deduction for \ref ExtractAccessor
 template <typename Evaluator>
 auto createTupleOfAccessors(cl::sycl::handler& cgh, const Evaluator& expr)
 -> decltype(ExtractAccessor<Evaluator>::getTuple(cgh, expr)) {
   return ExtractAccessor<Evaluator>::getTuple(cgh, expr);
 }
-}
-}
-}
+
+} /// namespace TensorSycl
+} /// namespace internal
+} /// namespace Eigen
 #endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_ACCESSOR_HPP
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h
index 801b4f5..4271253 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h
@@ -141,7 +141,30 @@
 struct FunctorExtractor<TensorEvaluator<TensorEvalToOp<RHSExpr>, Dev> >
 : FunctorExtractor<TensorEvaluator<const TensorEvalToOp<RHSExpr>, Dev> > {};
 
+template<typename Dim, size_t NumOutputDim> struct DimConstr {
+template<typename InDim>
+  static inline Dim getDim(InDim dims ) {return dims;}
+};
 
+template<typename Dim> struct DimConstr<Dim, 0> {
+  template<typename InDim>
+    static inline Dim getDim(InDim dims ) {return Dim(dims.TotalSize());}
+};
+
+template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
+struct FunctorExtractor<TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>>{
+  typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Evaluator;
+  typedef typename Eigen::internal::conditional<Evaluator::NumOutputDims==0, DSizes<typename Evaluator::Index, 1>, typename Evaluator::Dimensions >::type Dimensions;
+  const Dimensions m_dimensions;
+  const Dimensions& dimensions() const { return m_dimensions; }
+  FunctorExtractor(const TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>& expr)
+  : m_dimensions(DimConstr<Dimensions, Evaluator::NumOutputDims>::getDim(expr.dimensions())) {}
+};
+
+
+template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
+struct FunctorExtractor<TensorEvaluator<TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>>
+: FunctorExtractor<TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>>{};
 /// template deduction function for FunctorExtractor
 template <typename Evaluator>
 auto inline extractFunctors(const Evaluator& evaluator)-> FunctorExtractor<Evaluator> {
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclLeafCount.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclLeafCount.h
index 8d520d2..25d1fac 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclLeafCount.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclLeafCount.h
@@ -43,8 +43,7 @@
   static const size_t Count = LeafCount<Arg>::Count + CategoryCount<Args...>::Count;
 };
 
-/// specialisation of the \ref LeafCount struct when the node type is const
-/// TensorMap
+/// specialisation of the \ref LeafCount struct when the node type is const TensorMap
 template <typename PlainObjectType, int Options_, template <class> class MakePointer_>
 struct LeafCount<const TensorMap<PlainObjectType, Options_, MakePointer_> > {
   static const size_t Count =1;
@@ -61,18 +60,15 @@
 template <template <class, class...> class CategoryExpr, typename OP, typename... RHSExpr>
 struct LeafCount<CategoryExpr<OP, RHSExpr...> > :LeafCount<const CategoryExpr<OP, RHSExpr...> >{};
 
-/// specialisation of the \ref LeafCount struct when the node type is
-/// const TensorSelectOp is an exception
+/// specialisation of the \ref LeafCount struct when the node type is const TensorSelectOp is an exception
 template <typename IfExpr, typename ThenExpr, typename ElseExpr>
 struct LeafCount<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr> > : CategoryCount<IfExpr, ThenExpr, ElseExpr> {};
-/// specialisation of the \ref LeafCount struct when the node type is
-/// TensorSelectOp
+/// specialisation of the \ref LeafCount struct when the node type is TensorSelectOp
 template <typename IfExpr, typename ThenExpr, typename ElseExpr>
 struct LeafCount<TensorSelectOp<IfExpr, ThenExpr, ElseExpr> >: LeafCount<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr> > {};
 
 
-/// specialisation of the \ref LeafCount struct when the node type is const
-/// TensorAssignOp
+/// specialisation of the \ref LeafCount struct when the node type is const TensorAssignOp
 template <typename LHSExpr, typename RHSExpr>
 struct LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >: CategoryCount<LHSExpr,RHSExpr> {};
 
