We are upgrading the repository! We will continue our upgrade in February 2025 - we have taken a break from the upgrade to open some collections for end-of-semester submission. The MS-GIST Master's Reports, SBE Senior Capstones, IPLP dissertations, and UA Faculty Publications collections are currently open for submission. Please reach out to repository@u.library.arizona.edu with your questions, or if you are a UA affiliate who needs to make content available in another collection.
Author
Clark, JerryAffiliation
Lockheed Martin Telemetry & InstrumentationIssue Date
1996-10Keywords
Parallel ComputingProcessing Elements (PEs)
Efficiently Mapped
Dependence
Vector
Systolic Array
Metadata
Show full item recordRights
Copyright © International Foundation for TelemeteringCollection Information
Proceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.Abstract
Improved processing techniques, particularly with respect to parallel computing, are the underlying focus in computer science, engineering, and industry today. Semiconductor technology is fast approaching device physical limitations. Further advances in computing performance in the near future will be realized by improved problem-solving approaches. An important issue in parallel processing is how to effectively utilize parallel computers. It is estimated that many modern supercomputers and parallel processors deliver only ten percent or less of their peak performance potential in a variety of applications. Yet, high performance is precisely why engineers build complex parallel machines. Cumulative performance losses occur due to mismatches between applications, software, and hardware. For instance, a communication system's network bandwidth may not correspond to the central processor speed or to module memory. Similarly, as Internet bandwidth is consumed by modern multimedia applications, network interconnection is becoming a major concern. Bottlenecks in a distributed environment are caused by network interconnections and can be minimized by intelligently assigning processing tasks to processing elements (PEs). Processing speeds are improved when architectures are customized for a given algorithm. Parallel processing techniques have been ineffective in most practical systems. The coupling of algorithms to architectures has generally been problematic and inefficient. Specific architectures have evolved to address the prospective processing improvements promised by parallel processing. Real performance gains will be realized when sequential algorithms are efficiently mapped to parallel architectures. Transforming sequential algorithms to parallel representations utilizing linear dependence vector mapping and subsequently configuring the interconnection network of a systolic array will be discussed in this paper as one possible approach for improved algorithm/architecture symbiosis.Sponsors
International Foundation for TelemeteringISSN
0884-51230074-9079