SHF:Small: Accurate and Computationally Efficient Predictors of Java Memory Resource Consumption

SHF:Small：Java 内存资源消耗的准确且计算高效的预测器

• 批准号: 1320498
• 负责人: J. Eliot Moss
• 金额: $ 45万
• 依托单位: University of Massachusetts Amherst
• 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=1320498&HistoricalAwards=false
• 关键词: SHF; Small; Accurate; Computationally; Efficient

The Java programming language is widely-used and of great commercial and economic significance. It is favored in part because it features automatic management of the computer memory resources it uses, simplifying such management for the programmer. Memory management in Java (and other managed languages) has reached a plateau in cost and effectiveness because most current techniques are tuned based on a small number of coarse-grained measures gathered while programs run. Substantial improvement might be gained from using more accurate estimation of current and near-future memory use to drive better memory management decisions. This would reduce the time, memory, and energy requirements to run Java programs. This is of significance to the full range of Java applications from small embedded systems through laptops and desktops to large servers. There is therefore an urgent need for techniques to derive better online predictors of Java memory use.The long-term goal of the research program this award will support is to substantially improve memory allocation and garbage collection effectiveness by using better online predictors to drive more sophisticated allocator and collector decisions. The objective of this particular project is to develop machine learning techniques that induce accurate and computationally efficient predictors of characteristics of Java memory allocation that influence memory manager performance. Examples include predicting the volume of objects that become "garbage" (can be reclaimed and reused for future allocations), as well as objects that will be in use for a long time and will not become garbage soon. The approach is to learn models that predict memory usage based on features compiled from observable run-time events like calls to particular methods or allocations of certain objects. Data to learn models will be obtained from analysis of detailed program execution traces. Features will be selected that are both informative of memory use and computable with low space and time overheads. Programs will then be modified to compute these features as they run, and real-time predictive models will be used to predict future memory usage as programs execute. These predictions will be used to improve memory management performance. This will be accomplished by, for example, improving the timing of garbage collection so that it occurs at points during program execution that result in higher memory reclamation with lower effort.

Java编程语言被广泛使用，具有重要的商业和经济意义。它受欢迎的部分原因是它具有自动管理所使用的计算机内存资源的特点，简化了程序员的管理。Java（和其他托管语言）中的内存管理在成本和效率方面已经达到了一个平稳期，因为大多数当前技术都是基于程序运行时收集的少量粗粒度度量进行调优的。通过对当前和近期的内存使用情况进行更准确的估计，从而推动更好的内存管理决策，可能会获得实质性的改进。这将减少运行Java程序所需的时间、内存和能量。这对于从小型嵌入式系统到笔记本电脑和台式机再到大型服务器的所有Java应用程序都具有重要意义。因此，迫切需要一种技术来派生出更好的Java内存使用在线预测器。该奖项将支持的研究项目的长期目标是通过使用更好的在线预测器来驱动更复杂的分配器和收集器决策，从而大幅提高内存分配和垃圾收集的效率。这个特定项目的目标是开发机器学习技术，以诱导对影响内存管理器性能的Java内存分配特征进行准确且计算高效的预测。示例包括预测成为“垃圾”的对象的数量（可以回收并在将来分配时重用），以及将长时间使用但不会很快成为垃圾的对象。该方法是学习基于从可观察的运行时事件（如对特定方法的调用或某些对象的分配）编译的特性来预测内存使用的模型。学习模型的数据将从分析详细的程序执行轨迹中获得。将选择既具有内存使用信息又具有低空间和时间开销的可计算特性。然后，程序将被修改以在运行时计算这些特性，实时预测模型将用于在程序执行时预测未来的内存使用情况。这些预测将用于提高内存管理性能。这可以通过改进垃圾收集的时间来实现，例如，在程序执行期间的某个点进行垃圾收集，从而以更低的工作量获得更高的内存回收。