@@ -81,31 +77,38 @@
 template <typename LHSExpr, typename RHSExpr>
 struct LeafCount<TensorAssignOp<LHSExpr, RHSExpr> > :LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >{};
 
-/// specialisation of the \ref LeafCount struct when the node type is const
-/// TensorForcedEvalOp
+/// specialisation of the \ref LeafCount struct when the node type is const TensorForcedEvalOp
 template <typename Expr>
 struct LeafCount<const TensorForcedEvalOp<Expr> > {
     static const size_t Count =1;
 };
 
-/// specialisation of the \ref LeafCount struct when the node type is
-/// TensorForcedEvalOp
+/// specialisation of the \ref LeafCount struct when the node type is TensorForcedEvalOp
 template <typename Expr>
 struct LeafCount<TensorForcedEvalOp<Expr> >: LeafCount<const TensorForcedEvalOp<Expr> > {};
 
-/// specialisation of the \ref LeafCount struct when the node type is const
-/// TensorEvalToOp
+/// specialisation of the \ref LeafCount struct when the node type is const TensorEvalToOp
 template <typename Expr>
 struct LeafCount<const TensorEvalToOp<Expr> > {
   static const size_t Count = 1 + CategoryCount<Expr>::Count;
 };
 
-/// specialisation of the \ref LeafCount struct when the node type is
-/// TensorEvalToOp
+/// specialisation of the \ref LeafCount struct when the node type is const TensorReductionOp
+template <typename OP, typename Dim, typename Expr>
+struct LeafCount<const TensorReductionOp<OP, Dim, Expr> > {
+    static const size_t Count =1;
+};
+
+/// specialisation of the \ref LeafCount struct when the node type is TensorReductionOp
+template <typename OP, typename Dim, typename Expr>
+struct LeafCount<TensorReductionOp<OP, Dim, Expr> >: LeafCount<const TensorReductionOp<OP, Dim, Expr> >{};
+
+/// specialisation of the \ref LeafCount struct when the node type is TensorEvalToOp
 template <typename Expr>
 struct LeafCount<TensorEvalToOp<Expr> >: LeafCount<const TensorEvalToOp<Expr> >{};
-}
-}
-}  // namespace Eigen
+
+} /// namespace TensorSycl
+} /// namespace internal
+} /// namespace Eigen
 
