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@ -4,9 +4,9 @@
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This directory contains a benchmark harness for testing different implementations of
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summing numbers.
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A single high-level main() is present in the benchmark.cpp file, which has definitions of proble3m sizes and so forth.
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A single high-level main() is present in the benchmark.cpp file, which has definitions of problem sizes and so forth.
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This main() will make calls to two routines that must be provided by your code:
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main() will make calls to two routines that must be provided by your code:
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* setup(N, A) // where you initialize N values in A
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* result = sum(N, A) // where you compute the sum of N values in A and return the answer
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@ -15,24 +15,122 @@ This harness will generate three different executables using the one benchmark.c
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Your job is to:
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* Add code for setup() and sum() in each of sum_direct.cpp, sum_indirect.cpp, and sum_vector.cpp
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* Add instrumention code in benchmark.cpp to measure elapsed time for the call to the sum() routine
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* Add instrumention code in benchmark.cpp to measure elapsed time for the call to the sum() routine.
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Have a look at the [chrono_timer](https://github.com/SFSU-Bethel-Instructional/chrono_timer) code repo for an example of instrumentation code that measures
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elapsed time.
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You should not need to modify anything inside CMakeLists.txt.
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# Build environment prerequisites
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To build and run this code, you need to have the following software tools installed on your platform:
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* C++ compiler
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* cmake
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* make
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# Default build instructions:
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`% cd sum_harness_instructional` # contains the source files and CMakeLists.txt file
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`% mkdir build`
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`% cd build`
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`% cmake ../` # cmake generates lots of output
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`% make` # to build the programs
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cd sum_harness_instructional # contains the source files and CMakeLists.txt file
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mkdir build
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cd build
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cmake ../ # cmake generates lots of output
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make # to build the programs
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# Adding your code
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You will need to add code in three places:
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* Inside benchmark.cpp: please add instrumentation code that will measure and report elapsed time consumed by the call to the sum() routine. Please refer to the [chrono_timer code](https://github.com/SFSU-Bethel-Instructional/chrono_timer) for an example of how to do this kind of time measurement.
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* The setup() routine inside each of sum_direct.cpp, sum_indirect.cpp, and sum_vector.cpp. See the homework writeup for details on how to perform initialization for each of these different codes.
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* The sum() routine inside each of sum_direct.cpp, sum_indirect.cpp, and sum_vector.cpp. See the homework writeup for details on how to perform the sum operation for each of these different codes.
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# Running the codes
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Once the codes are built, you should be able to just run each one from the command line from within your build directory:
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./sum_direct
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or
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./sum_indirect
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or
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./sum_vector
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When you run each code, it will iterate through the set of problem sizes predefined inside benchmark.cpp
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The instrumentation code you added to benchmark.cpp should report the elapsed time for your sum() method's execution for each problem size.
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# Building and running the codes on Perlmutter@NERSC
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After [logging in to perlmutter at NERSC,](https://docs.nersc.gov/systems/perlmutter/), either pull the code directly from git or [transfer a copy from your local machine to NERSC](https://docs.nersc.gov/services/scp/).
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Set up your environment to make use of the CPU nodes by typing in this command:
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module load cpu
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Then follow the build instructions above.
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Once you have built the codes, you may request interactive access to a Perlmutter CPU node by using this command:
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salloc --nodes 1 --qos interactive --time 00:30:00 --constraint cpu --account=m3930
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Once you are on an interactive Perlmutter CPU node, run each of the codes using these commands from within your build directory:
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./sum_direct
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./sum_indirect
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./sum_vector
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# Using the Python scripts for plotting on Perlmutter@NERSC
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Included in the code harness are two Python files that will load a
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csv text file and use matplotlib.pyplot to create a 3-variable chart.
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Please modify these Python files as needed to update the axis labels,
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plot title, and so forth.
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To run Python on Perlmutter, first do a:
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module load python
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That command will make available to you a full conda environment that
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is preloaded with many of the commonly used Python packages. The default
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version of Python as of the time of this writing is 3.11.6.
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Once you've loaded the python module, you can see the set of
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installed packages using this command:
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conda list
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When you run the provided plot\_3vars.py Perlmutter, it will produce some
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output to the console and will also attempt to display the plot on your
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screen.
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In order for the display to actually appear on your screen you must use
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the -Y argument with ssh when you login, e.g.:
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ssh -Y user@saul-p1.nersc.gov
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There are two python scripts in the distro: plot\_3vars.py and
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plot\_3vars\_savefig.py. The difference between them is that the
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plot\_3vars\_savefig.py will in addition to trying to display
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the plot to the screen also save an image file named *myplot.png*.
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# Additional build options -- Compiler Optimization Level
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As of the time of this writing (Oct 2023), most SFSU students will not need
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the information about different build options unless otherwise
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instructed.
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By default, the CMakeLists.txt will do a "Release" build, which means there will be full compiler optimizations.
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There are two methods for modifying the compiler optimization level.
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If need be, there are two methods for modifying the compiler optimization level.
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Option 1 (best approach): set the CMAKE_CXX_FLAGS_RELEASE environment variable then run cmake
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@ -61,56 +159,4 @@ For -O0: no optimization in gcc/g++
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After modifying CMakeLists.txt, clean your build directory, and rerun cmake and then make.
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# Adding your code
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You will need to add code in three places:
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* Inside benchmark.cpp: please add instrumentation code that will measure and report elapsed time consumed by the call to the sum() routine. Please refer to the [chrono_timer code](https://github.com/SFSU-CSC746/chrono_timer) for an example of how to do this kind of time measurement.
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* The setup() routine inside each of sum_direct.cpp, sum_indirect.cpp, and sum_vector.cpp. See the homework writeup for details on how to perform initialization for each of these different codes.
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* The sum() routine inside each of sum_direct.cpp, sum_indirect.cpp, and sum_vector.cpp. See the homework writeup for details on how to perform the sum operation for each of these different codes.
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# Running the codes
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Once the codes are built, you should be able to just run each one from the command line:
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`% ./sum_direct`
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or
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`% ./sum_indirect`
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or
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`% ./sum_vector`
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When you run each code, it will iterate through the set of problem sizes predefined inside benchmark.cpp
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# Building and running the codes on Perlmutter@NERSC
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After [logging in to perlmutter at NERSC,](https://docs.nersc.gov/systems/perlmutter/), either pull the code directly from git or [transfer a copy from your local machine to NERSC](https://docs.nersc.gov/services/scp/).
|
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|
Set up your environment to make use of the CPU nodes by typing in this command:
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`% module load cpu`
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Then follow the build instructions above.
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Once you have built the codes, you may request interactive access to a Perlmutter CPU node by using this command:
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`% salloc --nodes 1 --qos interactive --time 00:30:00 --constraint cpu --account=m3930`
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Once you are on an interactive CPU node, run each of the codes using these commands:
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`srun ./sum_direct`
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`srun ./sum_indirect`
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`srun ./sum_vector`
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# Building and running the codes on Cori@NERSC (deprected as of March 2023)
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Please refer to lecture slides for additional information about accessing Cori, building your code there, and running your code there.
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# EOF
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