Reducing Load Latency with Cache Level Prediction
Majid Jalili, Mattan Erez
Abstract
High load latency that results from deep cache hierarchies and relatively slow main memory is an important limiter of single-thread performance. Data prefetch helps reduce this latency by fetching data up the hierarchy before it is requested by load instructions. However, data prefetching has shown to be imperfect in many situations. We propose cache-level prediction to complement prefetchers. Our method predicts which memory hierarchy level a load will access allowing the memory loads to start earlier, and thereby saves many cycles. The predictor provides high prediction accuracy at the cost of just one cycle added latency to L1 misses. Level prediction reduces the memory access latency by 20% on average, and provides speedup of 10.3% over a conventional baseline, and 6.1% over a boosted baseline on generic, graph, and HPC applications.
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Install the CLIlune papers fulltext 3ceb97f0-ccfa-4077-afb4-dcaefdd00becCited by top-tier papers5
- Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load PredictionRahul Bera, Konstantinos Kanellopoulos, Shankar Balachandran, David Novo et al.MICRO 2022 · 37 citations
- A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch FilteringAlexandre Valentin Jamet, Georgios Vavouliotis, Daniel A. Jiménez, Lluc Alvarez et al.HPCA 2024 · 18 citations
- Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware PrefetchingZixiao Chen, Chentao Wu, Yunfei Gu, Ranhao Jia et al.HPCA 2025 · 10 citations
- COSMOS: RL-Enhanced Locality-Aware Counter Cache Optimization for Secure MemoryHaoran Geng, Xiaoyang Lu, Yuezhi Che, Ziang Tian et al.MICRO 2025 · 1 citation
- Athena: Synergizing Data Prefetching and Off-Chip Prediction via Online Reinforcement LearningRahul Bera, Zhenrong Lang, Caroline Hengartner, Konstantinos Kanellopoulos et al.HPCA 2026
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