 #endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_LEAF_COUNT_HPP
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolder.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolder.h
deleted file mode 100644
index 43a63c7..0000000
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolder.h
+++ /dev/null
@@ -1,99 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Mehdi Goli    Codeplay Software Ltd.
-// Ralph Potter  Codeplay Software Ltd.
-// Luke Iwanski  Codeplay Software Ltd.
-// Contact: <eigen@codeplay.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-/*****************************************************************
- * TensorSyclPlaceHolder.h
- *
- * \brief:
- *  The PlaceHolder expression are nothing but a container preserving
- *  the order of actual data in the tuple of sycl buffer.
- *
-*****************************************************************/
-
-#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP
-#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP
-
-namespace Eigen {
-namespace internal {
-/// \struct PlaceHolder
-/// \brief PlaceHolder is used to replace the \ref TensorMap in the expression
-/// tree.
-/// PlaceHolder contains the order of the leaf node in the expression tree.
-template <typename Scalar, size_t N>
-struct PlaceHolder {
-  static constexpr size_t I = N;
-  typedef Scalar Type;
-};
-
-/// \brief specialisation of the PlaceHolder node for const TensorMap
-#define TENSORMAPPLACEHOLDER(CVQual)\
-template <typename PlainObjectType, int Options_, template <class> class MakePointer_, size_t N>\
-struct PlaceHolder<CVQual TensorMap<PlainObjectType, Options_, MakePointer_>, N> {\
-  static const size_t I = N;\
-  typedef CVQual TensorMap<PlainObjectType, Options_, MakePointer_> Type;\
-  typedef typename Type::Self Self;\
-  typedef typename Type::Base Base;\
-  typedef typename Type::Nested Nested;\
-  typedef typename Type::StorageKind StorageKind;\
-  typedef typename Type::Index Index;\
-  typedef typename Type::Scalar Scalar;\
-  typedef typename Type::RealScalar RealScalar;\
-  typedef typename Type::CoeffReturnType CoeffReturnType;\
-};
-
-TENSORMAPPLACEHOLDER(const)
-TENSORMAPPLACEHOLDER()
-#undef TENSORMAPPLACEHOLDER
-
-/// \brief specialisation of the PlaceHolder node for TensorForcedEvalOp. The
-/// TensorForcedEvalOp acts as a leaf node for its parent node.
-#define TENSORFORCEDEVALPLACEHOLDER(CVQual)\
-template <typename Expression, size_t N>\
-struct PlaceHolder<CVQual TensorForcedEvalOp<Expression>, N> {\
-  static const size_t I = N;\
-  typedef CVQual  TensorForcedEvalOp<Expression> Type;\
-  typedef typename Type::Nested Nested;\
-  typedef typename Type::StorageKind StorageKind;\
-  typedef typename Type::Index Index;\
-  typedef typename Type::Scalar Scalar;\
-  typedef typename Type::Packet Packet;\
-  typedef typename Type::RealScalar RealScalar;\
-  typedef typename Type::CoeffReturnType CoeffReturnType;\
-  typedef typename Type::PacketReturnType PacketReturnType;\
-};
-
-TENSORFORCEDEVALPLACEHOLDER(const)
-TENSORFORCEDEVALPLACEHOLDER()
-#undef TENSORFORCEDEVALPLACEHOLDER
-
-template <typename PlainObjectType, int Options_, template <class> class Makepointer_, size_t N>
-struct traits<PlaceHolder<const TensorMap<PlainObjectType, Options_, Makepointer_>, N> >: public traits<PlainObjectType> {
-  typedef traits<PlainObjectType> BaseTraits;
-  typedef typename BaseTraits::Scalar Scalar;
-  typedef typename BaseTraits::StorageKind StorageKind;
-  typedef typename BaseTraits::Index Index;
-  static const int NumDimensions = BaseTraits::NumDimensions;
-  static const int Layout = BaseTraits::Layout;
-  enum {
-    Options = Options_,
-    Flags = BaseTraits::Flags,
-  };
-};
-
-template <typename PlainObjectType, int Options_, template <class> class Makepointer_, size_t N>
-struct traits<PlaceHolder<TensorMap<PlainObjectType, Options_, Makepointer_>, N> >
-: traits<PlaceHolder<const TensorMap<PlainObjectType, Options_, Makepointer_>, N> > {};
-
-}  // end namespace internal
-}  // end namespoace Eigen
-
-#endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolderExpr.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolderExpr.h
index f456c35..d4c250c 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolderExpr.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolderExpr.h
@@ -25,6 +25,17 @@
 namespace Eigen {
 namespace TensorSycl {
 namespace internal {
+
+/// \struct PlaceHolder
+/// \brief PlaceHolder is used to replace the \ref TensorMap in the expression
+/// tree.
+/// PlaceHolder contains the order of the leaf node in the expression tree.
+template <typename Scalar, size_t N>
+struct PlaceHolder {
+  static constexpr size_t I = N;
+  typedef Scalar Type;
+};
+
 /// \sttruct PlaceHolderExpression
 /// \brief it is used to create the PlaceHolder expression. The PlaceHolder
 /// expression is a copy of expression type in which the TensorMap of the has
@@ -113,7 +124,7 @@
 #define TENSORMAPEXPR(CVQual)\
 template <typename Scalar_, int Options_, int Options2_, int NumIndices_, typename IndexType_, template <class> class MakePointer_, size_t N>\
 struct PlaceHolderExpression< CVQual TensorMap< Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> {\
-  typedef CVQual Eigen::internal::PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> Type;\
+  typedef CVQual PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> Type;\
 };
 
 TENSORMAPEXPR(const)
@@ -125,7 +136,7 @@
 #define FORCEDEVAL(CVQual)\
 template <typename Expr, size_t N>\
 struct PlaceHolderExpression<CVQual TensorForcedEvalOp<Expr>, N> {\
-  typedef CVQual Eigen::internal::PlaceHolder<CVQual TensorForcedEvalOp<Expr>, N> Type;\
+  typedef CVQual PlaceHolder<CVQual TensorForcedEvalOp<Expr>, N> Type;\
 };
 
