Development of a Parallel Programming Environment based on Distributed Shared Arrays with Application-Specific Resource Management

我们已经开发了一个并行编程环境，称为“分布式共享数组（DSA）”，它提供了一个共享的全局数组抽象跨网络连接的不同机器。在DSA中，用户可以定义和使用全局数组，这些数组可以从网络中的任何机器统一访问。阵列区域分配、复制和迁移的显式管理是通过显式调用定义阵列区域的阵列操作、阅读和写入阵列区域、同步以及复制和迁移的控制来实现的。DSA与网格（Globus）服务集成。我们还开发了生物信息学应用，如基因聚类分析，多序列比对，分子动力学模拟。在这些应用中，全局数组分别用于存储距离矩阵、排列矩阵和原子坐标。无法存储在单个机器的存储器中的大阵列区域将由该系统提供。DSA的可扩展性能相比，在MPI中编写的传统的并行程序。

We have developed a parallel programming environment called "Distributed Shared Array (DSA)," which provides a shared global array abstract across different machines connected by a network. In DSA, users can define and use global arrays that can be accessed uniformly from any machines in the network. Explicit management of array area allocation, replication, and migration is achieved by explicit calls for array manipulation defining array regions, reading and writing array regions, synchronization, and control of replication and migration. The DSA is integrated with Grid (Globus) services. We also develop the bioinformatics applications such as gene cluster analysis, multiple sequence alignment, and molecular dynamics simulation. In these applications, global arrays are used for storing the distance matrix, alignment matrix and atom coordinates, respectively. Large array areas, which cannot be stored in the memory of individual machines, will be made available by this system. Scalable performance of DSA was obtained compared to that of conventional parallel programs written in MPI.