RADAR：用于内存处理系统的抗偏斜和热度感知有序索引设计

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-07-09 DOI:10.1109/TPDS.2024.3424853

Yifan Hua;Shengan Zheng;Weihan Kong;Cong Zhou;Kaixin Huang;Ruoyan Ma;Linpeng Huang

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引用次数: 0

摘要

由于存在内存墙，指针追逐成为当今内存索引的性能瓶颈。新兴的内存处理（PIM）技术有望通过低延迟内存访问和随 PIM 模块数量增加而扩展的聚合内存带宽来缓解这一瓶颈。之前基于 PIM 的索引采用固定粒度来划分密钥空间，并在 PIM 模块之间保持跳表节点的静态高度，以加速跳表上的索引操作，但忽略了运行时数据访问模式的偏度和热度变化。在本文中，我们介绍了一种创新的 PIM 友好型跳转表 RADAR，它可以动态划分 PIM 模块之间的密钥空间，以适应不同的偏度。我们采用了一种基于离线学习的模型来捕捉热度变化，从而调整 skiplist 节点的高度。在多个数据集中，RADAR 实现了高达 198.2 倍的性能提升，在真实 PIM 硬件上的内存消耗比一流设计少 47.4%。

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RADAR: A Skew-Resistant and Hotness-Aware Ordered Index Design for Processing-in-Memory Systems

Pointer chasing becomes the performance bottleneck for today's in-memory indexes due to the memory wall. Emerging processing-in-memory (PIM) technologies are promising to mitigate this bottleneck, by enabling low-latency memory access and aggregated memory bandwidth scaling with the number of PIM modules. Prior PIM-based indexes adopt a fixed granularity to partition the key space and maintain static heights of skiplist nodes among PIM modules to accelerate index operations on skiplist, neglecting the changes in skewness and hotness of data access patterns during runtime. In this article, we present RADAR, an innovative PIM-friendly skiplist that dynamically partitions the key space among PIM modules to adapt to varying skewness. An offline learning-based model is employed to catch hotness changes to adjust the heights of skiplist nodes. In multiple datasets, RADAR achieves up to 198.2x performance improvement and consumes 47.4% less memory than state-of-the-art designs on real PIM hardware.

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.