SHF: Small: Novel Techniques for Handling Memory Model Bugs

SHF：小：处理内存模型错误的新技术

• 批准号: 1319983
• 负责人: Abdullah Muzahid
• 金额: $ 24.93万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319983&HistoricalAwards=false
• 关键词: SHF; Small; Novel; Techniques; Handling

With the ubiquitous availability of parallel architectures, the burden falls on programmers' shoulders to write correct parallel programs that have high performance and portability across different platforms. Unfortunately, most programmers find it a challenging task. One of the major issues that contribute to this challenge is the intricacies involved with the underlying memory consistency models that define the order of memory operations. The situation is worsened by the fact that the memory model specifications provided by public architecture vendors are often ambiguous, difficult to use, and even incorrect. This project looks at techniques to characterize, detect and avoid bugs caused by memory models. A memory model forms the fundamental basis for writing parallel programs. If a programmer is not careful about the constraints of the underlying memory model, a parallel program might end up having subtle bugs. In one scenario, the bugs might cause a program execution to have unintuitive interleaving of instructions, which eventually leads to incorrect behavior and lack of portability. In other scenarios, the bugs might cause a significant slowdown and poor scalability of the program. Unfortunately these bugs, referred to as Memory Model Bugs, receive very little attention from the research community. Therefore, this work focuses on developing new techniques to deal with these bugs. The work will first characterize different memory model bugs in real world code bases. The findings will be useful in developing hardware and compiler techniques (e.g., new hardware modules, exceptions, static analyzers, etc.) to detect as well as avoid these bugs. Finally, the work will focus on designing new software debugging tools to help programmers get rid of these subtle bugs. The research in this proposal will enable the widespread practice of parallel programming by addressing some of the hardest concurrency bugs. On one hand, it will encourage software and hardware companies to invest in new techniques for debugging and avoiding these bugs. On the other hand, it will help make C++ and Java memory models simple and robust.

随着平行体系结构无处不在的可用性，负担落在程序员的肩膀上，编写正确的并行程序，这些程序在不同平台上具有高性能和便携性。不幸的是，大多数程序员认为这是一项具有挑战性的任务。有助于这一挑战的主要问题之一是定义内存操作顺序的基础内存一致性模型所涉及的复杂性。公共架构供应商提供的内存模型规格通常模棱两可，难以使用甚至不正确，这使情况恶化了。该项目着眼于表征，检测和避免记忆模型引起的错误的技术。内存模型构成了编写并行程序的基本基础。如果程序员对基础内存模型的限制不小心，则并行程序可能最终会出现微妙的错误。在一种情况下，这些错误可能导致程序执行具有不直觉的指令，最终导致行为不正确和缺乏便携性。在其他情况下，这些错误可能会导致该程序的可扩展性大大放缓和差。不幸的是，这些错误，称为内存模型错误，很少受到研究社区的关注。因此，这项工作着重于开发应对这些错误的新技术。这项工作将首先描述现实世界代码库中不同的内存模型错误。这些发现对于开发硬件和编译器技术（例如，新硬件模块，异常，静态分析仪等）将很有用，以检测并避免使用这些错误。最后，这项工作将着重于设计新的软件调试工具，以帮助程序员摆脱这些微妙的错误。该提案中的研究将通过解决一些最困难的并发错误来实现并行编程的广泛实践。一方面，它将鼓励软件和硬件公司投资于调试和避免这些错误的新技术。另一方面，它将有助于使C ++和Java存储器模型简单稳健。