David A. Bader
IEEE Fellow
AAAS Fellow
Professor
College of Computing
Georgia Tech
Atlanta, GA 30332


 
 

 

DOSA: Design Optimizer for Scientific Applications

In this work, we propose an application composition system (ACS) that allows design-time exploration and automatic run-time optimizations so that we relieve application programmers and compiler writers from the challenging task of optimizing the computation in order to achieve high performance. Our new framework, called "Design Optimizer for Scientific Applications" (DOSA), allows the programmer or compiler writer to explore alternative designs and optimize for speed (or power) at design-time and use its run-time optimizer as an automatic ACS. The ACS constructs an efficient application that dynamically adapts to changes in the underlying execution environment based on the kernel model, architecture, system features, available resources, and performance feedback. The run-time system is a portable interface that enables dynamic application optimization by interfacing with the output of DOSA. It thus provides an application composition system that determines suitable components and performs continuous performance optimizations. We focus on utilizing advanced architectural features and memory-centric optimizations that reduce the I/O complexity, cache pollution, and processormemory traffic, in order to achieve high performance. The design-time effort uses a computer-aided design space exploration that provides a user-friendly graphical modeling environment, high-level performance estimation and profiling, and the ability to integrate low-level simulators suitable for HPC architectures.

Publication History

Versions of this paper appeared as:
  1. D.A. Bader and V.K. Prasanna, ``DOSA: Design Optimizer for Scientific Applications,'' NSF Next Generation Workshop, Long Beach, CA, March 25-26, 2007.
  2. D.A. Bader and V.K. Prasanna, ``DOSA: Design Optimizer for Scientific Applications,'' NSF Next Generation Workshop, Miami, FL, April 13-14, 2008.

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Last updated: February 16, 2009

 




Computational Biology



Parallel Computing



Combinatorics