Computational Technology Resources - CSETS

Keywords: dynamic code generation, compiler, GPU programming.

Abstract

In this chapter, we present an unusual strategy to perform dynamic code generation for NVIDIA GPUs. The usual compiler relies on assumptions that are not always true, which can lead to sub-optimal code resulting from a lack of information available to the compiler. By using a code generator deGoal that can produce a code in a pseudo-assembly code representation for NVIDIA GPUs, we will show how to dynamically generate code usable by the GPU. deGoal uses a low level intermediate representation to produce, at run-time, code that benefits from run-time information to produce more optimal and/or flexible GPU routines. This approach removes the need to statically specialize the code for a wide variety of configurations and still produces good performances. We illustrate our tool usage by the matrix multiplication on various configurations. This example shows that it allows us to develop an application and get near optimal results faster than developing a specialized version.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Computational Science, Engineering & Technology Series ISSN 1759-3158 CSETS: 34 PATTERNS FOR PARALLEL PROGRAMMING ON GPUS Edited by: F. Magoulès Chapter 2 Data Size and Data Type Dynamic GPU Code Generation H.-P. Charles and V. Lomüller CEA-LIST, Gif-sur-Yvette, France doi:10.4203/csets.34.2 purchase the full-text of this chapter Full Bibliographic Reference for this chapter H.-P. Charles, V. Lomüller, "Data Size and Data Type Dynamic GPU Code Generation", in F. Magoulès, (Editor), "Patterns for Parallel Programming on GPUs", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 2, pp 31-55, 2014. doi:10.4203/csets.34.2 Keywords: dynamic code generation, compiler, GPU programming. Abstract In this chapter, we present an unusual strategy to perform dynamic code generation for NVIDIA GPUs. The usual compiler relies on assumptions that are not always true, which can lead to sub-optimal code resulting from a lack of information available to the compiler. By using a code generator `deGoal` that can produce a code in a pseudo-assembly code representation for NVIDIA GPUs, we will show how to dynamically generate code usable by the GPU. `deGoal` uses a low level intermediate representation to produce, at run-time, code that benefits from run-time information to produce more optimal and/or flexible GPU routines. This approach removes the need to statically specialize the code for a wide variety of configurations and still produces good performances. We illustrate our tool usage by the matrix multiplication on various configurations. This example shows that it allows us to develop an application and get near optimal results faster than developing a specialized version. purchase the full-text of this chapter (price £20) go to the previous chapter go to the next chapter return to the table of contents return to the book description purchase this book (price £85 +P&P)
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