 FORCEDEVAL(const)
@@ -144,6 +155,18 @@
 EVALTO()
 #undef EVALTO
 
+
+/// specialisation of the \ref PlaceHolderExpression when the node is
+/// TensorReductionOp
+#define SYCLREDUCTION(CVQual)\
+template <typename OP, typename Dims, typename Expr, size_t N>\
+struct PlaceHolderExpression<CVQual TensorReductionOp<OP, Dims, Expr>, N>{\
+  typedef CVQual PlaceHolder<CVQual TensorReductionOp<OP, Dims,Expr>, N> Type;\
+};
+SYCLREDUCTION(const)
+SYCLREDUCTION()
+#undef SYCLREDUCTION
+
 /// template deduction for \ref PlaceHolderExpression struct
 template <typename Expr>
 struct createPlaceHolderExpression {
@@ -151,8 +174,8 @@
   typedef typename PlaceHolderExpression<Expr, TotalLeaves - 1>::Type Type;
 };
 
-}
-}
+}  // internal
+}  // TensorSycl
 }  // namespace Eigen
 
 #endif  // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_EXPR_HPP
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclRun.h b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclRun.h
index 57f2dda..7914b6f 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorSyclRun.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorSyclRun.h
@@ -37,20 +37,20 @@
     typedef  typename internal::createPlaceHolderExpression<Expr>::Type PlaceHolderExpr;
     auto functors = internal::extractFunctors(evaluator);
 
+    size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2;
     dev.m_queue.submit([&](cl::sycl::handler &cgh) {
 
       // create a tuple of accessors from Evaluator
       auto tuple_of_accessors = internal::createTupleOfAccessors<decltype(evaluator)>(cgh, evaluator);
       const auto range = utility::tuple::get<0>(tuple_of_accessors).get_range()[0];
-
-      size_t outTileSize = range;
-      if (range > 64) outTileSize = 64;
-      size_t yMode = range % outTileSize;
-      int yRange = static_cast<int>(range);
-      if (yMode != 0) yRange += (outTileSize - yMode);
-
+      size_t GRange=range;
+      if (tileSize>GRange) tileSize=GRange;
+      else if(GRange>tileSize){
+        size_t xMode = GRange % tileSize;
+        if (xMode != 0) GRange += (tileSize - xMode);
+      }
       // run the kernel
-      cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(yRange), cl::sycl::range<1>(outTileSize)), [=](cl::sycl::nd_item<1> itemID) {
+      cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), [=](cl::sycl::nd_item<1> itemID) {
         typedef  typename internal::ConvertToDeviceExpression<Expr>::Type DevExpr;
         auto device_expr =internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
         auto device_evaluator = Eigen::TensorEvaluator<decltype(device_expr.expr), Eigen::DefaultDevice>(device_expr.expr, Eigen::DefaultDevice());
@@ -61,6 +61,7 @@
     });
     dev.m_queue.throw_asynchronous();
   }
+
   evaluator.cleanup();
 }
 }  // namespace TensorSycl
diff --git a/unsupported/test/CMakeLists.txt b/unsupported/test/CMakeLists.txt
index aeaea16..b5fa1c8 100644
--- a/unsupported/test/CMakeLists.txt
+++ b/unsupported/test/CMakeLists.txt
@@ -1,6 +1,6 @@
 # generate split test header file only if it does not yet exist
 # in order to prevent a rebuild everytime cmake is configured
-if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h)  
+if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h)
   file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h "")
   foreach(i RANGE 1 999)
     file(APPEND ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h
@@ -16,11 +16,11 @@
 set_property(GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT "Unsupported")
 add_custom_target(BuildUnsupported)
 
