MemPerf: Profiling Allocator-Induced Performance Slowdowns




Zhou, Jin
Silvestro, Sam
Tang, Steven (Jiaxun)
Yang, Hanmei
Liu, Hongyu
Zeng, Guangming
Wu, Bo
Liu, Cong
Liu, Tongping

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Association for Computing Machinery


The memory allocator plays a key role in the performance of applications, but none of the existing profilers can pinpoint performance slowdowns caused by a memory allocator. Consequently, programmers may spend time improving application code incorrectly or unnecessarily, achieving low or no performance improvement. This paper designs the first profiler—MemPerf—to identify allocator-induced performance slowdowns without comparing against another allocator. Based on the key observation that an allocator may impact the whole life-cycle of heap objects, including the accesses (or uses) of these objects, MemPerf proposes a life-cycle based detection to identify slowdowns caused by slow memory management operations and slow accesses separately. For the prior one, MemPerf proposes a thread-aware and type-aware performance modeling to identify slow management operations. For slow memory accesses, MemPerf utilizes a top-down approach to identify all possible reasons for slow memory accesses introduced by the allocator, mainly due to cache and TLB misses, and further proposes a unified method to identify them correctly and efficiently. Based on our extensive evaluation, MemPerf reports 98% medium and large allocator-reduced slowdowns (larger than 5%) correctly without reporting any false positives. MemPerf also pinpoints multiple known and unknown design issues in widely-used allocators.



memory allocator, performance slowdowns


Zhou, J., Silvestro, S., Tang, S., Yang, H., Liu, H., Zeng, G., . . . Liu, T. (2023). MemPerf: Profiling Allocator-Induced Performance Slowdowns. Proceedings of the ACM on Programming Languages, 7(OOPSLA2). doi:10.1145/3622848


Computer Science