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Parallel Matlab: RTExpress on 64-bit SGI Altix with SCSL and MPT

Parallel Matlab: RTExpress on 64-bit SGI Altix with SCSL and MPT. Cosmo Castellano castellano@sensors.com Integrated Sensors, Inc 502 Court Street Suite 210 Utica, NY 13502 (315) 798-1377 www.sensors.com. Matlab FFT Benchmark Test on Shared Memory SGI.

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Parallel Matlab: RTExpress on 64-bit SGI Altix with SCSL and MPT

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  1. Parallel Matlab:RTExpress on 64-bit SGI Altixwith SCSL and MPT Cosmo Castellano castellano@sensors.com Integrated Sensors, Inc 502 Court Street Suite 210 Utica, NY 13502 (315) 798-1377 www.sensors.com

  2. Matlab FFT Benchmark Test on Shared Memory SGI • A Matlab script performs the 2D complex FFT matrix = ones(fftsize, fftsize) + j * ones(fftsize, fftsize)loop store time t1 a = fft(init_matrix) store time t2 a = a’ store time t3 a = fft(a) store time t4end loop • RTExpress is used to compile and run the MATLAB script on the 64-bit parallel computer on varying numbers of processors in data-parallel • Elapsed times are computed, averaging time over several iterations • First iteration is not counted fft1 elapsed time = t2 - t1 ctm elapsed time = t3 - t2 fft2 elapsed time = t4 - t3

  3. Scaling: 64-Processor Altix Cornerturn Scaling is excellent up to 50 processors at 8K FFT (complex/single precision) 64p 1.7GHz/9MB cache Altix 3700 (note: production systems are 1.6GHz)

  4. SGI Shared Memory Improves Cornerturn and 2D FFT Altix350 1.4 Ghz/3MB L3 cache

  5. Timing Information NOTES • Please note that all timing information gathered is not intended to provide a recommendation for any particular hardware, but to illustrate parallel operation with various combinations of processors and interconnect systems • Equipment used in the following tests may no longer be the hardware vendor’s current offering • “Improvement,” or speed-up, as compared to first-processor performance used to examine scaling rather than absolute timing • Maximum RTExpress performance may be gained by fully using vector operations in MATLAB rather than using sequential loops Parallel performance is extremely dependent on a specific application and implementation

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