Efficient support of position independence on non-volatile memory

Chen, Guoyang; Zhang, Lei; Budhiraja, Richa; Shen, Xipeng; Wu, Youfeng

doi:10.1145/3123939.3124543

Cited by 21 publications

(12 citation statements)

References 19 publications

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“…To allow persistent structures to be mapped at different virtual addresses in different processes and at different times, we use an offset-based pointer representation [8,10] to provide fully positionindependent data. (Specifically, each pointer stores the 64-bit offset of the target from the pointer itself.)…”

Section: Introductionmentioning

confidence: 99%

Understanding and optimizing persistent memory allocation

Cai

Wen

Beadle

et al. 2020

Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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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 desirable to 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 three key innovations. First, we persist just enough information during normal operation to permit correct reconstruction of the heap after a fullsystem crash. Our reconstruction mechanism performs garbage collection (GC) to identify and remedy any failure-induced memory leaks. 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, to allow persistent regions to be mapped at an arbitrary address, we employ position-independent (offset-based) pointers for both data and metadata.Experiments show Ralloc to be performance-competitive with both Makalu, the state-of-theart 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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Section: Introductionmentioning

confidence: 99%

Understanding and optimizing persistent memory allocation

Cai

Wen

Beadle

et al. 2020

Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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“…We use an offset-based pointer representation to allow persistent structures to be mapped at different virtual addresses after failure recovery. The same technique has been used in previous projects [4,6,9]. The nvalloc_free_from() returns a block or an extent specified by 𝑎𝑑𝑑𝑟𝑒𝑠𝑠 to the persistent memory heap.…”

Section: Programming Modelmentioning

confidence: 99%

NVAlloc: rethinking heap metadata management in persistent memory allocators

Dong

Hong

et al. 2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Persistent memory allocation is a fundamental building block for developing high-performance and in-memory applications. Existing persistent memory allocators suffer from suboptimal heap organizations that introduce repeated cache line flushes and small random accesses in persistent memory. Worse, many allocators use static slab segregation resulting in a dramatic increase in memory consumption when allocation request size is changed. In this paper, we design a novel allocator, named NVAlloc, to solve the above issues simultaneously. First, NVAlloc eliminates cache line reflushes by mapping contiguous data blocks in slabs to interleaved metadata entries stored in different cache lines. Second, it writes small metadata units to a persistent bookkeeping log in a sequential pattern to remove random heap metadata accesses in persistent memory. Third, instead of using static slab segregation, it supports slab morphing, which allows slabs to be transformed between size classes to significantly improve slab usage. NVAlloc is complementary to the existing consistency models. Results on 6 benchmarks demonstrate that NVAlloc improves the performance of state-of-the-art persistent memory allocators by up to 6.4x and 57x for small and large allocations, respectively. Using NVAlloc reduces memory usage by up to 57.8%. Besides, we integrate NVAlloc in a persistent FPTree. Compared to the state-of-the-art allocators, NVAlloc improves the performance of this application by up to 3.1x. CCS CONCEPTS• Software and its engineering → Allocation / deallocation strategies; • Hardware → Non-volatile memory.

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“…ObjectIDs must be translated to an address before they can be used to access memory. This overhead can be reduced through compiler or hardware support [9,26].…”

Section: Overheads and Optimizationsmentioning

confidence: 99%

“…In contrast, NVMM offers the potential for programmers to access persistent data directly through loads and stores. Providing direct access (DAX) to programmers improves performance, by cutting away unnecessary layers of software, but also introduces challenges, like support for failure-safety [22,11,16,5] and the need for position independence [9].…”

Section: Introductionmentioning

confidence: 99%

Persistent Data Retention Models

Wang,

Tuck

2020

Preprint

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Non-Volatile Memory devices may soon be a part of main memory, and programming models that give programmers direct access to persistent memory through loads and stores are sought to maximize the performance benefits of these new devices. Direct access introduces new challenges. In this work, we identify an important aspect of programming for persistent memory: the persistent data retention model.A Persistent Data Retention Model describes what happens to persistent data when code that uses it is modified. We identify two models present in prior work but not described as such, the Reset and Manual Model, and we propose a new one called the Automatic Model. The Reset model discards all persistent data when a program changes leading to performance overheads and write amplification. In contrast, if data is to be retained, the Manual Model relies on the programmer to implement code that upgrades data from one version of the program to the next. This reduces overheads but places a larger burden on the programmer.We propose the Automatic Model to assist a programmer by automating some or all of the conversion. We describe one such automatic approach, Lazily Extendible Data Structures, that uses language extensions and compiler support to reduce the effort and complexity associated with updating persistent data. We evaluate our PDRMs in the context of the Persistent Memory Development Kit (PMDK) using kernels and the TPC-C application. Manual Model shows an overhead of 2.90% to 4.10% on average, and LEDS shows overhead of 0.45% to 10.27% on average, depending on the workload. LEDS reduces the number of writes by 26.36% compared to Manual Model. Furthermore, LEDS significantly reduces the programming complexity by relying on the compiler to migrate persistent data.

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Efficient support of position independence on non-volatile memory

Cited by 21 publications

References 19 publications

Understanding and optimizing persistent memory allocation

Understanding and optimizing persistent memory allocation

NVAlloc: rethinking heap metadata management in persistent memory allocators

Persistent Data Retention Models

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