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A spatial path scheduling algorithm for EDGE architectures
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Source Architectural Support for Programming Languages and Operating Systems archive
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems table of contents
San Jose, California, USA
SESSION: Scheduling and spatial programming table of contents
Pages: 129 - 140  
Year of Publication: 2006
ISBN:1-59593-451-0
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Authors
Katherine E. Coons  University of Texas at Austin
Xia Chen  University of Texas at Austin
Doug Burger  University of Texas at Austin
Kathryn S. McKinley  University of Texas at Austin
Sundeep K. Kushwaha  Qualcomm
Sponsors
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGOPS: ACM Special Interest Group on Operating Systems
Publisher
ACM  New York, NY, USA
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ABSTRACT

Growing on-chip wire delays are motivating architectural features that expose on-chip communication to the compiler. EDGE architectures are one example of communication-exposed microarchitectures in which the compiler forms dataflow graphs that specify how the microarchitecture maps instructions onto a distributed execution substrate. This paper describes a compiler scheduling algorithm called spatial path scheduling that factors in previously fixed locations - called anchor points - for each placement. This algorithm extends easily to different spatial topologies. We augment this basic algorithm with three heuristics: (1) local and global ALU and network link contention modeling, (2) global critical path estimates, and (3) dependence chain path reservation. We use simulated annealing to explore possible performance improvements and to motivate the augmented heuristics and their weighting functions. We show that the spatial path scheduling algorithm augmented with these three heuristics achieves a 21% average performance improvement over the best prior algorithm and comes within an average of 5% of the annealed performance for our benchmarks.


REFERENCES

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Collaborative Colleagues:
Katherine E. Coons: colleagues
Xia Chen: colleagues
Doug Burger: colleagues
Kathryn S. McKinley: colleagues
Sundeep K. Kushwaha: colleagues