Abstract
SimPoint is a technique used to pick what parts of the program’s execution to simulate in order to have a complete picture of execution. SimPoint uses data clustering algorithms from machine learning to automatically find repetitive (similar) patterns in a program’s execution, and it chooses one sample to represent each unique repetitive behavior. Each sample is then simulated and weighted appropriately, and then together the results from these samples represent an accurate picture of the complete execution of the program.
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Hamerly, G., Perelman, E., Sherwood, T., Calder, B. (2010). Representative Sampling Using SimPoint. In: Leupers, R., Temam, O. (eds) Processor and System-on-Chip Simulation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6175-4_10
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