Generating Empirically Optimized Composed Matrix Kernels from MATLAB Prototypes

The development of optimized codes is time-consuming and requires extensive architecture, compiler, and language expertise, therefore, computational scientists are often forced to choose between investing considerable time in tuning code or accepting lower performance. In this paper, we describe the first steps toward a fully automated system for the optimization of the matrix algebra kernels that are a foundational part of many scientific applications. To generate highly optimized code from a high-level MATLAB prototype, we define a three-step approach. To begin, we have developed a compiler that converts a MATLAB script into simple C code. We then use the polyhedral optimization system PLuTo to optimize that code for coarse-grained parallelism and locality simultaneously. Finally, we annotate the resulting code with performance-tuning directives and use the empirical performance-tuning system Orio to generate many tuned versions of the same operation using different optimization ...

• Type: Conference
• Year: 2009
• Where: ICCS
• Authors: Boyana Norris, Albert Hartono, Elizabeth R. Jessup, Jeremy G. Siek
• Keywords: Applied Computing, Code, ICCS 2009, Optimized Code, Optimized Code Variants