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Data-parallel programming languages are an important component in today's parallel computing landscape. Among those are domain-specific languages like shading languages in graphics (HLSL, GLSL, RenderMan, etc.) and “general-purpose” languages like CUDA or OpenCL. Current implementations of those languages on CPUs solely rely on multi-threading to implement parallelism and ignore the additional intra-core...
General-Purpose Computing on Graphics Processing Units (GPGPU) has lately been of great interest due to the release of architectures and software that simplifies programming graphics cards. This study explores how performance scales with FIR digital filters by varying the number of taps and the samples. We also discuss the trade-offs with various techniques for GPGPU programming in CUDA.
Recent years have seen a trend in using graphic processing units (GPU) as accelerators for general-purpose computing. The inexpensive, single-chip, massively parallel architecture of GPU has evidentially brought factors of speedup to many numerical applications. However, the development of a high-quality GPU application is challenging, due to the large optimization space and complex unpredictable...
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