Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement Learning

Prefetching and off-chip prediction are two techniques proposed to hide long memory access latencies in high-performance processors. In this work, we demonstrate that: (1) prefetching and off-chip prediction often provide complementary performance benefits, yet (2) naively combining these two mechanisms often fails to realize their full performance potential, and (3) existing prefetcher control policies (both heuristic-and learning-based) leave significant room for performance improvement behind.

Our goal is to design a holistic framework that can autonomously learn to coordinate an off-chip predictor with multiple prefetchers employed at various cache levels, delivering consistent performance benefits across a wide range of workloads and system configurations.

To this end, we propose a new technique called Athena, which models the coordination between prefetchers and off-chip predictor (OCP) as a reinforcement learning (RL) problem. Athena acts as the RL agent that observes multiple system-level features (e.g., prefetcher/OCP accuracy, bandwidth usage) over an epoch of program execution, and uses them as state information to select a coordination action (i.e., enabling the prefetcher and/or OCP, and adjusting prefetcher aggressiveness). At the end of every epoch, Athena receives a numerical reward that measures the change in multiple system-level metrics (e.g., number of cycles taken to execute an epoch). Athena uses this reward to autonomously and continuously learn a policy to coordinate prefetchers with OCP.

Athena makes a key observation that using performance improvement as the sole RL reward, as used in prior work, is unreliable, as it confounds the effects of the agent's actions with inherent variations in workload behavior. To address this limitation, Athena introduces a composite reward framework that separates (1) system-level metrics directly influenced by Athena's actions (e.g., last-level cache misses) from ( 2) metrics primarily driven by workload phase changes (e.g., number of mispredicted branches). This allows Athena to autonomously learn a coordination policy by isolating the true impact of its actions from inherent variations in workload behavior.

Our extensive evaluation using a diverse set of 100 memoryintensive workloads shows that Athena consistently outperforms multiple prior state-of-the-art coordination policies across a wide range of system configurations with various combinations of underlying prefetchers at various cache levels, OCPs, and main memory bandwidths, while incurring only modest storage overhead and design complexity. The source code of Athena is freely available at https://github.com/CMU-SAFARI/Athena.