-include_directories(../../test ../../unsupported ../../Eigen 
+include_directories(../../test ../../unsupported ../../Eigen
                     ${CMAKE_CURRENT_BINARY_DIR}/../../test)
 
 find_package (Threads)
-                    
+
 find_package(GoogleHash)
 if(GOOGLEHASH_FOUND)
   add_definitions("-DEIGEN_GOOGLEHASH_SUPPORT")
@@ -134,7 +134,7 @@
 ei_add_test(cxx11_tensor_layout_swap)
 ei_add_test(cxx11_tensor_io)
 if("${CMAKE_SIZEOF_VOID_P}" EQUAL "8")
-  # This test requires __uint128_t which is only available on 64bit systems 
+  # This test requires __uint128_t which is only available on 64bit systems
   ei_add_test(cxx11_tensor_uint128)
 endif()
 endif()
@@ -145,6 +145,7 @@
     ei_add_test_sycl(cxx11_tensor_forced_eval_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_broadcast_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_device_sycl "-std=c++11")
+    ei_add_test_sycl(cxx11_tensor_reduction_sycl "-std=c++11")
   endif(EIGEN_TEST_SYCL)
   # It should be safe to always run these tests as there is some fallback code for
   # older compiler that don't support cxx11.
diff --git a/unsupported/test/cxx11_tensor_broadcast_sycl.cpp b/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
index ecebf7d..7201bfe 100644
--- a/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_broadcast_sycl.cpp
@@ -25,55 +25,50 @@
 using Eigen::Tensor;
 using Eigen::TensorMap;
 
-// Types used in tests:
-using TestTensor = Tensor<float, 3>;
-using TestTensorMap = TensorMap<Tensor<float, 3>>;
-static void test_broadcast_sycl(){
+static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){
 
-	cl::sycl::gpu_selector s;
-  cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
-    for (const auto& e : l) {
-      try {
-        std::rethrow_exception(e);
-      } catch (cl::sycl::exception e) {
-        std::cout << e.what() << std::endl;
+  // BROADCAST test:
+  array<int, 4> in_range   = {{2, 3, 5, 7}};
+  array<int, 4> broadcasts = {{2, 3, 1, 4}};
+  array<int, 4> out_range;  // = in_range * broadcasts
+  for (size_t i = 0; i < out_range.size(); ++i)
+    out_range[i] = in_range[i] * broadcasts[i];
+
+  Tensor<float, 4>  input(in_range);
+  Tensor<float, 4> out(out_range);
+
+  for (size_t i = 0; i < in_range.size(); ++i)
+    VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
+
+
+  for (int i = 0; i < input.size(); ++i)
+    input(i) = static_cast<float>(i);
+
+  float * gpu_in_data  = static_cast<float*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(float)));
+  float * gpu_out_data  = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
+
+  TensorMap<Tensor<float, 4>>  gpu_in(gpu_in_data, in_range);
+  TensorMap<Tensor<float, 4>> gpu_out(gpu_out_data, out_range);
+  sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(float));
+  gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
+
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 0; j < 9; ++j) {
+      for (int k = 0; k < 5; ++k) {
+        for (int l = 0; l < 28; ++l) {
+          VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l));
+        }
       }
     }
-  });
-	SyclDevice sycl_device(q);
-	// BROADCAST test:
-	array<int, 4> in_range   = {{2, 3, 5, 7}};
-	array<int, in_range.size()> broadcasts = {{2, 3, 1, 4}};
-	array<int, in_range.size()> out_range;  // = in_range * broadcasts
-	for (size_t i = 0; i < out_range.size(); ++i)
-		out_range[i] = in_range[i] * broadcasts[i];
-
-	Tensor<float, in_range.size()>  input(in_range);
-	Tensor<float, out_range.size()> output(out_range);
-
-	for (int i = 0; i < input.size(); ++i)
-		input(i) = static_cast<float>(i);
-
-	TensorMap<decltype(input)>  gpu_in(input.data(), in_range);
-	TensorMap<decltype(output)> gpu_out(output.data(), out_range);
-		gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
-		sycl_device.deallocate(output.data());
-
-	for (size_t i = 0; i < in_range.size(); ++i)
-		VERIFY_IS_EQUAL(output.dimension(i), out_range[i]);
-
-	for (int i = 0; i < 4; ++i) {
-		for (int j = 0; j < 9; ++j) {
-			for (int k = 0; k < 5; ++k) {
-				for (int l = 0; l < 28; ++l) {
-					VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), output(i,j,k,l));
-				}
-			}
-		}
-	}
-	printf("Broadcast Test Passed\n");
+  }
+  printf("Broadcast Test Passed\n");
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
 }
 
