1. "Distributed Computing and Grid-technologies in Science and Education " PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov Georgy Dubna, 2012

2. AGENDA Grid & GPU GPU architecture CUDA technologies Grid-projects with GPU using Monotonic Basin Hopping method CUDA-realization of MBH Further investigations plan Summary

3. Grid & GPU PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 GPU advantages: ~33% of all PCs are equipped with modern GPU (~60% - Nvidia) Common usage of GPU resources <5% (HD film) GPU optimized for working with huge textures arrays Modern GPUs consist of tens or even hundreds cores. It means great performance for some kinds of tasks Problems, solving by Grid: effective using of existing resources working with huge data arrays providing high performance

4. GPU architecture scalable array of ТРС with it’s own DRAM PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 8 Scalar Processors 2 Special Functions Units Double Precision Unit Register File Shared Memory Texture Memory Cache Constant Memory Cache

5. CUDA technology CUDA – Compute Unified Device Architecture Supports all NVidia GPUs starting from GeForce 8-x series Low level access to the hardware - graphics API knowledge not required CUDA programming language is based on C/C++ syntax – easier porting of existing code Greater performance comparing to OpenCL (50-100% performance increase in different researches) PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012

6. CUDA technology PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 CUDA programming model

7. CUDA technology PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 CUDA threads hierarchy Threads groups in Blocks (1, 2 or 3-dim) Blocks groups in Grid (1 or 2-dim) Treads within Block: Sharing data through shared memory Synchronizing their execution Threads from different blocks operate independently Built variables threadIdx, blockIdx etc.

8. CUDA technology Memory typeAccessLevelSpeed RegistersR/WPer-threadHigh (on chip) LocalR/WPer-threadLow (DRAM) SharedR/WPer-blockHigh (on chip) GlobalR/WPer-gridLow (DRAM) ConstantR/OPer-gridHigh (L1 cache) TextureR/OPer-gridHigh (L1 cache) PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 CUDA memory hierarchy

9. Grid-projects with GPU using GPUgrid.net - volunteer distributed computing project for biomedical research from the Universitat Pompeu Fabra in Barcelona (Spain) Collatz Conjecture - research in mathematics, specifically testing the Collatz Conjecture also known as 3x+1 or HOTPO (half or triple plus one). PrimeGrid - to bring the excitement of prime finding PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012

10. Monotonic Basin Hopping method PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 1. Start from point x 0 2. Repeat until the stop condition: 2.1. generate point Φ(x) 2.2. apply the local minimization algorithm to the point Φ (x) → get point x 1. 2.3. if f (x 1 ) < f (x), then x = x 1 3. Return x Algorithm steps: * Gradient descent was used as local minimization algorithm

11. CUDA-realization of MBH PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 Ymin Ymax I, j XminXmax Divide the research area into equal square areas Each thread implements the algorithm in it’s area Find minimum among the results of each thread

12. CUDA-realization of MBH PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 GPU1 - Tesla 10: max threads per block = 512 max threads per dim = 512 max blocks per dim = 65535 number of multiproc = 30 GPU2 - GeForce GT 525M: max threads per block = 1024 max threads per dim = 1024 max blocks per dim = 65535 number of multiproc = 2 CPU - Intel core2duo T6400 number of cores = 2 Clock speed = 2 GHz Used hardware:

13. CUDA-realization of MBH PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 Four parameters: the radius of the “jump” of the algorithm MBH - r, the maximum number of steps in the cycle - N, the number of blocks launched - Nb and the number of threads per block - Nt Set Nb and Nt The radius r is calculated as half of a square area diametr The number of cycle’s steps N is determined a result of the experiment * 4 test functions were selected: Ackley, Griewank, Rastrigin, Shubert Methodology of the experiment 1.The result is considered valid if it differs from the tabular less than 0.001 2.The result is considered valid if an average of 9 times out of 10 gives the right within the specified accuracy of the answer 3.The time averaged over 20 runs of the program

14. CUDA-realization of MBH PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 AVG executing time CPU160 sec GeForce GT 525M35 sec Tesla 101.5 sec Results for Ackley function Number of treads per block block blocks block blocks Minimal time of finding extremum, sec

15. CUDA-realization of MBH PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 Results for Griewank function AVG executing time CPU155 sec GeForce GT 525M33 sec Tesla 102.2 sec Number of treads per block block blocks block blocks Minimal time of finding extremum, sec

16. CUDA-realization of MBH PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 Results for Rastrigin function AVG executing time CPU125 sec GeForce GT 525M28.5 sec Tesla 102.0 sec Number of treads per block block blocks block blocks Minimal time of finding extremum, sec

17. CUDA-realization of MBH PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012 Results for Shubert function AVG executing time CPU300 sec GeForce GT 525M82 sec Tesla 104.3 sec block blocks Number of treads per block block blocks Minimal time of finding extremum, sec

18. Further investigations plan Use more complicated and accurate local optimization methods Uprgrade method of parallization Improve algorithm of MBH “jump” set-up Build solution for Molecular cluster modeling based on MBH method Integrate CUDA-solution to BNB-Grid project Describe class of functions that can be effectively processed on GPUs PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012

19. Summary There are huge share of GPUs among PCs GPU is a multicore system CUDA is one of the technologies that provides great performance of GPU calculations There are a number of Grid-projects that already use CUDA Tests shows that in some cases GPU perform 5-100 times better than CPU PROSPECTS OF USING GPU IN DESKTOP-GRID SYSTEMS Klimov G., CMC MSU 2012

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