Indigo3 is a labeled benchmark suite based on 7 graph algorithms that are implemented in different styles, including versions with deliberately planted bugs.
To generate all codes for all models, run the generate script with no arguments:
python3 ./generate_all_codes.py
The script has 2 optional parameters for specifying a specific model and/or code. The model options are C, CPP, OMP, and ALL. The code options are BFS, CC, MIS, MST, PR, SSSP, TC, and ALL. Both parameters are case-insensitive and default to ALL.
For example, this command will generate all of the OpenMP codes:
python3 ./generate_all_codes.py omp
While this command will generate the Connected Components codes for all models:
python3 ./generate_all_codes.py all cc
The generated codes will be in the generatedCodes directory. You can also modify configure.txt under the codeGen directory to define the type of bugs you want to generate.
To compile all codes for all models from the generatedCodes directory, run the compile script with only the gpu_compute_capability parameter:
python3 ./compile_all_codes.py <gpu_compute_capability>
The gpu_compute_capability parameter specifies the targeted NVIDIA GPU compute capability for CUDA codes. For example, to compile all CUDA codes for a Titan V, which has a compute capability of 7.0, use the following:
python3 ./compile_all_codes.py 70 cuda
The compile script has the same optional parameters as the generate script. For example, this command will compile just the Breadth-First Search C++ codes:
python3 ./compile_all_codes.py 0 cpp bfs
The compiled executables will be in the executables directory.
A ./run_all_codes.py script is also provided. It looks in the executables directory. To see the full list of parameters, run it without arguments:
python3 ./run_all_codes.py
For example, this command will run the compiled Connected Components C codes on the inputs in ./inputs/ using 32 CPU threads and write the output to ./run_logs/:
python3 ./run_all_codes.py ./inputs/ 1 32 0 1 c cc
Small sample inputs are available in the inputs directory. The download_large_inputs.sh script will download five additional large graphs and place them in a large_inputs directory. Graph generators for creating additional small inputs are provided in the graphGen directory with their own README.
The codes in this suite use ECL graphs stored in binary CSR format. Converters and additional inputs are available here.
If you use Indigo3, please cite the following publication.
- Yiqian Liu, Noushin Azami, Avery Vanausdal, and Martin Burtscher. "Indigo3: A Parallel Graph Analytics Benchmark Suite for Exploring Implementation Styles and Common Bugs." ACM Transactions on Parallel Computing. May 2024. [doi] [pdf]
You may also be interested in the predecessor suites Indigo and Indigo2 as well as in the related ECL-Suite.
The following paper describes ideas on how to use Indigo3 (aka Sapphire) for teaching parallel programming.
- Yiqian Liu, Noushin Azami, Avery Vanausdal, and Martin Burtscher. "Sapphire: a Tool for Teaching Parallel Programming in Hundreds of Different Ways." Proceedings of the 16th Annual International Conference on Education and New Learning Technologies. July 2024. [doi] [pdf] [pptx]
This work has been supported in part by the National Science Foundation under Grant No. 1955367 as well as by an equipment donation from NVIDIA Corporation.