Mathematical computations with GPUs
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1 Master Educational Program Information technology in applications Mathematical computations with GPUs Introduction Alexey A. Romanenko Novosibirsk State University
2 How to.. Process terabytes of seismic data on desktop PC. Estimate tsunami impact faster then wave reaches dry earth. Rasterize 3D scenes in real-time.
3 Topics of the course architecture of a GPU and its difference from generalpurpose CPU; general notions related to CUDA; contents and functionality of GPU-optimized libraries; area of applicability of GPUs for solving scientific and applied problems; general principles of optimizing programs for GPUs; methods of debugging and profiling programs running on GPUs; tools and instruments for developing, debugging and profiling programs running on GPUs.
4 Performance Computer power «Titan» Opteron Cores k20 NVIDIA GPU 20+ Pflops «K computer» x 8-core SPARC64 VIIIfx. 8,162 Pflops 280 Tflops 212,992 CPUs Time
5 Growth of performance Growth of clock rate Number of cores/cpus CPU architecture complexity Number of registers Pipe-line Digital capacity (4bit CPU, 16, 32, 64) etc
6 Computer graphics
7 Computer graphics Small vectors Small matrixes filters/post-processing Calculation of projections etc. Huge mumber of identical small operations.
8 Performance of video cards
9 March 2013 Kepler: 28nm, 1.4 Tflops DP Maxwell: 22nm, 4 Tflops DP
10 Memory bandwidth
11 Heterogeneous systems + 3 of 5 leaders in top500 list!
12 Heterogeneous vs. Homogeneous computing systems ( ) Tianhe-1A (2 place) Xeon X5670 6C 2.93 GHz, NVIDIA 2050 Jaguar (3 place) Opteron 6-core 2.6 GHz 4,7 Pflops (peak) 4040 kw 2,3 Pflops (peak) 6950 kw
13 Toward exaflop Direct scaling Tianhe-1A 4,7 Pflops (peak) * 213 = 1001,1 Pflops 4040 kw * 213 = 860 MW Jaguar 2,3 Pflops (peak) * 435 = 1000,5 Pflops 6950 kw * 435 = 3023 MW exa peta tera giga 10 9 mega 10 6 kilo 10 3
14 Sayano Shushenskaya Dam Turbines Installed capacity Maximum capacity Annual generation MW (initial) MW (current) 5,120 MW (current) 6,400 MW 23.5 TW
15 GFLOPs per Watt Green500 (November 2013) List of most power effective supercomputer Ten of top have NVIDIA GPUs. Two 6 month ago. Performance / Watt 4,5 now 0,3 November, 2007 Reaching exascale means boosting speeds by times, while keeping power relatively static.
16 Application speedup Example Applications URL Application Speedup Seismic Database 66x to 100x Mobile Phone Antenna Simulation Molecular Dynamics 45x 21x to 100x Neuron Simulation 100x MRI processing 245x to 415x Atmospheric Cloud Simulation tml 50x
17 Approaches to GPU programming Application Optimized libraries Compiler directives Programming languages (С/С++/FORTRAN) Fast development Maximum performance
18 CUDA Roadmap years! CUDA CUDA CUDA CUDA CUDA CUDA CUDA
19 CUDA vs. OpenCL CUDA NVIDIA GPU (Cray, HP, IBM, T-Platforms, NextIO ) Close to peak performance Functionality Comfort for developers (debugger, performance analyzer, etc.) Support Teaching materials, libraries OpenCL Architecture is not fixed, universality Performance is not high priority
20 CUDA vs. OpenCL performance CUDA applications have up to 30% higher performance than OpenCL application. er=
21 Useful links
22 Questions
Mathematical computations with GPUs
Master Educational Program Information technology in applications Mathematical computations with GPUs GPU architecture Alexey A. Romanenko arom@ccfit.nsu.ru Novosibirsk State University GPU Graphical Processing
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