Python и программирование GPU (Ивашкевич Глеб)


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Ивашкевич Глеб - HPC software developer / Gero / Украина, Харьков

Графические процессоры становятся частью стандартного инструментария в высокопроизводительных вычислениях. Одновременно появляются новые и совершенствуются уже существующие программные средства. Мы поговорим об архитектуре графических процессоров Nvidia и о том, как с ними работать из Python.

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Python и программирование GPU (Ивашкевич Глеб)

  1. 1. Python and GPU Computing Glib Ivashkevych HPC software developer, GERO Lab
  2. 2. Parallel revolution The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software Herb Sutter, March 2005 When serial code hits the wall. Power wall. Now, Intel is embarked on a course already adopted by some of its major rivals: obtaining more computing power by stamping multiple processors on a single chip rather than straining to increase the speed of a single processor. Paul S. Otellini, Intel's CEO May 2004
  3. 3. July 2006 Feb 2007 Nov 2008 Intel launches Core 2 Duo (Conroe) Nvidia releases CUDA SDK Tsubame, first GPU accelerated supercomputer Dec 2008 OpenCL 1.0 specification released Today >50 GPU powered supercomputers in Top500, 9 in Top50
  4. 4. It's very clear, that we are close to the tipping point. If we're not at a tipping point, we're racing at it. Jen-Hsun Huang, NVIDIA Co-founder and CEO March 2013 Heterogeneous computing becomes a standard in HPC and programming has changed
  5. 5. Heterogeneous computing CPU main memory GPU cores GPU memory multiprocessors Host Device
  6. 6. CPU GPU general purpose sophisticated design and scheduling perfect for task parallelism highly parallel huge memory bandwidth lightweight scheduling perfect for data parallelism
  7. 7. Anatomy of GPU: multiprocessors GPU MP shared memory GPU is composed of tens of multiprocessors (streaming processors), which are composed of tens of cores = hundreds of cores
  8. 8. Compute Unified Device Architecture is a hierarchy of computation memory synchronization
  9. 9. Compute hierarchy software kernel hardware abstractions hardware thread thread block grid of blocks core multiprocessor GPU
  10. 10. Compute hierarchy thread threadIdx thread block blockIdx, blockDim grid of blocks gridDim
  11. 11. Python fast development huge # of packages: for data analysis, linear algebra, special functions etc metaprogramming Convenient, but not that fast in number crunching
  12. 12. PyCUDA Wrapper package around CUDA API Convenient abstractions: GPUArray, random numbers generation, reductions & scans etc Automatic cleanup, initialization and error checking, kernels caching Completeness
  13. 13. GPUArray NumPy-like interface for GPU arrays Convenient creation and manipulation routines Elementwise operations Cleanup
  14. 14. SourceModule Abstraction to create, compile and run GPU code GPU code to compile is passed as a string Control over nvcc compiler options Convenient interface to get kernels
  15. 15. Metaprogramming GPU code can be created at runtime PyCUDA uses mako template engine internally Any template engine is ok to create GPU source code. Remember about codepy Create more flexible and optimized code
  16. 16. Installation numpy, mako, CUDA driver & toolkit are required Boost.Python is optional Dev packages: if you build from source Also: PyOpenCl, pyfft
  17. 17. NumbaPro Accelerator package for Python Generates machine code from Python scalar functions (create ufunc) from numbapro import vectorize import numpy as np @vectorize(['float32(float32, float32)'], target='cpu') def add2(a, b): return a + b X = np.ones((1024), dtype='float32') Y = 2*np.ones((1024), dtype='float32') print add(X, Y) [3., 3., … 3.]
  18. 18. GPU computing resources Documentation Intro to Parallel Programming by David Luebke (Nvidia) and John Owens (UC Davis) Heterogeneous Parallel Programming by Wen-mei W. Hwu (UIUC) Tesla K20/K40 test drive