Towards Energy Efficient Parallel Computing on Consumer Electronic Devices
In the last two decades supercomputers have sustained a remarkable growth in performance that even out-performed the predictions of Moore’s law, primarily due to increased levels of parallelism . As industry and academia try to come up with viable approaches for exascale systems, attention turns to energy efficiency as the primary design consideration. At the same time, energy efficiency has always been the main concern in the mobile computing area. Additionally, mobile and consumer electronic devices are becoming ever more powerful as the use cases (e.g., Web 2.0 applications, video encoding, virtual and augmented reality) become more computationally demanding. It is therefore an interesting question to ask if these devices are the possible building blocks of future HPC systems. It was the workstation and server market in the past that provided the CPUs that power supercomputers and it might be the consumer electronic market that provides the underlying technology in the future.
In this paper we try to analyze the current state of energy efficient parallel and distributed computing on mobile and consumer electronic devices. We provide an overview of performance characteristics of some current and announced future devices for scientific computation and we build a small proof-of-concept cluster from Apple’s second generation “Apple TV” devices and evaluate its performance on standard benchmark applications. We discuss the limiting factors, and analyze the industry trajectory that we believe could make consumer electronic-based design a feasible technology basis for future HPC system designs.
KeywordsAugmented Reality Double Precision Cache Size Consumer Electronic Device Advance Risc Machine
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- 1.ARM company profile, http://www.arm.com/about/company-profile/index.php
- 2.The coming war: ARM versus x86, http://vanshardware.com/2010/08/mirror-the-coming-war-arm-versus-x86/
- 4.Marvell Armada XP multicore series, http://www.marvell.com/products/processors/embedded/armada_xp/
- 5.STREAM: Sustainable memory bandwidth in high performance computers, http://www.cs.virginia.edu/stream/
- 7.Adiga, N. R., et al.: An overview of the BlueGene/ L supercomputer (2002)Google Scholar
- 10.Buttari, A., Luszczek, P., Kurzak, J., Dongarra, J., Bosilca, G.: SCOP3: A rough guide to scientific computing on the PlayStation 3. version 0.1. Technical Report UT-CS-07-595, Innovative Computing Laboratory, University of Tennessee Knoxville (April 2007)Google Scholar
- 11.Calxeda 5 watt ARM server, http://insidehpc.com/2011/03/14/calxeda-boasts-of-5-watt-arm-server-node/
- 12.The Green500 List, http://www.green500.org
- 13.Kogge, P.M., et al.: Exascale computing study: Technology challenges in achieving exascale systems. DARPA Information Processing Techniques Office (IPTO) Sponsored Study (2008)Google Scholar
- 14.Neill, R., Shabarshin, A., Carloni, L.P.: A heterogeneous parallel system running OpenMPI on a broadband network of embedded set-top devices. In: Proceedings of the 7th ACM International Conference on Computing Frontiers, CF 2010, pp. 187–196. ACM, New York (2010)Google Scholar
- 15.NVIDIA. The benefits of multiple CPU cores in mobile devices, whitepaper (2010), http://goo.gl/g3MXo
- 16.OSU Micro-Benchmarks, http://mvapich.cse.ohio-state.edu/benchmarks
- 17.PS3 cluster at NCSU, http://moss.csc.ncsu.edu/~mueller/cluster/ps3/
- 18.Katie, R.-H., Hedge, P.: ARM Cortex-A8 vs. Intel Atom: Architectural and benchmark comparisons (2009)Google Scholar
- 19.The Top 500 Supercomputer Sites, http://www.top500.org
- 20.Unmodified Xbox Cluster, http://www.bgfax.com/xbox/home.html