Efficient algorithms for reduction operations across a group of processes are crucial for good performance in many large-scale, parallel scientific applications. While previous algorithms limit processing to the host CPU, we utilize the programmable processors and local memory available on modern cluster network interface cards (NICs) to explore a new dimension in the design of reduction algorithms. In this paper, we present the benefits and challenges, design issues and solutions, analytical models, and experimental evaluations of a family of NIC-based reduction algorithms. Performance and scalability evaluations were conducted on the ASCI Linux Cluster (ALC), a 960-node, 1920-processor machine at Lawrence …
continued below
Publisher Info:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA
Place of Publication:
Livermore, California
Provided By
UNT Libraries Government Documents Department
Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.
Descriptive information to help identify this article.
Follow the links below to find similar items on the Digital Library.
Description
Efficient algorithms for reduction operations across a group of processes are crucial for good performance in many large-scale, parallel scientific applications. While previous algorithms limit processing to the host CPU, we utilize the programmable processors and local memory available on modern cluster network interface cards (NICs) to explore a new dimension in the design of reduction algorithms. In this paper, we present the benefits and challenges, design issues and solutions, analytical models, and experimental evaluations of a family of NIC-based reduction algorithms. Performance and scalability evaluations were conducted on the ASCI Linux Cluster (ALC), a 960-node, 1920-processor machine at Lawrence Livermore National Laboratory, which uses the Quadrics QsNet interconnect. We find NIC-based reductions on modern interconnects to be more efficient than host-based implementations in both scalability and consistency. In particular, at large-scale--1812 processes--NIC-based reductions of small integer and floating-point arrays provided respective speedups of 121% and 39% over the host-based, production-level MPI implementation.
Journal Name: International Journal of High Performance Computing and Networking, vol. 4, no. 3-2, June 1, 2006, pp. 122-136; Journal Volume: 4; Journal Issue: 3-2
This article is part of the following collection of related materials.
Office of Scientific & Technical Information Technical Reports
Reports, articles and other documents harvested from the Office of Scientific and Technical Information.
Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.
Petrini, F.; Moody, A. T.; Fernandez, J.; Frachtenberg, E. & Panda, D. K.NIC-based Reduction Algorithms for Large-scale Clusters,
article,
July 30, 2004;
Livermore, California.
(https://digital.library.unt.edu/ark:/67531/metadc887533/:
accessed May 15, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT Libraries Government Documents Department.