 void test_cxx11_tensor_broadcast_sycl() {
-  CALL_SUBTEST(test_broadcast_sycl());
+  cl::sycl::gpu_selector s;
+  Eigen::SyclDevice sycl_device(s);
+  CALL_SUBTEST(test_broadcast_sycl(sycl_device));
 }
diff --git a/unsupported/test/cxx11_tensor_device_sycl.cpp b/unsupported/test/cxx11_tensor_device_sycl.cpp
index f54fc87..7f79753 100644
--- a/unsupported/test/cxx11_tensor_device_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_device_sycl.cpp
@@ -20,20 +20,12 @@
 #include "main.h"
 #include <unsupported/Eigen/CXX11/Tensor>
 
-void test_device_sycl() {
-  cl::sycl::gpu_selector s;
-  cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
-    for (const auto& e : l) {
-      try {
-        std::rethrow_exception(e);
-      } catch (cl::sycl::exception e) {
-        std::cout << e.what() << std::endl;
-      }
-    }
-  });
-  Eigen::SyclDevice sycl_device(q);
-	printf("Helo from ComputeCpp: Device Exists\n");
+void test_device_sycl(const Eigen::SyclDevice &sycl_device) {
+  std::cout <<"Helo from ComputeCpp: the requested device exists and the device name is : "
+    << sycl_device.m_queue.get_device(). template get_info<cl::sycl::info::device::name>() <<std::endl;;
 }
 void test_cxx11_tensor_device_sycl() {
-  CALL_SUBTEST(test_device_sycl());
+  cl::sycl::gpu_selector s;
+  Eigen::SyclDevice sycl_device(s);
+  CALL_SUBTEST(test_device_sycl(sycl_device));
 }
diff --git a/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp b/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
index 182ec7f..5690da7 100644
--- a/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_forced_eval_sycl.cpp
@@ -22,18 +22,7 @@
 
 using Eigen::Tensor;
 
-void test_forced_eval_sycl() {
-	cl::sycl::gpu_selector s;
-  cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
-    for (const auto& e : l) {
-      try {
-        std::rethrow_exception(e);
-      } catch (cl::sycl::exception e) {
-        std::cout << e.what() << std::endl;
-      }
-    }
-  });
-	SyclDevice sycl_device(q);
+void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) {
 
   int sizeDim1 = 100;
   int sizeDim2 = 200;
@@ -43,17 +32,22 @@
   Eigen::Tensor<float, 3> in2(tensorRange);
   Eigen::Tensor<float, 3> out(tensorRange);
 
+  float * gpu_in1_data  = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float)));
+  float * gpu_in2_data  = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float)));
+  float * gpu_out_data =  static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
+
   in1 = in1.random() + in1.constant(10.0f);
   in2 = in2.random() + in2.constant(10.0f);
 
-	// creating TensorMap from tensor
-  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(in1.data(), tensorRange);
-  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(in2.data(), tensorRange);
-  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(out.data(), tensorRange);
-
+  // creating TensorMap from tensor
+  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
+  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
+  Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
+  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(float));
+  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in1.dimensions().TotalSize())*sizeof(float));
   /// c=(a+b)*b
-	gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
-	sycl_device.deallocate(out.data());
+  gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -62,7 +56,15 @@
       }
     }
   }
-	printf("(a+b)*b Test Passed\n");
+  printf("(a+b)*b Test Passed\n");
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+  sycl_device.deallocate(gpu_out_data);
+
 }
 
