Abstract
Graphical Processing Units (GPUs) became an important platform to general purpose computing, thanks to their high performance and low cost when compared to CPUs. However, programming GPUs requires a different mindset and optimization techniques that take advantage of the peculiarities of the GPU architecture. Moreover, GPUs are rapidly changing, in the sense of including capabilities that can improve performance of general purpose applications, such as support for concurrent execution. Thus, benchmark suites developed to evaluate GPU performance and scalability should take those aspects into account and could be quite different from traditional CPU benchmarks. Nowadays, Rodinia, Parboil and SHOC are the main benchmark suites for evaluating GPUs. This work analyzes these benchmark suites in detail and categorizes their behavior in terms of computation type (integer or float), usage of memory hierarchy, efficiency and hardware occupancy. We also intend to evaluate similarities between the kernels of those suites. This characterization will be useful to disclosure the resource requirements of the kernels of these benchmarks that may affect further concurrent execution.
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Notes
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We did not analyze the CFD application, since nvprof was not able to correctly extract the corresponding metrics.
References
Che, S., Boyer, M., Meng, J., Tarjan, D., Sheaffer, J.W., Lee, S.-H., Skadron, K.: Rodinia: a benchmark suite for heterogeneous computing. In: Proceedings of the IEEE International Symposium on Workload Characterization (IISWC), pp. 44–54 (2009)
Stratton, J.A., Rodrigues, C., Sung, I.-J., Obeid, N., Chang, L.-W., Anssari, N., Liu, G.D., Hwu, W.M.W.: Parboil: a revised benchmark suite for scientific and commercial throughput computing (2012)
Danalis, A., Marin, G., McCurdy, C., Meredith, J.S., Roth, P.C., Spafford, K., Tipparaju, V., Vetter, J.S.: The scalable heterogeneous computing (SHOC) benchmark suite. In: Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, pp. 63–74 (2010)
Pai, S., Thazhuthaveetil, M.J., Govindarajan, R.: Improving GPGPU concurrency with elastic kernels. In: ACM SIGPLAN Notices, vol. 48, pp. 407–418. ACM (2013)
Che, S., Sheaffer, J.W., Boyer, M., Szafaryn, L.G., Wang, L., Skadron, K.: A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads. In: Proceedings of the IEEE International Symposium on Workload Characterization (2010)
Kerr, A., Diamos, G., Yalamanchili, S.: A characterization and analysis of PTX kernels. In: IEEE International Symposium on Workload Characterization, IISWC 2009, pp. 3–12. IEEE (2009)
Goswami, N., Shankar, R., Joshi, M., Li, T.: Exploring GPGPU workloads: characterization methodology, analysis and microarchitecture evaluation implications. In: 2010 IEEE International Symposium on Workload Characterization (IISWC), pp. 1–10. IEEE (2010)
Burtscher, M., Nasre, R., Pingali, K.: A quantitative study of irregular programs on GPUs. In: 2012 IEEE International Symposium on Workload Characterization (IISWC), pp. 141–151. IEEE (2012)
O’Neil, M.A., Burtscher, M.: Microarchitectural performance characterization of irregular GPU kernels. In: 2014 IEEE International Symposium on Workload Characterization (IISWC), pp. 130–139. IEEE (2014)
Bakhoda, A., Yuan, G.L., Fung, W.W., Wong, H., Aamodt, T.M.: Analyzing CUDA workloads using a detailed GPU simulator. In: IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2009, pp. 163–174. IEEE (2009)
Bienia, C.: Benchmarking Modern Multiprocessors. Princeton University, Princeton (2011)
Asanovic, K.: The landscape of parallel computing research: a view from Berkeley, Technical report UCB/EECS-2006-183, EECS Department, University of California, Berkley, CA, USA (2006)
SHOC (2012). https://github.com/vetter/shoc/wiki
NVIDIA Corporation: Profiler user’s guide (2017). http://docs.nvidia.com/cuda/profiler-users-guide/index.html#nvprof-overview, an optional note
Bienia, C.: Benchmarking modern multiprocessors, Ph.D. thesis, Princeton University (2011)
Joshi, A., Phansalkar, A., Eeckhout, L., John, L.K.: Measuring benchmark similarity using inherent program characteristics. IEEE Trans. Comput. 55(6), 769–782 (2006)
Che, S., Skadron, K.: Benchfriend: correlating the performance of GPU benchmarks. Int. J. High Perform. Comput. Appl. 28(2), 238–250 (2014)
Spafford, K., Meredith, J., Vetter, J., Chen, J., Grout, R., Sankaran, R.: Accelerating S3D: a GPGPU case study. In: Lin, H.-X., Alexander, M., Forsell, M., Knüpfer, A., Prodan, R., Sousa, L., Streit, A. (eds.) Euro-Par 2009. LNCS, vol. 6043, pp. 122–131. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14122-5_16
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Carvalho, P., Drummond, L.M.A., Bentes, C., Clua, E., Cataldo, E., Marzulo, L.A.J. (2018). Analysis and Characterization of GPU Benchmarks for Kernel Concurrency Efficiency. In: Mocskos, E., Nesmachnow, S. (eds) High Performance Computing. CARLA 2017. Communications in Computer and Information Science, vol 796. Springer, Cham. https://doi.org/10.1007/978-3-319-73353-1_5
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