Type of Document	Dissertation
Author	Whaley, R. Clint
Author's Email Address	whaley@cs.fsu.edu
URN	etd-11022004-175509
Title	Automated Empirical Optimization of High Performance Floating Point Kernels
Degree	Doctor of Philosophy
Department	Computer Science, Department of
Advisory Committee	Advisor Name Title David Whalley Committee Chair Gordon Erlebacher Committee Member Micheal Mascagni Committee Member Theodore Baker Committee Member Xin Yuan Committee Member
Keywords	Kernel Optimization Optimizing Compilers AEOS Backend Optimization Empirical Optimization
Date of Defense	2004-11-02
Availability	unrestricted
Abstract Using traditional methodologies and tools, the problem of keeping performance-critical kernels at high efficiency on hardware evolving at the incredible rates dictated by Moore's Law is almost intractable. On product lines where ISA compatibility is maintained through several generations of architecture, the growing gap between the machine that the software sees and the actual hardware exacerbates this problem considerably, as do the evolving software layers between the application in question and the ISA. To address this problem, we have utilized a relatively new technique, which we call AEOS (Automated Empirical Optimization of Software). In this paper, we describe the AEOS systems we have researched, implemented and tested. The first of these is ATLAS (Automatically Tuned Linear Algebra Software), which empirically optimizes key linear algebra kernels to arbitrary cache-based machines. Our latest research effort is instantiated in the iFKO (iterative Floating Point Kernel Optimizer) project, whose aim is to perform empirical optimization of relatively arbitrary kernels using a low-level iterative and empirical compilation framework.
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