Collaborative Research: CSR-PSCE, SM: Adaptive Memory Management in Shared Environments

合作研究：CSR-PSCE、SM：共享环境中的自适应内存管理

• 批准号: 0834566
• 负责人: Chen Ding
• 金额: $ 23.46万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0834566&HistoricalAwards=false
• 关键词: Collaborative; Research; CSR; PSCE; SM

Program performance is highly dependent on the amount of memory available to the program. In traditional computing systems, the memory working set of an application has a bounded size - providing more memory to an application improves performance until its working set is met. Once the working set is met, additional memory yields little or no benefit. However, in the presence of garbage collection (a technique for memory management where space that is unlikely to be reused by an application is automatically reclaimed), the relationship between program performance and memory allocation is more complex. Data is managed at three levels: the compiler manages data objects at the program level, the garbage collector manages the heap at the virtual machine level, and the virtual memory manager manages virtual memory at the operating system level. The middle layer plays a critical role. Increasing an application's heap size can reduce the frequency of garbage collections and improve performance, but too large a heap may trigger paging and degrade performance.Software developers take advantage of garbage collection (GC) for the many benefits it provides by using either garbage-collecting languages, such as Java and C#, or conventional languages (e.g., C and C++) augmented with conservative garbage collectors. While a conventional program uses exactly as much memory as it needs, the memory use of a garbage-collected program can be adjusted by changing the size of the heap used by the garbage collector. This difference can allow an advanced execution system to control applications' memory demands in response to the changing amount of available memory in a shared environment. This concept is increasingly important for today's multicore, multiprocessor machines.Building on previous work, this project develops the technology required to model the memory demand of garbage-collected programs and enable adaptive management in existing virtual machines and operating systems. Specifically, the project extends the PIs' work on whole-program locality and phase models and adaptive memory management, combining program analysis, garbage collection control, and on-line system monitoring.This work develops program-level adaptive memory management (PAMM) for garbage-collected programs running concurrently with other garbage-collected programs and with conventional applications. The goal is to adjust all applications' demands to fully use available memory and avoid contention from periods of over demand.

程序的性能高度依赖于程序可用的内存量。在传统的计算系统中，应用程序的内存工作集具有有限的大小——为应用程序提供更多的内存可以提高性能，直到满足其工作集。一旦满足了工作集，额外的内存几乎没有好处。然而，在存在垃圾收集（一种内存管理技术，其中应用程序不太可能重用的空间被自动回收）的情况下，程序性能和内存分配之间的关系更加复杂。数据在三个级别上进行管理：编译器在程序级别管理数据对象，垃圾收集器在虚拟机级别管理堆，虚拟内存管理器在操作系统级别管理虚拟内存。中间层起着关键作用。增加应用程序的堆大小可以减少垃圾收集的频率并提高性能，但是过大的堆可能会触发分页并降低性能。通过使用垃圾收集语言（如Java和c#）或常规语言（如C和c++）增强了保守的垃圾收集器，软件开发人员可以利用垃圾收集（GC）提供的许多好处。虽然传统程序使用的内存与它所需要的完全相同，但是垃圾收集程序的内存使用可以通过更改垃圾收集器使用的堆的大小来调整。这种差异允许高级执行系统根据共享环境中可用内存量的变化来控制应用程序的内存需求。这个概念对于今天的多核、多处理器机器来说越来越重要。在先前工作的基础上，该项目开发了对垃圾收集程序的内存需求进行建模所需的技术，并在现有虚拟机和操作系统中实现自适应管理。具体而言，该项目扩展了pi在整个程序局部和阶段模型以及自适应内存管理方面的工作，结合了程序分析，垃圾收集控制和在线系统监控。这项工作为与其他垃圾收集程序和传统应用程序并发运行的垃圾收集程序开发了程序级自适应内存管理（PAMM）。目标是调整所有应用程序的需求，以充分利用可用内存，并避免在需求过剩期间发生争用。