Scheduling instructions with uncertain latencies in asynchronous architectures

  • D. K. Arvind
  • S. Sotelo-Salazar
Workshops 10+11+14: Parallel Computer Architecture and Image Processing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1300)


This paper addresses the problem of scheduling instructions in micronet-based asynchronous processors (MAP), in which the latencies of the instructions are not precisely known. A PTD scheduler is proposed which minimises true dependencies, and results are compared with two list schedulers — the Gibbons and Muchnick scheduler, and a variation of the Balanced scheduler. The PTD scheduler has a lower time complexity and produces better quality schedules than the other two when applied twenty-three loop- and control-intensive benchmark programs.


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  1. 1.
    D. K. Arvind and V. E. F. Rebello. Instruction-level parallelism in asynchronous processor architectures. Proc 3rd. International Workshop on Algorithms and Parallel VLSI Leuven, Belgium, August 1994, pp. 203–215.Google Scholar
  2. 2.
    P. B. Gibbons and S. S. Muchnick. Efficient instruction scheduling for a pipelined architecture. Proc. SIGPLAN 1986 Symposium on Compiler Construction, SIGPLAN Notices, 21(7), July 1986, pp. 11–16.Google Scholar
  3. 3.
    D. R. Kerns and S. J. Eggers. Balanced scheduling: Instruction scheduling when memory latency is uncertain. In Proc SIGPLAN 1993 Conference on Programming Language Design and Implementation, SIGPLAN Notices, 28(6), June 1993, pp. 278–289.Google Scholar
  4. 4.
    D. J. Kinniment. An evaluation of asynchronous addition. IEEE Transactions on Very Large Scale Integration (VLSI) systems, March 1996, pp. 137–140.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • D. K. Arvind
    • 1
  • S. Sotelo-Salazar
    • 1
  1. 1.Department of Computer ScienceThe University of EdinburghEdinburghScotland

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