Analysis of photonic networks for a chip multiprocessor using scientific applications

Authors:
Gilbert Hendry;Shoaib Kamil;Aleksandr Biberman;Johnnie Chan;Benjamin G. Lee;Marghoob Mohiyuddin;Ankit Jain;Keren Bergman;Luca P. Carloni;John Kubiatowicz;Leonid Oliker;John Shalf
Affiliations:
Lightwave Research Laboratory, Columbia University, New York, 10027, USA;Computer Science Department, University of California, Berkeley, 94720, USA;Lightwave Research Laboratory, Columbia University, New York, 10027, USA;Lightwave Research Laboratory, Columbia University, New York, 10027, USA;Lightwave Research Laboratory, Columbia University, New York, 10027, USA;Computer Science Department, University of California, Berkeley, 94720, USA;Computer Science Department, University of California, Berkeley, 94720, USA;Lightwave Research Laboratory, Columbia University, New York, 10027, USA;Computer Science Department, Columbia University, New York, 10027, USA;Computer Science Department, University of California, Berkeley, 94720, USA;CRD/NERSC, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;CRD/NERSC, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Venue:
NOCS '09 Proceedings of the 2009 3rd ACM/IEEE International Symposium on Networks-on-Chip
Year:
2009

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Abstract

As multiprocessors scale to unprecedented numbers of cores in order to sustain performance growth, it is vital that these gains are not nullified by high energy consumption from inter-core communication. With recent advances in 3D Integration CMOS technology, the possibility for realizing hybrid photonic-electronic networks-on-chip warrants investigating real application traces on functionally comparable photonic and electronic network designs. We present a comparative analysis using both synthetic benchmarks as well as real applications, run through detailed cycle accurate models implemented under the OMNeT++ discrete event simulation environment. Results show that when utilizing standard process-to-processor mapping methods, this hybrid network can achieve 75脳 improvement in energy efficiency for synthetic benchmarks and up to 37脳 improvement for real scientific applications, defined as network performance per energy spent, over an electronic mesh for large messages across a variety of communication patterns.