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.

研究人员和教育工作者广泛使用了扩展程序tracestractrogram执行痕迹的扩展程序tracestractrogram执行跟踪的高性能压缩基础架构。 不幸的是，即使仅捕获每个执行指令的信息字节，也会根据现代高端微处理器每秒的千兆字节生成。 因此，非平凡的痕迹需要以压缩形式存储以可管理。尽管仅捕获执行指令的PC的痕迹存在良好的压缩方案，但这些方案可能在包括重要其他信息的扩展痕迹上无效，这些信息包括寄存器的内容，有效的地址，总线上的值等。我们建议我们采用价值预测文献中的技术来压缩扩展痕迹。 初步结果表明，我们的方法在最重要的轨迹上，即在其他算法无法很好地压缩的迹线上提供了大大提高的压缩率。扩展信息很重要，因为它封装了许多当前研究努力中感兴趣的参数。 为了在课堂和实验室中利用此类信息，需要开发和公开提供高性能的压缩工具。这是该提议的主要目标。 该工具将允许学生和研究人员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。 它将在网络上以及注释的源代码，文档和教程中提供。 此外，将在研究论文中宣传和传播压缩算法的内部工作，以鼓励使用以及对压缩算法的进一步研究。