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Issue No. 12 - Dec. (2012 vol. 61)
ISSN: 0018-9340
pp: 1711-1723
Ankit Sethia , University of Michigan, Ann Arbor
Ganesh Dasika , ARM, Austin
Trevor Mudge , University of Michigan, Ann Arbor
Scott Mahlke , University of Michigan, Ann Arbor
The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices have clearly changed the landscape of computing, there are two central problems that arise. First, GPUs are designed and optimized for graphics applications resulting in delivered performance that is far below peak for more general scientific and mathematical applications. Second, GPUs are power hungry devices that often consume 100-300 watts, which restricts the scalability of the solution and requires expensive cooling. To combat these challenges, this paper presents the PEPSC architecture—an architecture customized for the domain of data parallel dense matrix style scientific application where power efficiency is the central focus. PEPSC utilizes a combination of a 2D single-instruction multiple-data (SIMD) datapath, an intelligent dynamic prefetching mechanism, and a configurable SIMD control approach to increase execution efficiency over conventional GPUs. A single PEPSC core has a peak performance of 120 GFLOPs while consuming 2 W of power when executing modern scientific applications, which represents an increase in computation efficiency of more than 10X over existing GPUs.
Graphics processing unit, Benchmark testing, Computer architecture, Energy efficiency, Energy management, Low power electronics, Parallel processing, scientific computing, Low-power design, hardware, SIMD processors, processor architectures, parallel processors, Graphics Processing Unit (GPU), throughput computing

T. Mudge, G. Dasika, A. Sethia and S. Mahlke, "A Customized Processor for Energy Efficient Scientific Computing," in IEEE Transactions on Computers, vol. 61, no. , pp. 1711-1723, 2012.
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