Efficient Memory Footprint Reduction for Java Performance

有效减少内存占用以提高 Java 性能

• 批准号: 531328-2018
• 负责人: Pekhimenko, Gennady
• 金额: $ 9.91万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699064
• 关键词: Efficient; Memory; Footprint; Reduction; Java

Our prior work on data compression significantly advanced this subfield of computer architecture, and also showed the significant potential of applying optimized data compression across the whole systems stack (from caches to memory). As it commonly happens, our results also highlighted some completely new problems and opportunities. First of all, to be efficient, data compression needs to be very fast, especially decompression part as it is frequently on the critical execution path. Second, compressibility varies a lot for different applications, but still is generic enough to be interesting and is observed in 60% to 75% of different applications for both CPU and GPGPU applications. Third, to be efficient in memory hierarchy, it is better to use domain-specific or pattern-specific compression algorithms that might sacrifice some compression ratio for acceptable overhead of compression/decompression. In this work, we aim to apply our experience on data compression to a new area where the current application of data compression was very limited so far - just-in-time (JIT) compilation. Based on our initial discussion, there is a strong indication that memory footprint is an important concern for Java performance. In our project, we aim to address this problem using data compression. We envision several major directions in our research. First, we would like to collect a set of representative Java applications and generate their memory dumps that would include both the JIT metadata, compiled objects, and the heap. Second, we will perform an extensive analysis of existing compression algorithms that might be useful for each for the major contributors to the memory consumption. Third, we will look for potential domain-specific encodings that will improve compressibility of Java applications even further. Fourth, we will investigate a potential of running execution directly on compressed data when possible.

我们之前在数据压缩方面的工作大大推进了计算机体系结构的这一子领域，也显示了在整个系统堆栈（从缓存到内存）中应用优化数据压缩的巨大潜力。正如通常发生的那样，我们的研究结果也突出了一些全新的问题和机会。首先，为了提高效率，数据压缩需要非常快，特别是解压缩部分，因为它经常在关键执行路径上。其次，可压缩性因不同的应用程序而变化很大，但仍然足够通用，并且在60%到75%的CPU和GPGPU应用程序中观察到。第三，为了在存储器层次结构中有效，最好使用特定于域或特定于模式的压缩算法，这些算法可能会牺牲一些压缩比以获得可接受的压缩/解压缩开销。 在这项工作中，我们的目标是将我们的经验，数据压缩到一个新的领域，目前的应用程序的数据压缩是非常有限的，到目前为止-即时（JIT）编译。根据我们最初的讨论，有一个强烈的迹象表明，内存占用是Java性能的一个重要问题。在我们的项目中，我们的目标是使用数据压缩来解决这个问题。我们在研究中设想了几个主要方向。首先，我们希望收集一组有代表性的Java应用程序，并生成它们的内存转储，其中包括JIT元数据、编译对象和堆。其次，我们将对现有的 压缩算法，这可能是有用的每一个主要贡献者的内存消耗。第三，我们将寻找潜在的特定于领域的编码，这些编码将进一步提高Java应用程序的可压缩性。第四，我们将研究在可能的情况下直接对压缩数据运行执行的可能性。