CMU-SAFARI/Athena
A reinforcement learning based policy to dynamically coordinate off-chip predictor with multiple data prefetchers, as described in the HPCA2026 paper by Bera and Lang et al.: https://arxiv.org/abs/2601.17615
Athena is a tool for computer architects and system designers to optimize the performance of high-performance processors. It takes in workload execution traces and configuration details of various cache prefetchers and off-chip predictors. The output helps analyze and improve processor execution cycles and memory access efficiency, specifically by coordinating how data is fetched and predicted.
Use this if you are designing or evaluating high-performance processor architectures and need to optimize the interaction between data prefetchers and off-chip predictors to reduce memory latency.
Not ideal if you are working on software-level performance optimization or general application profiling, as this tool is focused on hardware-level architectural simulations.
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C++
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Last pushed
Jan 27, 2026
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