Erez Perelman

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Understanding program behavior is at the foundation of computer architecture and program optimization. Many programs have wildly different behavior on even the very largest of scales (over the complete execution of the program). This realization has ramifications for many architectural and compiler techniques, from thread scheduling, to feedback directed(More)
Abstract: Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months to complete. To overcome this problem researchers choose a very small portion of a program's execution to evaluate their results, rather than simulating the entire program. In this(More)
Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months to complete. To address this issue we have recently proposed using Simulation Points (found by only examining basic block execution frequency profiles) to increase the efficiency and accuracy(More)
This paper describes the new features available in the SimPoint 3.0 release. The release provides two techniques for drastically reducing the run-time of SimPoint: faster searching to find the best clustering, and efficiently clustering large numbers of intervals. SimPoint 3.0 also provides an option to output only the simulation points that represent the(More)
Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of a single industry standard benchmark at this level of detail takes on the order of months to complete. This problem is exacerbated by the fact that to properly perform an architectural evaluation requires multiple benchmarks to be evaluated across(More)
Most programs are repetitive, where similar behavior can be seen at different execution times. Algorithms have been proposed that automatically group similar portions of a program's execution into phases, where samples of execution in the same phase have homogeneous behavior and similar resource requirements. In this paper, we present an automated profiling(More)
This paper describes the new features available in the SimPoint 3.0 release. The release provides two techniques for drastically reducing the run-time of SimPoint: faster searching to find the best clustering, and efficiently clustering large numbers of intervals. SimPoint 3.0 also provides an option to output only the simulation points that represent the(More)
Most programs are repetitive, where similar behavior can be seen at different execution times. Proposed algorithms automatically group similar portions of a program’s execution into phases, where the intervals in each phase have homogeneous behavior and similar resource requirements. These prior techniques focus on fixed length intervals (such as a hundred(More)
Most programs are repetitive, where similar behavior can be seen at different execution times. Algorithms have been proposed that automatically group similar portions of a program's execution into phases, where samples of execution in the same phase have homogeneous behavior and similar resource requirements. In this paper, we examine applying these phase(More)