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HIPC
2009
Springer

Optimizing the use of GPU memory in applications with large data sets

11 years 3 months ago
Optimizing the use of GPU memory in applications with large data sets
Abstract--With General Purpose programmable GPUs becoming more and more popular, automated tools are needed to bridge the gap between achievable performance from highly parallel architectures and the performance required in applications. In this paper, we concentrate on improving GPU memory management for applications with large and intermediate data sets that do not completely fit in GPU memory. For such applications, the movement of the extra data to CPU memory must be carefully managed. In particular, we focus on solving the joint task scheduling and data transfer scheduling problem posed in [1], and propose an algorithm that gives close to optimal results (as measured by running simulated annealing overnight) in terms of the amount of data transferred for image processing benchmarks such as edge detection and Convolutional Neural Networks. Our results enable a reduction of up to 30
Nadathur Satish, Narayanan Sundaram, Kurt Keutzer
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where HIPC
Authors Nadathur Satish, Narayanan Sundaram, Kurt Keutzer
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