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Cache-Aware Kernel Tiling: An Approach for System-Level Performance Optimization of GPU-Based Applications
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Singapore University of Technology and Design (SUTD), Information Systems Technology and Design (ISTD).
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present a software approach to address the data latency issue for certain GPU applications. Each application is modeled as a kernel graph, where the nodes represent individual GPU kernels and the edges capture data dependencies. Our technique exploits the GPU L2 cache to accelerate parameter passing between the kernels. The key idea is that, instead of having each kernel process the entire input in one invocation, we subdivide the input into fragments (which fit in the cache) and, ideally, process each fragment in one continuous sequence of kernel invocations. Our proposed technique is oblivious to kernel functionalities and requires minimal source code modification. We demonstrate our technique on a full-fledged image processing application and improve the performance on average by 30% over various settings.

Place, publisher, year, edition, pages
2019.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-160934DOI: 10.23919/DATE.2019.8714861Scopus ID: 2-s2.0-85066611204OAI: oai:DiVA.org:liu-160934DiVA, id: diva2:1361171
Conference
2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), Florence, Italy, 25-29 March, 2019
Available from: 2019-10-15 Created: 2019-10-15 Last updated: 2019-10-25Bibliographically approved

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Maghazeh, Arian

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