Precise Runahead Execution
Precise Runahead Execution
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精确的超前执行
DOI:
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发表时间:
2020
期刊:
影响因子:
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通讯作者:
L. Eeckhout
中科院分区:
文献类型:
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作者:
Ajeya Naithani;Josué Feliu;Almutaz Adileh;L. Eeckhout
Runahead execution improves processor performance by accurately prefetching long-latency memory accesses. When a long-latency load causes the instruction window to fill up and halt the pipeline, the processor enters runahead mode and keeps speculatively executing code to trigger accurate prefetches. A recent improvement tracks the chain of instructions that leads to the long-latency load, stores it in a runahead buffer, and executes only this chain during runahead execution, with the purpose of generating more prefetch requests. Unfortunately, all prior runahead proposals have shortcomings that limit performance and energy efficiency because they release processor state when entering runahead mode and then need to refill the pipeline to restart normal operation. Moreover, runahead buffer limits prefetch coverage by tracking only a single chain of instructions that leads to the same long-latency load. We propose precise runahead execution (PRE) which builds on the key observation that when entering runahead mode, the processor has enough issue queue and physical register file resources to speculatively execute instructions. This mitigates the need to release and re-fill processor state in the ROB, issue queue, and physical register file. In addition, PRE pre-executes only those instructions in runahead mode that lead to full-window stalls, using a novel register renaming mechanism to quickly free physical registers in runahead mode, further improving efficiency and effectiveness. Finally, PRE optionally buffers decoded runahead micro-ops in the frontend to save energy. Our experimental evaluation using a set of memory-intensive applications shows that PRE achieves an additional 18.2% performance improvement over the recent runahead proposals while at the same time reducing energy consumption by 6.8%.
DOI:
10.1109/hpca.2019.00064
发表时间:
2019
期刊:
2019 IEEE International Symposium on High Performance Computer Architecture (HPCA
影响因子:
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作者:
Kondguli, Sushant;Huang, Michael
通讯作者:
Huang, Michael