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Abstract
As power densities increase and mobile applications become pervasive, power-aware microprocessor design has become a critical issue. We present HLSpower, a unique tool for power-aware design space exploration of superscalar processors. HLSpower is based upon HLS [OCF00], a tool which used a novel blend of statistical modeling and symbolic execution to accelerate performance modeling more than 100-1000X over conventional cycle-based simulators.
In this paper, we extend the HLSmetho dology to model energy efficiency of superscalars. We validate our results against the Wattch [BTM00] cycle-based power simulator. While minor second order power effects continue to require detailed cycle-by-cycle simulation, HLSpower is useful for large-scale exploration of the significant power-performance design space. For example, we can show that the instruction cache hit rate and pipeline depth interact with power efficiency in a non-trivial way as they are varied over significant ranges. In particular, we note that, while the IPC of a superscalar increases monotonically with both optimizations, the energy efficiency does not. We highlight the design capabilities by focusing on these non-monotonic contour graphs to demonstrate how HLSpower can help build intuition in power-aware design.
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Authors and Affiliations
University of California at Davis, USA
Ravishankar Rao & Frederic T. Chong
University of Washington, USA
Mark H. Oskin
- Ravishankar Rao
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- Frederic T. Chong
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Editors and Affiliations
CISE Department, University of Florida, 32611, FL, Gainesville, USA
Sartaj Sahni
Department of Electrical Engineering, EEB 200C, University of Southern California, 3740 McClintok Ave., 90089-2562, CA, Los Angeles, USA
Viktor K. Prasanna
India Software Lab, IBM Global Services India Exports, Golden Enclave, TISL Tower, Airport Road, 560 017, Bangalore, India
Uday Shukla
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Rao, R., Oskin, M.H., Chong, F.T. (2002). HLSpower: Hybrid Statistical Modeling of the Superscalar Power-Performance Design Space. In: Sahni, S., Prasanna, V.K., Shukla, U. (eds) High Performance Computing — HiPC 2002. HiPC 2002. Lecture Notes in Computer Science, vol 2552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36265-7_58
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