GPGPU: general-purpose computation on graphics hardware. The graphics processor (GPU) on today’s commodity video cards has evolved into an extremely powerful and flexible processor. Modern graphics architectures provide tremendous memory bandwidth and computational horsepower, with dozens of fully programmable shading units that support vector operations and IEEE floating point precision. High-level languages have emerged for graphics hardware, making this computational power accessible. GPGPU stands for ”General-Purpose Computation on GPUs”. GPGPU researchers have achieved over an order of magnitude speedup over modern CPUs on some non-graphics problems.This course provides detailed coverage of general-purpose computation on graphics hardware. We emphasize core computational building blocks, ranging from linear algebra to database queries, and review the tools, perils, and strategies in GPU programming. We present analysis of GPU performance characteristics, and use this analysis to provide insight into how to build efficient GPGPU algorithms. Finally we present a set of case studies on general-purpose applications of graphics hardware.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 17 articles )

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  1. Xu, Chuanfu; Deng, Xiaogang; Zhang, Lilun; Fang, Jianbin; Wang, Guangxue; Jiang, Yi; Cao, Wei; Che, Yonggang; Wang, Yongxian; Wang, Zhenghua; Liu, Wei; Cheng, Xinghua: Collaborating CPU and GPU for large-scale high-order CFD simulations with complex grids on the TianHe-1A supercomputer (2014)
  2. Demidov, Denis; Ahnert, Karsten; Rupp, Karl; Gottschling, Peter: Programming CUDA and OpenCL: a case study using modern C++ libraries (2013)
  3. Přikryl, Jan: Graphics card as a cheap supercomputer. (2013)
  4. Cabido, Raúl; Montemayor, Antonio S.; Pantrigo, Juan J.: High performance memetic algorithm particle filter for multiple object tracking on modern gpus (2012)
  5. Xu, Xin-Hai; Yang, Xue-Jun; Xue, Jing-Ling; Lin, Yu-Fei; Lin, Yi-Song: PartialRC: A partial recomputing method for efficient fault recovery on GPGPUs (2012)
  6. Mistry, Perhaad; Schaa, Dana; Jang, Byunghyun; Kaeli, David; Dvornik, Albert; Meglan, Dwight: Data structures and transformations for physically based simulation on a GPU (2011)
  7. Langdon, William B.: Large scale bioinformatics data mining with parallel genetic programming on graphics processing units (2010)
  8. Vidal, Pablo; Alba, Enrique: Cellular genetic algorithm on graphic processing units (2010)
  9. Lin, Yu-Te; Chen, Peng-Sheng: Compiler support for general-purpose computation on GPUs (2009)
  10. Wong, Man Leung; Wong, Tien Tsin: Implementation of parallel genetic algorithms on graphics processing units (2009)
  11. Elsen, Erich; LeGresley, Patrick; Darve, Eric: Large calculation of the flow over a hypersonic vehicle using a GPU (2008)
  12. Jung, Jin Hyuk; O’leary, Dianne P.: Implementing an interior point method for linear programs on a CPU-GPU system (2008)
  13. Kotani, Y.; Ino, F.; Hagihara, K.: A resource selection system for cycle stealing in GPU grids (2008)
  14. Anderson, Amos G.; Goddard, William A. III; Schröder, Peter: Quantum Monte Carlo on graphical processing units (2007)
  15. Huang, Xin; Li, Sheng; Wang, Guoping: Displacement modeling: Hardware-accelerated interactive feature modeling on subdivision surfaces (2007)
  16. Hughey, Richard; Di Blas, Andrea: Finding the next computational model: Experience with the UCSC kestrel (2007)
  17. Ohshima, Satoshi; Kise, Kenji; Katagiri, Takahiro; Yuba, Toshitsugu: Parallel processing of matrix multiplication in a CPU and GPU heterogeneous environment (2006)