CMakeLists.txt

@@ -5,7 +5,6 @@ cmake_minimum_required( VERSION 3.5)
 project (sum_harness_instructional LANGUAGES CXX)
 
 set(CMAKE_BUILD_TYPE "Release")
-
 #set(CMAKE_CXX_FLAGS "-Wall")  # uncomment this line to turn on compiler warnings
 
 # info for setting the compiler optimization level
@@ -28,7 +27,7 @@ set(CMAKE_BUILD_TYPE "Release")
 # option 2 (works but not preferred): uncomment one of the following two lines then run/rerun cmake:
 
 # -O3 is full optimization in gcc/g++
-set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
+#set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3")
 # -O0 is no optimization in gcc/g++
 #set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O0")
 

README.md

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

benchmark.cpp

@@ -1,5 +1,5 @@
 //
-// (C) 2022-2023, E. Wes Bethel
+// (C) 2022, E. Wes Bethel
 // benchmark-* harness for running different versions of the sum study
 //    over different problem sizes
 //
@@ -34,7 +34,7 @@ int main(int argc, char** argv)
    /* For each test size */
    for (int64_t n : problem_sizes) 
    {
-      printf("Working on problem size N=%lld \n", n);
+      printf("Working on problem size N=%d \n", n);
 
       // invoke user code to set up the problem
       setup(n, &A[0]);

plot_3vars.py

@@ -42,13 +42,6 @@ xlocs = [i for i in range(len(problem_sizes))]
 
 plt.xticks(xlocs, problem_sizes)
 
-# here, we are plotting the raw values read from the input .csv file, which
-# we interpret as being "time" that maps directly to the y-axis.
-#
-# what if we want to plot MFLOPS instead? How do we compute MFLOPS from
-# time and problem size? You may need to add some code here to compute
-# MFLOPS, then modify the plt.plot() lines below to plot MFLOPS rather than time.
-
 plt.plot(code1_time, "r-o")
 plt.plot(code2_time, "b-x")
 plt.plot(code3_time, "g-^")