Fast and secure Global-Heap for memory-centric computing
Fast and secure Global-Heap for memory-centric computing
复制标题
快速、安全的全局堆，用于以内存为中心的计算
DOI：
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发表时间：
2021
影响因子：
3.3
通讯作者：
Kangho Kim
中科院分区：
文献类型：
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作者：
Myung;Sangmin Lee;Baiksong An;Hong;Kangho Kim
内存计算已经被广泛用于在内存中快速处理数据，但它已经无法科普当前的数据爆炸。这是因为现代计算机在内存容量和带宽方面都有结构性限制，从根本上限制了内存中可以处理的数据量。为了克服这些限制，提出了以存储器为中心的计算概念，并且作为使该概念可行的尝试，正在积极研究非易失性存储器、下一代互连和以存储器为中心的操作系统技术。然而，虽然已经有许多研究这些概念，有效和安全地使用极大的存储空间的基本问题还没有完全解决。本文提出的全局堆解决了上述问题，从而支持了以内存为中心的计算的核心技术。在该技术中，以应用程序友好的方式提供可以在进程之间非常轻松地切换的堆级抽象作为全局资源。Global-Heap还提供了一种有效的方法，可以安全地使用基于Intel内存保护密钥的巨大地址空间。我们在Linux上实现了全局堆，并开发了三个适用于以数据为中心的应用程序的有用用例。我们的初步评估表明，Global-Heap支持无重组散列。此外，Global-Heap可以将内存中的键值存储Redis的性能提高至少7.56倍，并且可以将以管道方式执行的图形应用程序的运行时间减少至少48%。
In-memory computing has been widely used to process data quickly in memory, but it is no longer able to cope with the current data explosion. This has occurred because modern computers have structural constraints in terms of both memory capacity and bandwidth, fundamentally limiting the amount of data that can be processed in memory. In order to overcome these limitations, the memory-centric computing concept was proposed, and as an attempt to make this concept feasible, nonvolatile memory, next-generation interconnects, and memory-centric operating system technologies are being actively studied. Although, however, there have been many studies of these concepts, the essential problem of both efficiently and safely using the extremely large memory space has not been completely solved. This paper proposes what is termed Global-Heap which solves the above essential problem, thus supporting the core technology of memory-centric computing. In this technology, a heap-level abstraction which can be switched very lightly between processes is provided in an application-friendly manner as a global resource. Global-Heap also provides an effective means by which safely to use this vast address space based on Intel memory protection keys. We implemented Global-Heap on Linux and developed three useful use cases applicable to data-centric applications. Our primary evaluation shows that Global-Heap enables reorganization-free hashing. In addition, Global-Heap can improve the performance of an in-memory key-value store, Redis, by at least 7.56 times, and it can reduce the running time of graph applications executed in a pipeline approach by at least 48%.