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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Fax: (609) 964-1699
Administrative Assistant: Pamela DelOrefice, (609) 258-5551

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Support for High-Frequency Streaming in CMPs [abstract] (ACM DL, PDF)
Ram Rangan, Neil Vachharajani, Adam Stoler, Guilherme Ottoni, David I. August, and George Z. N. Cai
Proceedings of the 39th IEEE/ACM International Symposium on Microarchitecture (MICRO), December 2006.
Accept Rate: 24% (42/174).

As the industry moves toward larger-scale chip multiprocessors, the need to parallelize applications grows. High inter-thread communication delays, exacerbated by over-stressed high-latency memory subsystems and ever-increasing wire delays, require parallelization techniques to create partially or fully independent threads to improve performance. Unfortunately, developers and compilers alike often fail to find sufficient independent work of this kind.

Recently proposed pipelined streaming techniques have shown significant promise for both manual and automatic parallelization. These techniques have wide-scale applicability because they embrace inter-thread dependences (albeit acyclic dependences) and tolerate long-latency communication of these dependences. This paper addresses the lack of architectural support for this type of concurrency, which has blocked its adoption and hindered related language and compiler research. We observe that both manual and automatic techniques create high-frequency streaming threads, with communication occurring every 5 to 20 instructions. Even while easily tolerating inter-thread transit delays, this high-frequency communication makes thread performance very sensitive to intra-thread delays from the repeated execution of the communication operations. Using this observation, we define the design-space and evaluate several mechanisms to find a better trade-off between performance and operating system, hardware, and design costs. From this, we find a light-weight streaming-aware enhancement to conventional memory subsystems that doubles the speed of these codes and is within 2% of the best-performing, but heavy-weight, hardware solution.