Date: Tue, 05 Nov 1996 21:03:46 GMT Server: NCSA/1.5 Content-type: text/html Last-modified: Tue, 17 Sep 1996 19:08:22 GMT Content-length: 2544
For many experimental scientists, scientific progress and quality of research are strongly linked to computing throughput. In other words, most scientists are concerned with how many floating point operation per month or per year they can extract from their computing environment rather than the number of such operations the environment can provide them per second or minute. Floating point operations per second (FLOPS) has been the yardstick used by most High Performance Computing (HPC) efforts to evaluate their systems. Little attention as been devoted by the computing community to environments that can deliver large amounts of processing capacity over long periods of time. We refer to such environments as High Throughput Computing (HTC) environments. The key to HTC is effective management and exploitation of all available computing resources. Since the computing needs of most scientists can be satisfied these days by commodity CPUs and memory, high efficiency is not playing a major role in a HTC environment. The main challenge a typical HTC environment faces is how to maximize the amount of resources accessible to its customers. Distributed ownership of computing resources is the major obstacle such an environment has to overcome in order to expand the pool of resources it can draw from. Recent trends in the cost/performance ratio of computer hardware have placed the control (ownership) over powerful computing resources in the hands of individuals and small groups. These distributed owners will be willing to include their resources in a HTC environment only after they are convinced that their needs will be addressed and their rights protected. For more than a decade, the Condor team at the Computer Sciences Department at the University of Wisconsin-Madison has been developing and evaluating mechanisms and policies that support HTC on large collections distributively owned heterogeneous computing resources.
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