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OSDI 2000 Abstract
Performance-Driven Processor Allocation
Julita Corbalán, Xavier Martorell, and Jesús Labarta, Universitat Politècnica de Catalunya
Abstract
This work is focused on processor allocation in shared-memory multiprocessor systems, where no knowledge of the
application is available when applications are submitted. We perform the processor allocation taking into account the
characteristics of the application measured at run-time. We want to demonstrate the importance of an accurate performance
analysis and the criteria used to distribute the processors. With this aim, we present the SelfAnalyzer, an approach to
dynamically analyzing the performance of applications (speedup, efficiency and execution time), and the Performance-Driven
Processor Allocation (PDPA), a new scheduling policy that distributes processors considering both the global conditions of the
system and the particular characteristics of running applications. This work also defends the importance of the interaction
between the medium-term and the long-term scheduler to control the multiprogramming level in the case of the clairvoyant
scheduling policies1. We have implemented our proposal in an SGI Origin2000 with 64 processors and we have compared its
performance with that of some scheduling policies proposed so far and with the native IRIX scheduling policy. Results show
that the combination of the SelfAnalyzer+PDPA with the medium/long-term scheduling interaction outperforms the rest of the
scheduling policies evaluated. The evaluation shows that in workloads where a simple equipartition performs well, the PDPA
also performs well, and in extreme workloads where all the applications have a bad performance, our proposal can achieve a
speedup of 3.9 with respect to an equipartition and 11.8 with respect to the native IRIX scheduling policy.
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