图形处理单元的通用计算
计算机科学
绘图
加速
实时计算机图形学
并行计算
图形硬件
内存带宽
库达
指令集
SIMD公司
协处理器
计算机体系结构
计算机图形学(图像)
三维计算机图形学
作者
David Luebke,Mark Harris,Naga K. Govindaraju,Aaron Lefohn,Mike Houston,John D. Owens,Mark Segal,Matthew Papakipos,Ian Buck
标识
DOI:10.1145/1188455.1188672
摘要
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.
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