CSR: Medium: Collaborative Research: Fast and Simple Concurrency Through Data-Abstraction Transactions

CSR：媒介：协作研究：通过数据抽象事务实现快速而简单的并发

• 批准号: 1513447
• 负责人: Barbara Liskov
• 金额: $ 22.66万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513447&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; Fast

This research project has the goal of making easier to build fast software for today's multicore computers. The computer revolution depends on software whose performance continually improves. When software performance stalls, it becomes much harder to solve big scientific problems (such as genome analysis or climate modeling), and harder to continue running the systems that are important to our society. Modern software must simultaneously use many processors running in parallel. This is much harder than using a single processor: coordination issues can cause serious errors, and methods that avoid errors tend to run slowly. This research shows that a new system, called data-abstraction software transactional memory, can avoid errors without performance compromise.Data-abstraction software transaction memory is a kind of software transactional memory (STM). The STM design allows software programs to run arbitrary transactions on memory objects. Since transactions are inherently isolated, a transaction-based program can avoid much of the detailed, error-prone reasoning about coordination that plagues ad-hoc parallel programs. Data-abstraction STM advances on conventional STM by tracking changes at the level of abstract methods on data objects, rather than memory words. The resulting system has the same ease of programming as conventional STM, but performs much faster. Where a conventional STM might track hundreds of memory words to ensure transaction isolation, a data-abstraction STM can track single words, or even nothing at all, since data abstractions can intelligently track the minimal information required to preserve their semantics.The research project builds a data-abstraction software transactional memory and uses it to construct several fast software applications. These applications, including a world-class-speed database, achieve strong transactional guarantees, and great performance, while remaining easy to program. Programmers using data-abstraction STMs will be able to build more high-performance software, with less potential for error, on today's best and most efficient hardware, helping us bring the benefits of the computer revolution to more aspects of modern life.

这个研究项目的目标是使为今天的多核计算机构建快速软件变得更容易。计算机革命依赖于性能不断提高的软件。当软件性能停滞时，解决重大科学问题（如基因组分析或气候建模）变得更加困难，并且更难继续运行对我们社会重要的系统。 现代软件必须同时使用多个并行运行的处理器。这比使用单个处理器要困难得多：协调问题可能导致严重的错误，而避免错误的方法往往运行缓慢。研究表明，一种新的系统--数据抽象软件事务存储器（Data-Abstraction Software Transactional Memory，简称STM）可以在不影响性能的前提下避免错误的发生。STM设计允许软件程序在内存对象上运行任意事务。由于事务本身是孤立的，因此基于事务的程序可以避免许多困扰ad-hoc并行程序的关于协调的详细的、容易出错的推理。数据抽象STM通过跟踪数据对象的抽象方法而不是内存字的变化，在传统STM的基础上取得了进步。由此产生的系统具有与传统STM相同的编程容易性，但执行速度更快。传统的STM可能会跟踪数百个存储器字，以确保事务隔离，数据抽象STM可以跟踪单个字，甚至什么都没有，因为数据抽象可以智能地跟踪所需的最小信息，以保持其semantics.The研究项目建立了一个数据抽象软件事务内存，并使用它来构建几个快速的软件应用程序。这些应用程序，包括世界级速度的数据库，实现了强大的事务保证和出色的性能，同时保持易于编程。使用数据抽象STM的程序员将能够在当今最好、最高效的硬件上构建更高性能、错误可能性更小的软件，帮助我们将计算机革命的好处带到现代生活的更多方面。