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Synthesis of reconfigurable high-performance multicore systems
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International Symposium on Field Programmable Gate Arrays archive
Proceeding of the ACM/SIGDA international symposium on Field programmable gate arrays table of contents
Monterey, California, USA
SESSION: High level synthesis table of contents
Pages 201-208  
Year of Publication: 2009
ISBN:978-1-60558-410-2
Authors
Jason Cong  University of California, Los Angeles, Los Angeles, CA, USA
Karthik Gururaj  University of California, Los Angeles, Los Angeles, CA, USA
Guoling Han  University of California, Los Angeles, Los Angeles, CA, USA
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Reconfigurable high-performance computing systems (RHPC) have been attracting more and more attention over the past few years. RHPC systems are a promising solution for accelerating system performance, lowering power consumption and minimizing operation cost. In order to achieve high performance on this hybrid system, it is important to effectively explore the design space, which includes accelerator synthesis, resource allocation and job scheduling. In this paper we propose novel algorithms for reconfigurable resource allocation and job scheduling to optimize performance of multicore RHPC systems. Specifically, we first propose an interesting approximation algorithm to assign jobs to processors with consideration of coprocessors at the global optimization step. Then we present an optimal solution for coprocessor selection in the local optimization step. In this paper we also demonstrate that designers can quickly explore a large number of accelerator design choices with the help of high-level synthesis tools. Experiments show that our proposed techniques provide efficient solutions for real-life benchmarks and generate higher quality of results. When compared to other heuristic algorithms, our results can achieve up to 47% performance improvement.


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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Collaborative Colleagues:
Jason Cong: colleagues
Karthik Gururaj: colleagues
Guoling Han: colleagues