ITR: A High-Performance Compression Infrastructure for Extended Program Traces

ITR：用于扩展程序跟踪的高性能压缩基础设施

• 批准号: 0312966
• 负责人: Martin Burtscher
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312966&HistoricalAwards=false
• 关键词: ITR; Performance; Compression; Infrastructure; Extended

BurtscherA High-Performance Compression Infrastructure for Extended Program TracesAbstractProgram execution traces are widely used by researchers and educators to study program and processor behavior. Unfortunately, even capturing only a byte of information per executed instruction generates on the order of a gigabyte of data per second on a modern high-end microprocessor. Hence, nontrivial traces need to be stored in compressed form to be manageable. While good compression schemes exist for traces that capture only the PCs of the executed instructions, these schemes can be ineffective on extended traces that include important additional information such as the content of registers, effective addresses, values on a bus, etc. We propose to employ techniques from the value-prediction literature to compress extended traces. Preliminary results show that our approach delivers substantially improved compression rates on the traces where it matters the most, i.e., on the traces that other algorithms cannot compress well. The extended information is important because it encapsulates the parameters that are of interest in many current research endeavors. To enable the utilization of this kind of information in the classroom and the laboratory, high-performance compression tools need to be developed and made publicly available. This is the primary goal of this proposal. The tool will allow students and researchers alike to gain a better understanding of real programs and processors, and will promote the teaching and learning about these topics. It will be made available on the Web along with commented source code, documentation, and a tutorial. Moreover, the inner workings of the compression algorithm will be publicized and disseminated in research papers to encourage the usage as well as further studies of the compression algorithm.

Burtscher一种用于扩展程序跟踪的高性能压缩基础设施摘要程序执行跟踪被研究人员和教育工作者广泛用于研究程序和处理器行为。 不幸的是，即使每个执行的指令只捕获一个字节的信息，在现代高端微处理器上每秒也会产生大约十亿字节的数据。 因此，重要的跟踪需要以压缩形式存储，以便管理。虽然良好的压缩方案存在的痕迹，只捕获PC的执行指令，这些计划可能是无效的扩展痕迹，包括重要的附加信息，如寄存器的内容，有效地址，总线上的值等，我们建议采用技术的值预测文献压缩扩展痕迹。 初步结果表明，我们的方法在最重要的轨迹上提供了大幅提高的压缩率，即，其他算法无法很好地压缩的痕迹。扩展的信息是重要的，因为它封装的参数是在许多当前的研究工作的兴趣。 为了在教室和实验室利用这类信息，需要开发高性能的压缩工具并向公众提供。这是本提案的主要目标。 该工具将使学生和研究人员能够更好地了解真实的程序和处理器，并将促进有关这些主题的教学和学习。 它将与注释的源代码、文档和教程一起在Web上沿着。 此外，压缩算法的内部工作原理将在研究论文中公布和传播，以鼓励压缩算法的使用和进一步研究。