Year: 2004
Type: report
Abstract: There is growing interest in autonomic, self-tuning software that can optimize itself on new platforms, without man-ual intervention. Optimization requires detailed knowledge of the target platform such as the latency and throughput of instructions, the numbers of registers, and the organization of the memory hierarchy. An autonomic optimization system needs to determine such platform-specific information on its own. In this paper, we describe the design and implementation of X-Ray, which is a tool that automatically measures a large number of such platform-specific parameters. For some of these parameters, we also describe novel algorithms, which are more robust than existing ones. X-Ray is written in C for maximum portability, and it is based on accurate timing of a number of carefully designed micro-benchmarks. A novel feature of X-Ray is that it is easily extensible because it provides simple infrastructure and a code generator that can be used to produce the large number of micro-benchmarks needed for such measurements. There are few existing tools that address this problem. Our experiments show that X-Ray produces more accurate and more complete results than any of them. 1
Source: eCommons (Cornell University)
Authors: Kamen Yotov, Keshav Pingali, Paul Stodghill
Language: French
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: Synthesis of Block-recursive Codes for Deep Memory Hierarchies, MATLAB Extensions and Compiler Techniques for High-Performance Computing, ITR: Adaptive Software for Field-driven Simulations, Next Generation Software: A Framework For Developing Complex Applications On High-End Petaflop-Class Machines, CISE Research Infrastructure: A Two-tier Computation and Visualization Facility for Multiscale Problems
OpenAlex ID: W1598383369