Performance Prediction and Analysis of Compute-intensive Tasks on GPUs

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dc.contributor.authorHasan, Khondker
dc.date.accessioned2020-06-11T18:38:40Z
dc.date.available2020-06-11T18:38:40Z
dc.date.issued2014
dc.description.abstractUsing Graphics Processing Units (GPUs) to solve general purpose problems has received significant attention both in academia and industry. Harnessing the power of these devices however requires knowledge of the underlying architecture and the programming model. In this paper, we develop analytical models to predict the performance of GPUs for computationally intensive tasks. Our models are based on varying the relevant parameters - including total number of threads, number of blocks, and number of streaming multi-processors - and predicting the performance of a program for a specified instance of these parameters. The approach can be used in the context of heterogeneous environments where distinct types of GPU devices with different hardware configurations are employed.en_US
dc.identifier.citationKhondker S. Hasan, Amlan Chatterjee, Sridhar Radhakrishnan, and John K Antonio, "Performance Prediction and Analysis of Compute-intensive Tasks on GPUs",The 11th IFIP International Conference on Network and Parallel Computing (NPC-14), Sept. 2014, Lecture Notes in Computer Science (LNCS), Springer, ISBN: 978-3-662-44917-2, Vol: 8707, pp 612-17, Berlin, Germany, 2014.en_US
dc.identifier.urihttps://hdl.handle.net/10657.1/2368
dc.language.isoen_USen_US
dc.publisherThe 11th IFIP International Conference on Network and Parallel Computingen_US
dc.subjectCompute-Intense Kernels CUDA GPU Modeling and predictionen_US
dc.titlePerformance Prediction and Analysis of Compute-intensive Tasks on GPUsen_US
dc.typeArticleen_US

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Khondker Hasan