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Efficient Processor Allocation Policies for Massively Parallel Systems

大规模并行系统的高效处理器分配策略

基本信息

批准号：
10680336
负责人：
SHIMIZU Kentaro
金额：
$ 2.05万
依托单位：
THE UNIVERSITY OF TOKYO
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

项目摘要

We designed and implemented a new parallel programming environment called Parsley, which provides fine-grained scheduling services based on the structures of application programs. In Parsley, application programs are divided into subtasks that can run serially or in parallel. It provides a programming interface that allows a user to define subtasks and to easily specify precedence constraints among them. Parsley uses these constraints to schedule subtasks at run time. In this research project, we developed the scheduling policy and mechanism for Parsley and applied them to parallel molecular dynamics simulation program on distributed memory multiprocessor systems. The scheduling policy is automatically improved to reflect the hardware environment and resource usage. The basic policy is an incremental scheduling algorithm based on the critical path method. In this algorithm, subtask priorities are dynamically determined by using the execution time of each subtask, as monitored by the resource management facilities of Parsley. This policy improves processor utilization by 35 to 55 % compared with the FIFO scheduling policy. In addition, Parsley is useful in the heterogeneous environment (e.g. a network of different workstations and clusters) as well as the homogeneous environment. Users need not be aware of the individual performance of the computers and networks, because of the dynamic processor allocation facilities of Parsley. We have developed several resource management policies for Parsley on heterogeneous environment and evaluated the performance for molecular dynamics simulation.

我们设计并实现了一个新的并行编程环境Parsley，它根据应用程序的结构提供细粒度的调度服务。在欧芹中，应用程序被划分为可以串行或并行运行的子任务。它提供了一个编程接口，允许用户定义子任务并轻松地指定它们之间的优先约束。Parsley使用这些约束在运行时调度子任务。在本研究项目中，我们开发了欧芹的调度策略和机制，并将其应用于分布式存储多处理器系统上的并行分子动力学模拟程序。调度策略会自动改进，以反映硬件环境和资源使用情况。基本策略是基于关键路径法的增量式调度算法。在该算法中，在欧芹资源管理工具的监控下，通过使用子任务的执行时间来动态确定子任务的优先级。与FIFO调度策略相比，该策略将处理器利用率提高了35%到55%。此外，欧芹在异类环境(例如，由不同工作站和集群组成的网络)以及同构环境中也很有用。由于欧芹的动态处理器分配功能，用户不需要知道计算机和网络的单独性能。我们针对异质环境中的欧芹，提出了几种资源管理策略，并对其进行了分子动力学模拟。