Towards acceleration of fault simulation using graphics processing units
Proceedings of the 45th annual Design Automation Conference
Large calculation of the flow over a hypersonic vehicle using a GPU
Journal of Computational Physics
Accelerating statistical static timing analysis using graphics processing units
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
Fast circuit simulation on graphics processing units
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
Fault Table Computation on GPUs
Journal of Electronic Testing: Theory and Applications
Highly parallel decoding of space-time codes on graphics processing units
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Data structures and transformations for physically based simulation on a GPU
VECPAR'10 Proceedings of the 9th international conference on High performance computing for computational science
LCPC'10 Proceedings of the 23rd international conference on Languages and compilers for parallel computing
Proceedings of the 12th International Conference on Computer Systems and Technologies
Variants of Mersenne Twister Suitable for Graphic Processors
ACM Transactions on Mathematical Software (TOMS)
Optimising lossless stages in a GPU-based MPEG encoder
Multimedia Tools and Applications
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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.