-void test_cxx11_tensor_forced_eval_sycl() { CALL_SUBTEST(test_forced_eval_sycl()); }
+void test_cxx11_tensor_forced_eval_sycl() {
+  cl::sycl::gpu_selector s;
+  Eigen::SyclDevice sycl_device(s);
+  CALL_SUBTEST(test_forced_eval_sycl(sycl_device));
+}
diff --git a/unsupported/test/cxx11_tensor_reduction_sycl.cpp b/unsupported/test/cxx11_tensor_reduction_sycl.cpp
new file mode 100644
index 0000000..a9ef829
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_reduction_sycl.cpp
@@ -0,0 +1,138 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#define EIGEN_TEST_NO_LONGDOUBLE
+#define EIGEN_TEST_NO_COMPLEX
+#define EIGEN_TEST_FUNC cxx11_tensor_reduction_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+
+
+static void test_full_reductions_sycl(const Eigen::SyclDevice&  sycl_device) {
+
+  const int num_rows = 452;
+  const int num_cols = 765;
+  array<int, 2> tensorRange = {{num_rows, num_cols}};
+
+  Tensor<float, 2> in(tensorRange);
+  Tensor<float, 0> full_redux;
+  Tensor<float, 0> full_redux_gpu;
+
+  in.setRandom();
+
+  full_redux = in.sum();
+
+  float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float)));
+  float* gpu_out_data =(float*)sycl_device.allocate(sizeof(float));
+
+  TensorMap<Tensor<float, 2> >  in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<float, 0> >  out_gpu(gpu_out_data);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float));
+  out_gpu.device(sycl_device) = in_gpu.sum();
+  sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(float));
+  // Check that the CPU and GPU reductions return the same result.
+  VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+static void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device) {
+
+  int dim_x = 145;
+  int dim_y = 1;
+  int dim_z = 67;
+
+  array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}};
+  Eigen::array<int, 1> red_axis;
+  red_axis[0] = 0;
+  array<int, 2> reduced_tensorRange = {{dim_y, dim_z}};
+
+  Tensor<float, 3> in(tensorRange);
+  Tensor<float, 2> redux(reduced_tensorRange);
+  Tensor<float, 2> redux_gpu(reduced_tensorRange);
+
+  in.setRandom();
+
+  redux= in.sum(red_axis);
+
+  float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float)));
+  float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float)));
+
+  TensorMap<Tensor<float, 3> >  in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<float, 2> >  out_gpu(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float));
+  out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float));
+
+  // Check that the CPU and GPU reductions return the same result.
+  for(int j=0; j<reduced_tensorRange[0]; j++ )
+    for(int k=0; k<reduced_tensorRange[1]; k++ )
+      VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+}
+
+static void test_last_dim_reductions_sycl(const Eigen::SyclDevice &sycl_device) {
+
+  int dim_x = 567;
+  int dim_y = 1;
+  int dim_z = 47;
+
+  array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}};
+  Eigen::array<int, 1> red_axis;
+  red_axis[0] = 2;
+  array<int, 2> reduced_tensorRange = {{dim_x, dim_y}};
+
+  Tensor<float, 3> in(tensorRange);
+  Tensor<float, 2> redux(reduced_tensorRange);
+  Tensor<float, 2> redux_gpu(reduced_tensorRange);
+
+  in.setRandom();
+
+  redux= in.sum(red_axis);
+
+  float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float)));
+  float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float)));
+
+  TensorMap<Tensor<float, 3> >  in_gpu(gpu_in_data, tensorRange);
+  TensorMap<Tensor<float, 2> >  out_gpu(gpu_out_data, reduced_tensorRange);
+
+  sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float));
+  out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
+  sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float));
+  // Check that the CPU and GPU reductions return the same result.
+  for(int j=0; j<reduced_tensorRange[0]; j++ )
+    for(int k=0; k<reduced_tensorRange[1]; k++ )
+      VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
+
+  sycl_device.deallocate(gpu_in_data);
+  sycl_device.deallocate(gpu_out_data);
+
+}
+
+void test_cxx11_tensor_reduction_sycl() {
+  cl::sycl::gpu_selector s;
+  Eigen::SyclDevice sycl_device(s);
+  CALL_SUBTEST((test_full_reductions_sycl(sycl_device)));
+  CALL_SUBTEST((test_first_dim_reductions_sycl(sycl_device)));
+  CALL_SUBTEST((test_last_dim_reductions_sycl(sycl_device)));
+
+}
diff --git a/unsupported/test/cxx11_tensor_sycl.cpp b/unsupported/test/cxx11_tensor_sycl.cpp
index 0f66cd8..6a9c334 100644
--- a/unsupported/test/cxx11_tensor_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_sycl.cpp
@@ -27,42 +27,33 @@
 using Eigen::Tensor;
 using Eigen::TensorMap;
 
