Darryl Gove
Darryl Gove has worked at Google on performance and efficiency since 2015.
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Beyond malloc efficiency to fleet efficiency: a hugepage-aware memory allocator
Andrew Hamilton Hunter
15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) (2021) (to appear)
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Memory allocation represents significant compute cost at the warehouse scale and its optimization can yield considerable cost savings. One classical approach is to increase the efficiency of an allocator to minimize the cycles spent in the allocator code. However, memory allocation decisions also impact overall application performance via data placement, offering opportunities to improve fleetwide productivity by completing more units of application work using fewer hardware resources. Here, we focus on hugepage coverage. We present TEMERAIRE, a hugepage-aware enhancement of TCMALLOC to reduce CPU overheads in the application’s code. We discuss the design and implementation of TEMERAIRE including strategies for hugepage-aware memory layouts to maximize hugepage coverage and to minimize fragmentation overheads. We present application studies for 8 applications, improving requests-per-second (RPS) by 7.7% and reducing RAM usage 2.4%. We present the results of a 1% experiment at fleet scale as well as the longitudinal rollout in Google’s warehouse scale computers. This yielded 6% fewer TLB miss stalls, and 26% reduction in memory wasted due to fragmentation. We conclude with a discussion of additional techniques for improving the allocator development process and potential optimization strategies for future memory allocators.
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Adaptive Hugepage Subrelease for Non-moving Memory Allocators in Warehouse-Scale Computers
Khanh Nguyen
International Symposium on Memory Management (ISMM) 2021 (to appear)
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Modern C++ server workloads rely on 2 MB huge pages to improve memory system performance via higher TLB hit rates. Huge pages have traditionally been supported at the kernel level, but recent work has shown that user-level, huge page-aware memory allocators can achieve higher huge page coverage and thus performance. These memory allocators deal with a trade-off: 1) allocate memory from the operating system (OS) at the granularity of a huge page, achieve high performance, but potentially waste memory due to fragmentation, or 2) limit fragmentation by breaking up huge pages into smaller 4 KB pages and returning them to the OS, but reduce performance due to lower huge page coverage. For example, the state-of-the-art TCMalloc allocator handles this trade-off by releasing memory to the OS at a configurable release rate, breaking up huge pages as necessary.
This approach balances performance and fragmentation well for machines running one workload. For multiple applications on the same machine however, the reduction in memory usage is only useful to overall performance if another workload uses this memory. In warehouse-scale computers, when an application releases and then reacquires the same amount or more memory quickly, but no other application uses the memory in the meantime, the release causes poorer huge page coverage without any system-wide benefit.
We introduce a metric, realized fragmentation, to capture this effect. We then present an adaptive release policy that dynamically determines when to break up huge pages and return them to the OS to optimize system-wide performance. We built this policy into TCMalloc and deployed it fleet-wide in our data centers, leading to an estimated 1% fleet-wide throughput improvement at negligible memory overhead.
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