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Efficient performance prediction for modern microprocessors

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Published:01 June 2000Publication History

ABSTRACT

Generating an accurate estimate of the performance of a program on a given system is important to a large number of people. Computer architects, compiler writers, and developers all need insight into a machine's performance. There are a number of performance estimation techniques in use, from profile-based approaches to full machine simulation. This paper discusses a profile-based performance estimation technique that uses a lightweight instrumentation phase that runs in order number of dynamic instructions, followed by an analysis phase that runs in roughly order number of static instructions. This technique accurately predicts the performance of the core pipeline of a detailed out-of-order issue processor model while scheduling far fewer instructions than does full simulation. The difference between the predicted execution time and the time obtained from full simulation is only a few percent.

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                    • Published in

                      cover image ACM Conferences
                      SIGMETRICS '00: Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
                      June 2000
                      329 pages
                      ISBN:1581131941
                      DOI:10.1145/339331
                      • cover image ACM SIGMETRICS Performance Evaluation Review
                        ACM SIGMETRICS Performance Evaluation Review  Volume 28, Issue 1
                        Special issue on proceedings of ACM SIGMETRICS 2000
                        June 2000
                        327 pages
                        ISSN:0163-5999
                        DOI:10.1145/345063
                        Issue’s Table of Contents

                      Copyright © 2000 ACM

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                      Publication History

                      • Published: 1 June 2000

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                      SIGMETRICS '00 Paper Acceptance Rate28of165submissions,17%Overall Acceptance Rate459of2,691submissions,17%

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