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HPCA2023顶会

Safety Hints for HTM Capacity Abort Mitigation

Anirudh Jain, Divya Kiran Kadiyala, Alexandros Daglis

2023年份

摘要

Hardware Transactional Memory (HTM) is a high-performance instantiation of the powerful programming abstraction of transactional memory, which simplifies the daunting— yet critically important—task of parallel programming. While many HTM implementations with variable complexity exist in the literature, commercially available HTMs impose rigid restrictions to transaction and system behavior, limiting their practical use. A key constraint is the limited size of supported transactions, implicitly capped by hardware buffering capacity. We identify the opportunity to expand the effective capacity of these limited hardware structures by being more selective in memory accesses that need to be tracked. We leverage compiler and virtual memory support to identify safe memory accesses, which can never cause a transaction abort, subsequently passed as safety hints to the underlying HTM. With minor extensions over a conventional HTM implementation, HinTM uses these hints to selectively allocate transactional state tracking resources to unsafe accesses only, thus expanding the HTM’s effective capacity, and conversely reducing capacity aborts. We demonstrate that HinTM effectively augments the performance of a range of baseline HTM configurations. When coupled with a POWER8 HTM implementation, HinTM eliminates 64% of transactional capacity aborts, achieving 1.4× average speedup, and up to 8.7×.

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