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
Phone: (609) 258-2085
Fax: (609) 964-1699
Administrative Assistant: Pamela DelOrefice, (609) 258-5551

Front Page Publication List (with stats) Curriculum Vitae (PDF) The Liberty Research Group


Parallel-Stage Decoupled Software Pipelining [abstract] (ACM DL, PDF)
Easwaran Raman, Guilherme Ottoni, Arun Raman, Matthew Bridges, and David I. August
Proceedings of the 2008 International Symposium on Code Generation and Optimization (CGO), April 2008.
Accept Rate: 31% (21/66).

In recent years, the microprocessor industry has embraced chip multiprocessors (CMPs), also known as multi-core architectures, as the dominant design paradigm. For existing and new applications to make effective use of CMPs, it is desirable that compilers automatically extract thread-level parallelism from single-threaded applications. DOALL is a popular automatic technique for loop-level parallelization employed successfully in the domains of scientific and numeric computing. While the scalability of DOALL is only limited by the number of iterations of the loop, its applicability is limited by the presence of loop-carried dependences. A parallelization technique with greater applicability is decoupled software pipelining(DSWP), which parallelizes loops even in the presence of loop-carried dependences. However, the scalability of DSWP is limited by the size of the loop body and the number of recurrences it contains. This work proposes a novel non-speculative compiler parallelization technique called parallel-stage decoupled software pipelining (PS-DSWP). The goal of PS-DSWP is to combine the applicability of DSWP with the scalability of DOALL parallelization. A key insight of PS-DSWP is that, after isolating the recurrences in their own stages in DSWP, portions of the loop suitable for DOALL parallelization may be exposed. PS-DSWP extends DSWP to benefit from these opportunities, utilizing multiple threads to execute the same stage of a DSWPed loop in parallel. This paper describes the PS-DSWP transformation in detail and discusses its implementation in a research compiler. PS-DSWP produces an average speedup of 114% (up to a maximum of 155%) with 6 threads on loops from a set of 5 applications. Our experiments also demonstrate that PS-DSWP achieves better scalability with the number of threads than DSWP.