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SYCL Academy

Exercise 4: Vector add

In this first exercise you will learn:

  • How to manage data using buffers.
  • How to access data using accessors.
  • How to define an nd range SYCL kernel function.

In SYCL buffers are used to manage data across the host and device(s), and accessors are used to declare data dependencies to a SYCL kernel function as well as to access the data within a SYCL kernel function.

1.) Allocate your input and output vectors

Allocate memory on the host for your input and output data using std::vectors and initialize the input with values.

2.) Construct buffers

Construct a buffer to manage your input and output data. The template parameters for the the buffer class are the type and then the dimensionality. The parameters to construct a buffer are a pointer to the host data and a range.

Remember the dimensionality of the range must match the dimensionality of the buffer.

3.) Construct accessors

Construct an accessor for your input and output buffers. The template parameter to get_access is the access mode that specifies how you wish to use the data managed by the buffer.

Remember to pass the handler to get_access, if you don't this will construct a host accessor, which behaves differently to a regular accessor.

4.) Declare your kernel

Declare a SYCL kernel function using the parallel_for command with a range matching the size of the std::vectors. The kernel function should use the operator[] of the accessor objects to read from the inputs and write the sum to the output.

Remember the accessor's operator[] can take either a size_t (when the dimensionality is 1) and an id.

5.) Try a temporary buffer

You can construct a temporary buffer that doesn't copy back on destruction by initializing it with just a range and no host pointer.

Build And Execution Hints

For For DPC++ (using the Intel DevCloud):

dpcpp -o sycl-ex-4 -I../External/Catch2/single_include ../Code_Exercises/Exercise_4_Vector_Add/source.cpp

In Intel DevCloud, to run computational applications, you will submit jobs to a queue for execution on compute nodes, especially some features like longer walltime and multi-node computation is only abvailable through the job queue. Please refer to the example.

So wrap the binary into a script job_submission and run:

qsub job_submission

For ComputeCpp:

cmake -DSYCL_ACADEMY_USE_COMPUTECPP=ON -DSYCL_ACADEMY_INSTALL_ROOT=/insert/path/to/computecpp ..
make Exercise_4_source
./Code_Exercises/Exercise_4_Vector_Add/Exercise_4_source

For hipSYCL:

# Add -DHIPSYCL_GPU_ARCH=<arch> to the cmake arguments when compiling for GPUs.
# <arch> is e.g. sm_60 for NVIDIA Pascal GPUs, gfx900 for AMD Vega 56/64, and gfx906 for Radeon VII.
cmake -DSYCL_ACADEMY_USE_HIPSYCL=ON -DSYCL_ACADEMY_INSTALL_ROOT=/insert/path/to/hipsycl -DHIPSYCL_PLATFORM=<cpu|cuda|rocm> ..
make Exercise_4_source
./Code_Exercises/Exercise_4_Vector_Add/Exercise_4_source

alternatively, without cmake:

cd Code_Exercises/Exercise_4_Vector_Add
HIPSYCL_PLATFORM=<cpu|cuda|rocm> HIPSYCL_GPU_ARCH=<arch-when-compiling-for-gpu> /path/to/hipsycl/bin/syclcc -o sycl-ex-4 -I../../External/Catch2/single_include source.cpp
./sycl-ex-4