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Matching and searching analysis for parallel hardware implementation on FPGAs
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Source International Symposium on Field Programmable Gate Arrays archive
Proceedings of the 2001 ACM/SIGDA ninth international symposium on Field programmable gate arrays table of contents
Monterey, California, United States
Pages: 125 - 133  
Year of Publication: 2001
ISBN:1-58113-341-3
Authors
Pablo Moisset  University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Suite 1001, Marina del Rey, California
Pedro Diniz  University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Suite 1001, Marina del Rey, California
Joonseok Park  University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Suite 1001, Marina del Rey, California
Sponsor
SIGDA: ACM Special Interest Group on Design Automation
Publisher
ACM  New York, NY, USA
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ABSTRACT

Matching and searching computations play an important role in the indexing of data. These computations are typically encoded in very tight loops with a single index variable and a simple search/ matching predicate. Their inherent sequential nature, either because of data dependences but more often because of very strong control dependences, makes it impossible to apply existing data dependence and parallelization analysis to exploit significant levels parallelism on traditional architectures. This paper describes a class of searching and matching computations and describes a mapping strategy to map these computations to hardware. We have developed a compiler analysis in SUIF using array data dependence analysis and implicit loop unrolling analysis to expose more parallelism for the parallel evaluation of these computations. Our compiler generates parallel hardware specifications in VHDL. The resulting parallel hardware yields significant performance improvements when these kernel operators are repeated over shifted portions of the input data on FPGA-based computing architectures.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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The Stanford SUIF Compilation System". Public Domain Software and Documentation available at http:// suif.stanford.edu.
 
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Collaborative Colleagues:
Pablo Moisset: colleagues
Pedro Diniz: colleagues
Joonseok Park: colleagues