Type of Document Dissertation Author Whaley, R. Clint Author's Email Address firstname.lastname@example.org 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
- Backend Optimization
- Empirical Optimization
Date of Defense 2004-11-02 Availability unrestricted AbstractUsing 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
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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