Demystifying GPU microarchitecture through microbenchmarking

Graphics processors (GPU) offer the promise of more than an order of magnitude speedup over conventional processors for certain non-graphics computations. Because the GPU is often presented as a C-like abstraction (e.g., Nvidia's CUDA), little is known about the characteristics of the GPU'...

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Bibliographic Details
Published in2010 IEEE International Symposium on Performance Analysis of Systems and Software pp. 235 - 246
Main Authors Wong, Henry, Papadopoulou, Misel-Myrto, Sadooghi-Alvandi, Maryam, Moshovos, Andreas
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2010
Subjects
Clocks
Computer architecture
Delay
Graphics processing unit
Hardware
Kernel
Microarchitecture
Performance analysis
Registers
Samarium
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ISBN1424460239
9781424460236
DOI10.1109/ISPASS.2010.5452013

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Summary:Graphics processors (GPU) offer the promise of more than an order of magnitude speedup over conventional processors for certain non-graphics computations. Because the GPU is often presented as a C-like abstraction (e.g., Nvidia's CUDA), little is known about the characteristics of the GPU's architecture beyond what the manufacturer has documented. This work develops a microbechmark suite and measures the CUDA-visible architectural characteristics of the Nvidia GT200 (GTX280) GPU. Various undisclosed characteristics of the processing elements and the memory hierarchies are measured. This analysis exposes undocumented features that impact program performance and correctness. These measurements can be useful for improving performance optimization, analysis, and modeling on this architecture and offer additional insight on the decisions made in developing this GPU.
ISBN:1424460239
9781424460236
DOI:10.1109/ISPASS.2010.5452013