Introduction to the Special Issue on ACM International Systems and Storage Conference (SYSTOR) 2018

G. Yadgar, Donald E. Porter
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Abstract

This issue of ACM Transactions on Storage brings some of the highlights of the 11th ACM International Systems and Storage Conference (SYSTOR’18), held in Haifa, Israel, during June 2018. SYSTOR is an international forum for interaction across the systems research community, attracting authors and participants from all over the world. Of the 44 submitted and 10 accepted papers, we invited the authors of two notable papers to extend them for publication in this special issue. The first article, which was selected as the best paper at the conference, “Lerna: Parallelizing Dependent Loops Using Speculation,” by Mohamed M. Saad, Roberto Palmieri, and Binoy Ravindran, presents a technique for using speculation to parallelize code containing data dependencies. Lerna uses a combination of static analysis and profiling to rewrite a sequential program into a parallel one and manages the concurrent execution of jobs using transactional memory. This is the first framework for executing such process automatically, without any input from the programmers. The second article, “REGISTOR: A Platform for Unstructured Data Processing Inside SSD Storage,” by Shuyi Pei, Jing Yang, and Qing Yang, presents the design, implementation, and evaluation of an in-device regular-expression engine for SSDs. The resulting smart storage avoids transfer of data through the low-bandwidth I/O bus, increasing the application-perceived throughput by up to 10×. This is a significant step toward addressing the challenges of the Big Data era.
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ACM国际系统与存储会议(SYSTOR) 2018特刊简介
本期ACM存储交易带来了2018年6月在以色列海法举行的第11届ACM国际系统和存储会议(SYSTOR ' 18)的一些亮点。SYSTOR是一个跨系统研究社区互动的国际论坛,吸引了来自世界各地的作者和参与者。在提交的44篇论文和接受的10篇论文中,我们邀请了两篇著名论文的作者在本期特刊上发表。第一篇文章被选为会议上的最佳论文,“Lerna:使用推测并行化依赖循环”,作者是Mohamed M. Saad、Roberto Palmieri和Binoy Ravindran,该文介绍了一种使用推测来并行化包含数据依赖性的代码的技术。Lerna结合了静态分析和概要分析,将顺序程序重写为并行程序,并使用事务性内存管理作业的并发执行。这是第一个自动执行此类过程的框架,无需程序员的任何输入。第二篇文章,“REGISTOR: SSD存储内部非结构化数据处理的平台”,由Shuyi Pei、Jing Yang和Qing Yang撰写,介绍了SSD设备内正则表达式引擎的设计、实现和评估。由此产生的智能存储避免了通过低带宽I/O总线传输数据,将应用程序感知的吞吐量提高了10倍。这是应对大数据时代挑战的重要一步。
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