An integer programming framework for optimizing shared memory use on GPUs

Wenjing Ma, Gagan Agrawal

Research output: Contribution to conferencePaperpeer-review

14 Scopus citations

Abstract

General purpose computing using GPUs is becoming increasingly popular, because of GPU's extremely favorable performance/price ratio. Besides application development using CUDA, automatic code generation for GPUs is also receiving attention. Like standard processors, GPUs also have a memory hierarchy, which must be carefully optimized for in order to achieve efficient execution. Specifically, modern NVIDIA GPUs have a very small programmable cache, referred to as shared memory, accesses to which are nearly 100 to 150 times faster than accesses to the regular device memory. An automatically generated or hand-written CUDA program can explicitly control what variables and array sections are allocated on the shared memory at any point during the execution. This, however, leads to a difficult optimization problem. In this paper, we formulate and solve the shared memory allocation problem as an integer programming problem. We present a global (intraprocedural) framework which can model structured control flow, and is not restricted to a single loop nest. We consider allocation of scalars, arrays, and array sections on shared memory. We also briefly show how our framework can suggest useful loop transformations to further improve performance. Our experiments using several non-scientific application show that our integer programming framework outperforms a recently published heuristic method, and our loop transformations also improve performance for many applications.

Original languageEnglish (US)
Pages553
DOIs
StatePublished - 2010
Externally publishedYes
Event17th International Conference on High Performance Computing, HiPC 2010 - Goa, India
Duration: Dec 19 2010Dec 22 2010

Conference

Conference17th International Conference on High Performance Computing, HiPC 2010
Country/TerritoryIndia
CityGoa
Period12/19/1012/22/10

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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