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SYCL Academy

Exercise 16: Coalesced Global Memory

In this exercise you will learn how to apply row-major and column-major when linearizing the global id in order to compare the performance difference due to coalesced global memory access.

1.) Evaluate global memory access

Now that you have a working image convolution kernel you should evaluate whether the global memory access patterns in your kernel are coalesced.

Consider two alternative ways to linearize the global id:

auto rowMajorLinearId    = (idx[1] * width) + idx[0];  // row-major
auto columnMajorLinearId = (idx[0] * height) + idx[1];  // column-major

Try using both of these and compare the execution time of each.

Build and execution hints

From the syclacademy directory

cd build/Code_Exercises/Coalesced_Global_Memory

and execute:

  • make Coalesced_Global_Memory_source - to build source.cpp
  • make Coalesced_Global_Memory_solution - to build the solution provided
  • make - to build both

Alternatively from a terminal at the command line:

icpx -fsycl -o Coalesced_Global_Memory_source -I../../Utilities/include/ -I../../External/stb ../Code_Exercises/Coalesced_Global_Memory/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 available through the job queue. Please refer to the [guide][devcloud-job-submission].

So wrap the binary into a script job_submission

#!/bin/bash
./Coalesced_Global_Memory_source

and run:

qsub -l nodes=1:gpu:ppn=2 -d . job_submission

The stdout will be stored in job_submission.o<job id> and stderr in job_submission.e<job id>.

For DPC++: Using CMake to configure then build the exercise:

mkdir build
cd build
cmake .. "-GUnix Makefiles" -DSYCL_ACADEMY_USE_DPCPP=ON -DSYCL_ACADEMY_ENABLE_SOLUTIONS=OFF -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
make Coalesced_Global_Memory_source

Alternatively from a terminal at the command line:

icpx -fsycl -o Coalesced_Global_Memory_source ../Code_Exercises/Coalesced_Global_Memory/source.cpp
./Coalesced_Global_Memory_source

For AdaptiveCpp:

# <target specification> is a list of backends and devices to target, for example
# "generic" compiles for CPUs and GPUs using the generic single-pass compiler.
# When in doubt, use "generic" as it usually generates the fastest binaries.
#
# Recent, full installations of AdaptiveCpp may not need targets to be provided,
# compiling for "generic" by default.
cmake -DSYCL_ACADEMY_USE_ADAPTIVECPP=ON -DSYCL_ACADEMY_INSTALL_ROOT=/insert/path/to/adaptivecpp -DACPP_TARGETS="<target specification>" ..
make Coalesced_Global_Memory_source

alternatively, without CMake:

cd Code_Exercises/Coalesced_Global_Memory
/path/to/adaptivecpp/bin/acpp -o Coalesced_Global_Memory_source --acpp-targets="<target specification>" source.cpp
./Coalesced_Global_Memory_source