Abstract
This paper introduces a novel approach for exploring heterogeneous computing engines which include GPUs and FPGAs as accelerators. Our goal is to systematically automate finding solutions for such engines that maximize energy efficiency while meeting requirements in throughput and in resource constraints. The proposed approach, based on a linear programming model, enables optimization of system throughput and energy efficiency, and analysis of energy efficiency sensitivity and power consumption issues. It can be used in evaluating current and future computing hardware and interfaces to identify appropriate combinations. A heterogeneous system containing a CPU, a GPU and an FPGA with a PCI Express interface is studied based on the High Performance Linpack application. Results indicate that such a heterogeneous computing system is able to provide energy-efficient solutions to scientific computing with various performance demands. The improvement of system energy efficiency is more sensitive to some of the system components, for example in the studied system concurrently improving the energy efficiency of the interface and the GPU by 10 times could lead to over 10 times improvement of the system energy efficiency.
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References
Eijkhout, V., et al.: Introduction to high-performance scientific computing (May 2011), http://www.tacc.utexas.edu/eijkhout/istc/istc.html
Feng, W.-C.: The importance of being low power in high performance computing. Cyberinfrastructure Technology Watch Quarterly 1 (2005)
Ding, Y., et al.: Towards energy efficient scaling of scientific codes. In: IPDPS, pp. 1–8 (April 2008)
Wang, G., Ren, X.: Power-efficient work distribution method for CPU-GPU heterogeneous system. In: ISPA, pp. 122–129 (September 2010)
Turkington, K., et al.: FPGA based acceleration of the linpack benchmark: A high level code transformation approach. In: FPL, pp. 1–6 (August 2006)
Fatica, M.: Accelerating linpack with CUDA on heterogenous clusters. In: GPGPU-2, pp. 46–51 (March 2009)
Ogata, Y., et al.: An efficient, model-based CPU-GPU heterogeneous FFT library, pp. 1–10 (April 2008)
Tse, A., et al.: Dynamic scheduling Monte-Carlo framework for multi-accelerator heterogeneous clusters. In: FPT, pp. 233–240 (December 2010)
Barak, A., et al.: A package for openCL based heterogeneous computing on clusters with many GPU devices. In: Int. Conf. on Cluster Computing Workshops and Posters, pp. 1–7 (September 2010)
Liu, Q., Luk, W.: Objective-driven workload allocation in heterogeneous computing systems. In: FPT (December 2011)
Liu, Q., et al.: Combining optimizations in automated low power design. In: DATE, pp. 1791–1796 (2010)
Hong, S., Kim, H.: An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness. In: ISCA, pp. 152–163 (2009)
Liu, Q., et al.: Optimising designs by combining model-based and pattern-based transformations. In: FPL, pp. 308–313 (2009)
Petitet, A., et al.: HPL - a portable implementation of the high-performance linpack benchmark for distributed-memory computers, version 2.0, http://www.netlib.org/benchmark/hpl/
Adm-xrc-5t2 data sheet, http://www.alpha-data.com/pdfs/adm-xrc-5t2.pdf
The green 500, http://www.green500.org/
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Liu, Q., Luk, W. (2012). Heterogeneous Systems for Energy Efficient Scientific Computing. In: Choy, O.C.S., Cheung, R.C.C., Athanas, P., Sano, K. (eds) Reconfigurable Computing: Architectures, Tools and Applications. ARC 2012. Lecture Notes in Computer Science, vol 7199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28365-9_6
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DOI: https://doi.org/10.1007/978-3-642-28365-9_6
Publisher Name: Springer, Berlin, Heidelberg
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