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Are GPUs Non-Green Computing Devices?
DOI:
https://doi.org/10.24215/16666038.18.e17Keywords:
Power, Rodinia, GPU, NVML, RAPLAbstract
With energy consumption emerging as one of the biggest issues in the development of HPC (High Performance Computing) applications, the importance of detailed power-related research works becomes a priority. In the last years, GPU coprocessors have been increasingly used to accelerate many of these high-priced systems even though they are embedding millions of transistors on their chips delivering an immediate increase on power consumption necessities. This paper analyzes a set of applications from the Rodinia benchmark suite in terms of CPU and GPU performance and energy consumption. Specifically, it compares single-threaded and multi-threaded CPU versions with GPU implementations, and characterize the execution time, true instant power and average energy consumption to test the idea that GPUs are power-hungry computing devices.
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References
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[2] X. Mei, Q. Wang, X. Chu. A survey and measurement study of GPU DVFS on energy conservation. Digital Communications and Networks, vol. 3, no. 2, pp. 89-100, 2017.
[3] X. Ma, M. Dong, L. Zhong, Z. Deng. “Statistical Power Consumption Analysis and Modeling for GPU-based Computing”. In: HotPower ACM SOSP Workshop Power Aware Computing and Systems, pp. 1-5, 2009.
[4] H. Nagasaka, N. Maruyama. “Statistical Power Modeling of GPU Kernels Using Performance Counters”. In: International Conference on Green Computing, pp. 115-122, 2010.
[5] R. Suda, Da Qi Ren. “Accurate Measurements and Precise Modeling of Power Dissipation of CUDA Kernels toward Power Optimized High Performance CPU-GPU Computing”. In: International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 432-438, 2009.
[6] S. Huang, S. Xiao, W. Feng. “On the energy efficiency of Graphic Processing Units for scientific computing”. In: IEEE Symposium on Parallel & Distributed Processing, pp. 1-8, 2009.
[7] J. Lang, G. Rünger. “High-Resolution Power Profiling of GPU Functions Using Low-Resolution Measurement”. In: Euro-Par 2013 Parallel Processing Lecture Notes in Computer Science vol. 8097, Springer, pp. 801–812, 2013.
[8] NVML Reference Guide, http://docs.nvidia.com/deploy/nvml-api/index.html. Accessed on 2018-04-13.
[9] S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, S. Lee, K. Skadron. “Rodinia: A benchmark suite for heterogeneous computing”. In: IEEE International Symposium on Workload Characterization (IISWC), pp. 44-54, 2009.
[10]OpenMP Application Programming Interface, http://www.openmp.org/wpcontent/uploads/openmp-4.5.pdf. Accessed on 2018-04-13.
[11]10 K. Asanovic et al. “The landscape of parallel computing research: A view from Berkeley”. Technical Report UCB/EECS-2006-183, EECS Department, University of California, Berkeley, pp. 56-67, 2006.
[12]S. Che, J. W. Sheaffer, M. Boyer, L. G. Szafaryn, L. Wang, K. Skadron. “A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads”. In: IEEE International Symposium on Workload Characterization (IISWC), pp. 1-11, 2010.
[13]Running Average Power Limit – RAPL, https://01.org/blogs/2014/running-average-power-limit-%E2%80%93-rapl. Accessed on 2018-04-13.
[14]13 E. Rotem, A. Naveh, D. Rajwan, A. Anathakrishnan, E. Weissmann, “Powermanagement architecture of the Intel microarchitecture codenamed Sandy Bridge”. In: IEEE Micro, vol. 32, no. 2, pp. 20–27, 2012.
[15]M. Burtscher, I. Zecena, Z. Zong. “Measuring GPU Power with the K20 Built-in Sensor”. In: GPGPU-7 Proceedings of Workshop on General Purpose Processing Using GPUs, pp. 28-36, 2014.
[16]V. Weaver, M. Johnson, K. Kasichayanula, J. Ralph, P. Luszczek, D. Terpstra, S. Moore. “Measuring Energy and Power with PAPI”. In: 41st International Conference on Parallel Processing Workshops, pp. 262-268, 2012.
[17]D. Li, S. Byna, S. Chakradhar. “Energy-Aware Workload Consolidation on GPU”. In: 40th International Conference on Parallel Processing Workshops, pp. 389-398, 2011.
[18]Y. Zhang, Y. Hu, B. Li, L Peng. “Performance and power analysis of ATI GPU: A statistical approach”. In: 6th IEEE International Conference on Networking, Architecture and Storage (NAS), pp. 149-158, 2011.
[19]Y. Abe, H. Sasaki, S. Kato, K. Inoue, M. Edahiro, M. Peres. “Power and performance characterization and modeling of GPUaccelerated systems”. In: 28th IEEE International Parallel and Distributed Processing Symposium, pp. 113-122, 2014.
[20] J. Chen, B. Li, Y. Zhang, L. Peng, J. K. Peir. “Tree structured analysis on GPU power study”. In: 29th IEEE International Conference on Computer Design (ICCD), pp. 57-64, 2011.
[21]GCC Command Options: Options That Control Optimization, https://gcc.gnu.org/onlinedocs/gcc-4.6.2/gcc/Optimize-Options.html. Accessed on 2018-04-13.
Published
2018-10-09
How to Cite
Pi Puig, M., De Giusti, L., & Naiouf, M. (2018). Are GPUs Non-Green Computing Devices?. Journal of Computer Science and Technology, 18(02), e17. https://doi.org/10.24215/16666038.18.e17
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