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

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的行为。不幸的是，在现代的高端微处理器上，即使每执行一条指令只捕获一个字节的信息，每秒也会产生一个千兆字节的数据。因此，重要的跟踪需要以压缩形式存储，以便于管理。虽然对于只捕获执行指令的pc的迹线存在良好的压缩方案，但对于包含重要附加信息（如寄存器的内容、有效地址、总线上的值等）的扩展迹线，这些方案可能无效。我们建议采用价值预测文献中的技术来压缩扩展轨迹。初步结果表明，我们的方法在最重要的路径上提供了显著提高的压缩率，即在其他算法不能很好地压缩的路径上。扩展信息很重要，因为它包含了当前许多研究工作中感兴趣的参数。为了能够在课堂和实验室中利用这类信息，需要开发高性能压缩工具并使其公开可用。这是本提案的主要目标。该工具将使学生和研究人员都能更好地理解真实的程序和处理器，并将促进这些主题的教学和学习。它将与带注释的源代码、文档和教程一起在Web上提供。此外，压缩算法的内部工作将在研究论文中进行宣传和传播，以鼓励使用和进一步研究压缩算法。

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.