Z-rays

Author:

Sartor Jennifer B.1,Blackburn Stephen M.2,Frampton Daniel2,Hirzel Martin3,McKinley Kathryn S.1

Affiliation:

1. University of Texas at Austin, Austin, TX, USA

2. Australian National University, Canberra, Australia

3. IBM Watson Research Center, Hawthorne, NY, USA

Abstract

Arrays are the ubiquitous organization for indexed data. Throughout programming language evolution, implementations have laid out arrays contiguously in memory. This layout is problematic in space and time. It causes heap fragmentation, garbage collection pauses in proportion to array size, and wasted memory for sparse and over-provisioned arrays. Because of array virtualization in managed languages, an array layout that consists of indirection pointers to fixed-size discontiguous memory blocks can mitigate these problems transparently. This design however incurs significant overhead, but is justified when real-time deadlines and space constraints trump performance. This paper proposes z-rays , a discontiguous array design with flexibility and efficiency. A z-ray has a spine with indirection pointers to fixed-size memory blocks called arraylets , and uses five optimizations: (1) inlining the first N array bytes into the spine, (2) lazy allocation, (3) zero compression, (4) fast array copy, and (5) arraylet copy-on-write. Whereas discontiguous arrays in prior work improve responsiveness and space efficiency, z-rays combine time efficiency and flexibility. On average, the best z-ray configuration performs within 12.7% of an unmodified Java Virtual Machine on 19 benchmarks, whereas previous designs have two to three times higher overheads. Furthermore, language implementers can configure z-ray optimizations for various design goals. This combination of performance and flexibility creates a better building block for past and future array optimization.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Memento mori: dynamic allocation-site-based optimizations;ACM SIGPLAN Notices;2016-01-28

2. Memento mori: dynamic allocation-site-based optimizations;Proceedings of the 2015 International Symposium on Memory Management;2015-06-14

3. Exploiting array manipulation habits to optimize garbage collection and type flow analysis;Software: Practice and Experience;2014-11-24

4. Pacman;ACM SIGPLAN Notices;2013-12-04

5. Using managed runtime systems to tolerate holes in wearable memories;ACM SIGPLAN Notices;2013-06-23

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