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.
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.
From the syclacademy directory
cd build/Code_Exercises/Coalesced_Global_Memory
and execute:
make Coalesced_Global_Memory_source - to build source.cppmake Coalesced_Global_Memory_solution - to build the solution providedmake - to build bothAlternatively 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