Khepri: Crystallizing TAGE for Memory Efficient Prewarm in Serverless Computing

Le résumé fourni par la source

As an increasingly popular cloud computing model, serverless computing suffers from performance degradation caused by microarchitectural cold start. Previous studies identify the front-end as the bottleneck and explore solutions such as instruction prefetching and restoring Branch Target Buffer. However, they fail to prewarm the Conditional Branch Predictor (CBP), because the large size of its core component, the TAgged GEometric history length predictor (TAGE), makes it impractical to be saved and restored.This paper observes the predictive sparsity of TAGE, where only a small subset of entries can dominate the predictor’s coverage and accuracy. We introduce Khepri, a memory efficient CBP prewarming mechanism that uses a TAGE Crystallization algorithm to identify these dominant entries. Khepri records them in main memory and restores them to prewarm TAGE at the next invocation. Khepri achieves a 1.57× speedup over the baseline and outperforms the state-of-the-art technique by 14%, requiring only 1.54KB in main memory on average.

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