<ul><li>Researchers have developed Concorde, a methodology for learning fast and accurate performance models of microarchitectures.</li><li>Concorde uses compact performance distributions to predict program behavior based on different microarchitectural components.</li><li>Experiments show that Concorde is over five orders of magnitude faster than a reference cycle-level simulator.</li><li>It has an average Cycles-Per-Instruction (CPI) prediction error of about 2% across various benchmarks.</li></ul>

Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion

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