Technology for a better society. hetcomp.com
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1 Technology for a better society hetcomp.com 1
2 J. Seland, C. Dyken, T. R. Hagen, A. R. Brodtkorb, J. Hjelmervik,E Bjønnes GPU Computing USIT Course Week 16th November 2011 hetcomp.com 2
3 9:30 10:15 Introduction to GPU Computing 10:15 10:30 Break 10:30 11:00 CUDA Intermediate Example 11:00 11:30 Design, Test and Lifecycle hetcomp.com 3
4 GPUs are everywhere Highest performing chip in all classes of computers hetcomp.com 4
5 What is GPU Computing? GPU = Graphics Processing Unit = Video Card Delivers extreme floating point performance FLOPS / $ FLOPS / Watt FLOPS /Volume Massively parallel Fine grained parallelism TOP500 Supercomputer list: Num 2, 4 and 5 use GPUs hetcomp.com 5
6 Hardware characteristics Q Workstation Hardware I7-2600K Sandy Br, Fermi GPU Geforce 580 Xeon X7560 Fermi GPU Quadro 6000 Number of cores # of float arithmetic units Clock frequency (GHz) Single precision gigaflops Double:single performance 1:2 1:8 1:2 1:2 Gigaflops per watt Gigaflops per $ Memory bandwidth (GiB/s) hetcomp.com 6
7 What happened ? Increasing frequency hits several walls: Memory Expensive to build fast memory Remedy: Caches Instruction Level Parallelism Complex to identify Power Density Relative to frequency cubed hetcomp.com 7
8 How Parallelism Can Help 100% Single Core 100% 100% The power density of microprocessors is proportional the cube of the clock frequency Multi Core 85% 100% 170 % Frequency Power Performance 30% GPU 100 % ~10x hetcomp.com 8
9 GPU Programming Models GPU Hardware Parallel Complex Changing Proprietary Device driver act as operating system Memory management Task scheduling Just-in-time compilation Various abstractions hide details Remarkably successful! hetcomp.com 10
10 Graphics GPU Programming Models OpenGL DirectX WebGL Native APIs Custom shader programs Usage: Games, visualization, CAD++ Drives GPU design Actively maintained and developed hetcomp.com 11
11 Specialized Graphics GPU Programming Models OpenGL DirectX WebGL DirectCompute Various abstractions Automatic generation of shaders SIMD Programming Model Example: PeakStream, Brook, RapidMind Mostly died out CUDA OpenCL Compute kernels written in C SIMD/SPMD Programming Model Explicit memory management Expose low-level features Very high-performance WebCL hetcomp.com 12
12 General Specialized Graphics GPU Programming Models SPMD Programming Model Automatic memory management OpenGL Examples: C++ AMP, Java APARAPI Generate code for various backends DirectX Not yet production ready WebGL DirectCompute Matrix Algebra, FFTs, Various RNG, abstractions Image proc. Reductions, sorting Some memory management CUDA OpenCL WebCL Instrument existing code with pragmas Generate code for various backends Examples: HMPP, PGI Expensive Domain Specific Libraries Generic Libraries Compiler Pragmas Lang. Constructs hetcomp.com 13
13 OpenCL vs CUDA Two APIs for directly programming GPUs Expose the same programming model (SPMD) OpenCL has a public standard OpenCL Nvidia CUDA Owner Khronos Group Nvidia Target Platform GPUs, CPUs, cell phones Nvidia GPUs Programming Model SPMD SPMD Language C C/C++ (templates, virt. funcs) Low-Level HW Access Properitary extensions Full HW Fragmentation Much Some Tools Some Mature Vendor support Apple, AMD, Nvidia, Intel++ Nvidia hetcomp.com 14
14 Approaches to GPU Programming Approach Language Description Domain Graphics API OpenGL (WebGL) DirectX C(++), Fortran,.NET, Java, Python, Perl, Ruby, Javascript What GPUs were designed for Graphics (Games, Visualization, CAD etc.) Matlab/Mathematica Matlab/Mathematica Semi -automatic Scientific OpenMP like pragmas PGI Accelerator HMPP Cray GPU libraries Dedicated languages CUDA OpenCL C/C++ / Fortran C/C++ (call from anything) C(++) dialects (call from anything) Easy porting of legacy applications Easy to integrate into existing apps, if algorithm exists Expose GPU features Hand tuned algs. Manual mem. alloc. Scientific applications Scientific, encoding/decoding Scientific hetcomp.com 15
