David I. August
Professor in the Department of Computer Science, Princeton University
Affiliated with the Department of Electrical Engineering, Princeton University
Ph.D. May 2000, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

Office: Computer Science Building Room 221
Email: august@princeton.edu
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Administrative Assistant: Pamela DelOrefice, (609) 258-5551

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Chip Multi-Processor Scalability for Single-Threaded Applications [abstract] (ACM DL, PDF)
Neil Vachharajani, Matthew Iyer, Chinmay Ashok, Manish Vachharajani, David I. August, and Daniel A. Connors
Proceedings of the 2005 Workshop on Design, Architecture and Simulation of Chip Multi-Processors (dasCMP), November 2005.

The exponential increase in uniprocessor performance has begun to slow. Designers have been unable to scale performance while managing thermal, power, and electrical effects. Furthermore, design complexity limits the size of monolithic processors that can be designed while keeping costs reasonable. Industry has responded by moving toward chip multi-processor architectures (CMP). These architectures are composed from replicated processors utilizing the die area afforded by newer design processes. While this approach mitigates the issues with design complexity, power, and electrical effects, it does nothing to directly improve the performance of contemporary or future single-threaded applications.

This paper examines the scalability potential for exploiting the parallelism in single-threaded applications on these CMP platforms. The paper explores the total available parallelism in unmodified sequential applications and then examines the viability of exploiting this parallelism on CMP machines. Using the results from this analysis, the paper forecasts that CMPs, using the "intrinsic" parallelism in a program, can sustain the performance improvement users have come to expect from new processors for only 6-8 years provided many successful parallelization efforts emerge. Given this outlook, the paper advocates exploring methodologies which achieve parallelism beyond this "intrinsic" limit of programs.