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The proliferation of fast, dense, byte-addressable nonvolatile memory suggests that data might be kept in pointer-rich “in-memory” format across program runs and even process and system crashes. For full generality, such data requires dynamic memory allocation, and while the allocator could in principle be “rolled into” each data structure, it is desirableto make it a separate abstraction. Toward this end, we introduce _recoverability_, a correctness criterion for persistent allocators, together with a nonblocking allocator, _Ralloc,_ that satisfies this criterion. Ralloc is based on the _LRMalloc_ of Leite and Rocha, with four key innovations: First, we persist just enough information during normal operation to permit a garbage collection (GC) pass to correctly reconstruct the heap in the wake of a full-system crash. Second, we introduce the notion of _filter functions_, which identify the locations of pointers within persistent blocks to mitigate the limitations of conservative GC. Third, we reorganize the layout of the heap to facilitate the incremental allocation of physical space. Fourth, we employ position-independent (offset-based) pointers to allow persistent regions to be mapped at an arbitrary address. Experiments show Ralloc to be performance-competitive with both _Makalu_, the state-of-the-art lock-based persistent allocator, and such transient allocators as LRMalloc and JEMalloc. In particular, reliance on GC and offline metadata reconstruction allows Ralloc to pay almost nothing for persistence during normal operation.
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PMAlloc: A Holistic Approach to Improving Persistent Memory Allocation
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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- Award ID(s):
- 2216108
- PAR ID:
- 10653805
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Computer Systems
- Volume:
- 42
- Issue:
- 3-4
- ISSN:
- 0734-2071
- Page Range / eLocation ID:
- 1 to 52
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The proliferation of fast, dense, byte-addressable nonvolatile memory suggests the possibility of keeping data in pointer-rich “in-memory” format across program runs and even crashes. For full generality, such data requires dynamic memory allocation. Toward this end, we introduce _recoverability_, a correctness criterion for persistent allocators, together with a nonblocking allocator, _Ralloc_, that satisfies this criterion. Ralloc is based on _LRMalloc_, with three key innovations. First, we persist just enough information during normal operation to permit reconstruction of the heap after a full-system crash. Our reconstruction mechanism performs garbage collection (GC) to identify and remedy any failure-induced memory leaks. Second, in support of GC, we introduce the notion of _filter functions_, which identify the locations of pointers within persistent blocks. Third, to allow persistent regions to be mapped at an arbitrary address, we employ the position-independent pointer representation of Chen et al., both in data and in allocator metadata. Experiments show that Ralloc provides scalable performance competitive to that of both _Makalu_, the state-of-the-art lock-based persistent allocator, and the best transient allocators (e.g.,_JEMalloc_).more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3643886
