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Accelerating a climate physics model with OpenCL

Accelerating a climate physics model with OpenCL . CMSC 601 Spring 11 – Research Skills Dibyajyoti Ghosh. What is climate physics model?. Global weather is controlled by many interconnected events. Includes changes in atmosphere and oceans, ebb and flow of sea ice etc.

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Accelerating a climate physics model with OpenCL

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  1. Accelerating a climate physics model with OpenCL CMSC 601 Spring 11 – Research Skills Dibyajyoti Ghosh

  2. What is climate physics model? • Global weather is controlled by many interconnected events. Includes changes in atmosphere and oceans, ebb and flow of sea ice etc. • World’s most powerful super computers can simulate these events. • CCSM-2model simulate Earth’s climate patterns in considerable detail through700 billion calculations to recreate a single day of the world’s climate. • Scientists use these data to understand ocean currents, predict weather patterns, study O3 layer among others. http://www.ucar.edu/communications/CCSM/overview.html

  3. Background • Solar radiation component of NASA’s GEOS-v5 takes ~20% of model computation time. • NASA interested in analysis of performance and cost benefit using non traditional computing systems. • GEOS-v5 - 20+ old, written in Fortran (mostly), still evolving. • Cannot be entirely rewritten due to production constraints. http://www.ucar.edu/communications/CCSM/overview.html

  4. Related Work • Accelerating Climate Models with the IBM Cell Processor – Shujia Zhou et al, 2008 • GPU Computing for Atmospheric Modeling - Kelly, Rory  NCAR, Boulder, July-Aug. 2010   • Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies - Linford, John Christian , Virginia Tech, 2010 • Exploiting Array Syntax in Fortran for Accelerator Programming - Matthew J. Sottile, Craig E Rasmussen, Wayne N. Weseloh, Robert W. Robey, Los Alamos National Laboratory

  5. Motivation OpenCL - created with goal of unifying hybrid systems. No literature on OpenCL portability among architectures. No data on how OpenCL fares against GCC in vectorization. http://www.cc.gatech.edu/~bader/AFRL-GT-Workshop2009/AFRL-GT-Bader.pdf

  6. 0 1 2 3 VR1 VR2 VR3 VR4 VR5 a b c d What is vectorization? • original serial loop:for(i=0; i<N; i++){a[i] = a[i] + b[i];} • loop in vector notation:for (i=0; i<N; i+=VF) { a[i:i+VF-1] = a[i:i+VF-1] + b[i:i+VF-1];} VF = 4 OP(a) OP(b) OP(c) OP(d) VOP( a, b, c, d ) VR1 Vector operation vectorization Vector Registers • Data elements packed into vectors • Vector length  Vectorization Factor (VF) Data in Memory: a b c d e f g h i j k l m n o p 6 Thanks to Dorit Nuzman, IBM www.hipeac.net/system/files/4_Nuzman.ppt for this wonderful slide

  7. OpenCL trivia • A framework for heterogeneous computing resources developed by Apple Inc. now supported by all major vendors. • A subset of C language with additional features to facilitate parallel processing. http://www.khronos.org/opencl/

  8. How data ||-ism works on OpenCL? • Kernel is the code for a work item that is executed on a device (CPU or GPU or others). • Imagine a NxN grid with one kernel invocation per grid.

  9. Our Approach Used code from the production version of the NASA GEOS-v5 climate model. • Step #1 – Identify computation intensive sections from the weather model. • Step #2 – Port these sections to OpenCL on IBM Cell B.E. and then to Mac OSX to test on Intel CPU. • Step #3 – Analyze performance and reason the performance.

  10. Findings - I Speedup on Mac OSX with OpenCL Speedup on IBM Cell B.E. with OpenCL Serial VS parallel speedup of a code section analyzed on Mac OSX

  11. Findings - II 1. Speedup achieved ~40x on both IBM and Intel CPUs. 2. Code NOT portable among architectures, sections of code not functioningdue to incomplete OpenCL implementation on Mac OSX Intel based architecture. 3. GCC vectorization fails in certain cases compared to OpenCL. We attempted compilation of serial code with gcc -O2 -ftree-vectorize flag.

  12. Road Ahead • Making appropriate changes to the solar radiation code for Mac OS X Intel CPU based architecture. Remember some parts of the code base is non-functional on Intel CPUs. • Modify the OpenCL code to run on GPUs and understand if performance is portable, in addition to code.

  13. Summary • OpenCL’s attempt towards portability in high performance computing is still a long road ahead. • GCC vectorization fails against OpenCL.

  14. Acknowledgements • Dr. Shujia Zhou, MC2 Lab • FahadZafar, MC2 Lab • Center for Hybrid Multicore Productivity Research, UMBC • CMSC 601 folks

  15. http://www.asianjobportal.com/wp-content/uploads/2010/11/25_questions_interview.jpghttp://www.asianjobportal.com/wp-content/uploads/2010/11/25_questions_interview.jpg

  16. Vectorization Analysis - I A part of the serial code with gcc vectorization error output

  17. Vectorization Analysis - II A part of the OpenCL code with vectorized instruction set for the loop-construct in the last slide

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