A resource query interface for network-aware applications

  • Authors:
  • Bruce Lowekamp;Nancy Miller;Thomas Gross;Peter Steenkiste;Jaspal Subhlok;Dean Sutherland

  • Affiliations:
  • School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA

  • Venue:
  • Cluster Computing
  • Year:
  • 1999

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Abstract

Networked systems provide a cost-effective platform for parallel computing, but the applications have to deal with the changing availability of computation and communication resources. Network-awareness is a recent attempt to bridge the gap between the realities of networks and the demands of applications. Network-aware applications obtain information about their execution environment and dynamically adapt to enhance their performance. Adaptation is especially important for synchronous parallel applications because a single busy communication link can become the bottleneck and degrade overall performance dramatically. This paper presents Remos, a uniform API that allows applications to obtain relevant network information, and reports on the development of parallel applications in this environment. The challenges in defining a uniform interface include network heterogeneity, diversity and variability in network traffic, and resource sharing in the network and even inside an application. The first implementation of the Remos interface uses SNMP to monitor IP-based networks. This paper reports on our methodology for developing adaptive parallel applications for high-speed networks with Remos and presents experimental results using applications generated by the Fx parallelizing compiler. The results highlight the importance and effectiveness of adaptive parallel computing.