15 Some available libraries CUFFT CUBLAS CULA CUSPARSE CUSP CURAND NPP Nvidia Perf. Primitives CUDA Video Decoder/Encoder THRUST Fast Fourier Transform Dense Linear Algebra LAPACK interface Sparse Linear Algebra Linear Algebra Graph Computations Random Number Generation Image and Signal Processing H.264/MPEG-2 video coding STL like algorithms These libraries have various licenses hetcomp.com 16
16 GPU Clusters Each node has: 1-4 GPUs 1-4 multi-core CPUs MPI-style parallelism between nodes MPI-style parallelism between GPUs MPI or thread-parallelism between CPUs hetcomp.com 18
17 SPMD Programming Model Host code, runs on CPU Memory allocation Memory transfer Scheduling of tasks Dependencies Device code, runs on GPU Kernel functions Invoked over compute grids Compute grid can be much larger than #cores Written in C/C++-like languages (CUDA/OpenCL) Separate compiler hetcomp.com 19
18 Block (0,0) GPU compute grids Thread (0,0) Thread (1,0) Thread (2,0) Thread (3,0) Compute grid Thread (0,1) Thread (1,1) Thread (2,1) Thread (3,1) Block (0,0) Block (1,0) Block (2,0) Thread (0,2) Thread (1,2) Thread (2,2) Thread (3,2) Block (0,1) Block (1,1) Block (2,1) Execution invoked by CPU over a compute grid Compute grid subdivided into a set of blocks Blocks contains a set of threads, which can access block-level shared memory All threads in the compute grid run the same program But with individual data and individual code flow hetcomp.com 20
19 GPU Architecture hetcomp.com 21
20 System overview GPUs are on the PCIe bus GPUs have their own memory Some recent chips have embedded GPUs Multi-GPU system are common GPU RAM PCIe CPU RAM GPU RAM HDD USB hetcomp.com 22
21 GPU Architecture NVIDIA Fermi Multi Processor Execution Unit Core Scheduler Dispatch Register File L1 Cache hetcomp.com 23
22 Fermi Architecture Streaming Multiprocessor 32 cores per SM 64 KB shared memory and L1 cache Special Function Unit Double precision at half speed Concurrent kernel execution ECC Support hetcomp.com 24
23 Block (0,0) Recap Compute Grids Thread (0,0) Thread (1,0) Thread (2,0) Thread (3,0) Compute grid Thread (0,1) Thread (1,1) Thread (2,1) Thread (3,1) Block (0,0) Block (1,0) Block (2,0) Thread (0,2) Thread (1,2) Thread (2,2) Thread (3,2) Block (0,1) Block (1,1) Block (2,1) Execution invoked by CPU over a compute grid Compute grid subdivided into a set of blocks Blocks contains a set of threads, which can access block-level shared memory All threads in the compute grid run the same program But with individual data and individual code flow hetcomp.com 25
24 Challenges in CUDA/OpenCL Programming Hard to learn In our experience: 1/2 year to master if motivated Do you really care that much about performance after all? Hardware fragmentation Makes the build process more complex Driver/compiler version issues Low level of reusability of code Many different optimization strategies possible Memory access in particular hetcomp.com 26
25 Conclusion hetcomp.com 27
26 Conclusion GPU computing is here now (So is multi-core computing) Widely deployed on supercomputers (2 nd, 4 th and 5 th on TOP500) Easy to get started Libraries can be called from existing application Difficult to reach peak performance Requires intimate HW knowledge Easy to get some speedup Hard to reach optimum performance hetcomp.com 28
27 Overview of resources nvidia.com/cuda Programming guide Tutorials Forums khronos.org/opencl/ gpgpu.org Links to papers/libraries hetcomp.com 29
28 Questions? hetcomp.com 30
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