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View all- Seo DChen PSung CHoang QManzanares ADutt N(2024)MemScape: Sculpting Tiered Memory Management for Autonomous VehiclesACM Transactions on Embedded Computing Systems10.1145/3701621Online publication date: 26-Oct-2024
PMAlloc: A Holistic Approach to Improving Persistent Memory Allocation
Authors: 
Zheng Dang, Shuibing He, Xuechen Zhang, Peiyi Hong, Zhenxin Li, Xinyu Chen, Haozhe Song, Xian-He Sun, Gang ChenAuthors Info & Claims
Zheng Dang, Shuibing He, Xuechen Zhang, Peiyi Hong, Zhenxin Li, Xinyu Chen, Haozhe Song, Xian-He Sun, Gang ChenAuthors Info & ClaimsArticle No.: 7, Pages 1 - 52
Abstract
Persistent memory allocation is a fundamental building block for developing high-performance and in-memory applications. Existing persistent memory allocators suffer from many performance issues. First, they may introduce repeated cache line flushes and small random accesses in persistent memory for their poor heap metadata management. Second, they use static slab segregation resulting in a dramatic increase in memory consumption when allocation request size is changed. Third, they are not aware of NUMA effect, leading to remote persistent memory accesses in memory allocation and deallocation processes. In this article, we design a novel allocator, named PMAlloc, to solve the above issues simultaneously. (1) PMAlloc eliminates cache line reflushes by mapping contiguous data blocks in slabs to interleaved metadata entries stored in different cache lines. (2) It writes small metadata units to a persistent bookkeeping log in a sequential pattern to remove random heap metadata accesses in persistent memory. (3) Instead of using static slab segregation, it supports slab morphing, which allows slabs to be transformed between size classes to significantly improve slab usage. (4) It uses a local-first allocation policy to avoid allocating remote memory blocks. And it supports a two-phase deallocation mechanism including recording and synchronization to minimize the number of remote memory access in the deallocation. PMAlloc is complementary to the existing consistency models. Results on six benchmarks demonstrate that PMAlloc improves the performance of state-of-the-art persistent memory allocators by up to 6.4× and 57× for small and large allocations, respectively. PMAlloc with NUMA optimizations brings a 2.9× speedup in multi-socket evaluation and is up to 36× faster than other persistent memory allocators. Using PMAlloc reduces memory usage by up to 57.8%. Besides, we integrate PMAlloc in a persistent FPTree. Compared to the state-of-the-art allocators, PMAlloc improves the performance of this application by up to 3.1×.
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Publication History
Published: 20 September 2024
Online AM: 03 February 2024
Accepted: 21 January 2024
Revised: 08 September 2023
Received: 26 November 2022
Published in TOCS Volume 42, Issue 3-4
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View all- Seo DChen PSung CHoang QManzanares ADutt N(2024)MemScape: Sculpting Tiered Memory Management for Autonomous VehiclesACM Transactions on Embedded Computing Systems10.1145/3701621Online publication date: 26-Oct-2024
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