-// Types used in tests:
-using TestTensor = Tensor<float, 3>;
-using TestTensorMap = TensorMap<Tensor<float, 3>>;
-
-void test_sycl_cpu() {
-  cl::sycl::gpu_selector s;
-  cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
-    for (const auto& e : l) {
-      try {
-        std::rethrow_exception(e);
-      } catch (cl::sycl::exception e) {
-        std::cout << e.what() << std::endl;
-      }
-    }
-  });
-  SyclDevice sycl_device(q);
+void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) {
 
   int sizeDim1 = 100;
   int sizeDim2 = 100;
   int sizeDim3 = 100;
   array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
-  TestTensor in1(tensorRange);
-  TestTensor in2(tensorRange);
-  TestTensor in3(tensorRange);
-  TestTensor out(tensorRange);
-  in1 = in1.random();
+  Tensor<float, 3> in1(tensorRange);
+  Tensor<float, 3> in2(tensorRange);
+  Tensor<float, 3> in3(tensorRange);
+  Tensor<float, 3> out(tensorRange);
+
   in2 = in2.random();
   in3 = in3.random();
-  TestTensorMap gpu_in1(in1.data(), tensorRange);
-  TestTensorMap gpu_in2(in2.data(), tensorRange);
-  TestTensorMap gpu_in3(in3.data(), tensorRange);
-  TestTensorMap gpu_out(out.data(), tensorRange);
+
+  float * gpu_in1_data  = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float)));
+  float * gpu_in2_data  = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float)));
+  float * gpu_in3_data  = static_cast<float*>(sycl_device.allocate(in3.dimensions().TotalSize()*sizeof(float)));
+  float * gpu_out_data =  static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
+
+  TensorMap<Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
+  TensorMap<Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
+  TensorMap<Tensor<float, 3>> gpu_in3(gpu_in3_data, tensorRange);
+  TensorMap<Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
 
   /// a=1.2f
   gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
-  sycl_device.deallocate(in1.data());
+  sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.dimensions().TotalSize())*sizeof(float));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -74,7 +65,7 @@
 
   /// a=b*1.2f
   gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
-  sycl_device.deallocate(out.data());
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.dimensions().TotalSize())*sizeof(float));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -86,8 +77,9 @@
   printf("a=b*1.2f Test Passed\n");
 
   /// c=a*b
+  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.dimensions().TotalSize())*sizeof(float));
   gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
-  sycl_device.deallocate(out.data());
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -101,7 +93,7 @@
 
   /// c=a+b
   gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
-  sycl_device.deallocate(out.data());
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -115,7 +107,7 @@
 
   /// c=a*a
   gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
-  sycl_device.deallocate(out.data());
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -125,12 +117,11 @@
       }
     }
   }
-
   printf("c= a*a Test Passed\n");
 
   //a*3.14f + b*2.7f
   gpu_out.device(sycl_device) =  gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
-  sycl_device.deallocate(out.data());
+  sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -143,8 +134,9 @@
   printf("a*3.14f + b*2.7f Test Passed\n");
 
   ///d= (a>0.5? b:c)
+  sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.dimensions().TotalSize())*sizeof(float));
   gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
-  sycl_device.deallocate(out.data());
+  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
   for (int i = 0; i < sizeDim1; ++i) {
     for (int j = 0; j < sizeDim2; ++j) {
       for (int k = 0; k < sizeDim3; ++k) {
@@ -155,8 +147,13 @@
     }
   }
   printf("d= (a>0.5? b:c) Test Passed\n");
-
+  sycl_device.deallocate(gpu_in1_data);
+  sycl_device.deallocate(gpu_in2_data);
+  sycl_device.deallocate(gpu_in3_data);
+  sycl_device.deallocate(gpu_out_data);
 }
 void test_cxx11_tensor_sycl() {
-  CALL_SUBTEST(test_sycl_cpu());
+  cl::sycl::gpu_selector s;
+  Eigen::SyclDevice sycl_device(s);
+  CALL_SUBTEST(test_sycl_cpu(sycl_device));
 }