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Benchmarks used in the gpgpu-sim ispass 2009 paper
This project forked from gpgpu-sim/ispass2009-benchmarks
Benchmarks used in the gpgpu-sim ispass 2009 paper
The directories AES, BFS, CP, LPS, LIB, MUM, NN, NQU, RAY, STO, and WP contain benchmarks used in the ISPASS 2009 paper on GPGPU-Sim. Their original sources are listed below. The instructions below describe how to build and run the benchmarks assuming you are using GPGPU-Sim v3.x. This series of instructions is essentially the procedure we follow when using GPGPU-Sim v3.x in our research at UBC. Assuming all dependencies required by the benchmarks are satisifed, you can build/run them by doing the following: 1. Install the NVIDIA CUDA Toolkit and then install/build the NVIDIA CUDA SDK benchmarks. Note the CUDA Toolkit needs to be installed before you can build the CUDA SDK. Building the CUDA SDK is required since some of the ISPASS 2009 benchmarks use a library created when building the CUDA SDK. 2. Open Makefile.ispass-2009 in your favorite text editor and set the variables at the top of the file to suit your environment. 3. Define the following environment variables: CUDA_INSTALL_PATH NVIDIA_COMPUTE_SDK_LOCATION The first, CUDA_INSTALL_PATH, should point to the directory you installed the NVIDIA CUDA Toolkit (e.g., /usr/local/cuda). The second, NVIDIA_COMPUTE_SDK_LOCATION, should point to the directory you installed the NVIDIA GPU Computing SDK (e.g., ~/NVIDIA_GPU_Computing_SDK) You must also ensure your PATH includes $CUDA_INSTALL_PATH/bin. 4. run "make -f Makefile.ispass-2009" in this directory. You should NOT need to copy or move any files for this to work. 5. Verify binaries were generated for the benchmarks in ../bin/release/ (relative to the directory this file located in) 6. Type "source setup_environment" in the v3.x directory. This modifies your LD_LIBRARY_PATH to use GPGPU-Sim for CUDA/OpenCL applications. If you have not already done so, build GPGPU-Sim now. 7. Place a link to the configuration files (gpgpusim.config and the interconnect configuration file) in the simulation run directory. You can do this using the script "setup_config.sh" you will find in this directory, which creates symbolic links. For example type: ./setup_config.sh GTX480 Aside: If later you want to change the configuration, you need to first run ./setup_config.sh --cleanup then run ./setup_config.sh again with the configuration you want. 8. Run one of the applications by typing the command line in the README.GPGPU-Sim in the benchmark directory. For example, cd AES sh README.GPGPU-Sim 9. When debugging the simulator, you should build GPGPU-Sim in debug mode: cd $GPGPUSIM_ROOT source setup_environment debug make clean make Example of running the simulator in gdb (starting from this directory): cd AES gdb --args `cat README.GPGPU-Sim` # different steps required for WP ############################################################################### # References # ############################################################################### AES S. A. Manavski. CUDA compatible GPU as an efficient hardware accelerator for AES cryptography. In ICSPC 2007: Proc. of IEEE Int’l Conf. on Signal Processing and Communication, pages 65–68, 2007. BFS: P. Harish and P. J. Narayanan. Accelerating Large Graph Algorithms on the GPU Using CUDA. In HiPC, pages 197–208, 2007. CP: http://www.crhc.uiuc.edu/IMPACT/parboil.php. DG: T. C. Warburton. Mini Discontinuous Galerkin Solvers. http://www.caam.rice.edu/˜timwar/RMMC/MIDG.html. LPS: M. Giles. Jacobi iteration for a Laplace discretisation on a 3D structured grid. http://people.maths.ox.ac.uk/˜gilesm/hpc/NVIDIA/laplace3d.pdf LIB: M. Giles and S. Xiaoke. Notes on using the NVIDIA 8800 GTX graphics card. http://people.maths.ox.ac.uk/˜gilesm/hpc/ MUM: M. Schatz, C. Trapnell, A. Delcher, and A. Varshney. High-throughput sequence alignment using Graphics Processing Units. BMC Bioinformatics, 8(1):474, 2007. NN: Billconan and Kavinguy. A Neural Network on GPU. http://www.codeproject.com/KB/graphics/GPUNN.aspx. NQU: Pcchen. N-Queens Solver. http://forums.nvidia.com/index.php?showtopic=76893 RAY: Maxime. Ray tracing. http://www.nvidia.com/cuda. STO: S. Al-Kiswany, A. Gharaibeh, E. Santos-Neto, G. Yuan, and M. Ripeanu. StoreGPU: exploiting graphics processing units to accelerate distributed storage systems. In Proc. 17th Int’l Symp. on High Performance Distributed Computing, pages 165–174, 2008. WP: J. Michalakes and M. Vachharajani. GPU acceleration of numerical weather prediction. IPDPS 2008: IEEE Int’l Symp. on Parallel and Distributed Processing, pages 1–7, April 2008.
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