Performance analysis of Message Passing Interface collective communication on intel xeon quad-core gigabit ethernet and infiniband clusters

The performance of MPI implementation operations still presents critical issues for high performance computing systems, particularly for more advanced processor technology. Consequently, this study concentrates on benchmarking MPI implementation on multi-core architecture by measuring the performance of Open MPI collective communication on Intel Xeon dual quad-core Gigabit Ethernet and InfiniBand clusters using SKaMPI. It focuses on well known collective communication routines such as MPI-Bcast, MPI-AlltoAll, MPI-Scatter and MPI-Gather. From the collection of results, MPI collective communication on InfiniBand clusters had distinctly better performance in terms of latency and throughput. The analysis indicates that the algorithm used for collective communication performed very well for all message sizes except for MPI-Bcast and MPI-Alltoall operation of inter-node communication. However, InfiniBand provides the lowest latency for all operations since it provides applications with an easy to use messaging service, compared to Gigabit Ethernet, which still requests the operating system for access to one of the server communication resources with the complex dance between an application and a network

Learning from the Success of MPI

The Message Passing Interface (MPI) has been extremely successful as a portable way to program high-performance parallel computers. This success has occurred in spite of the view of many that message passing is difficult and that other approaches, including automatic parallelization and directive-based parallelism, are easier to use. This paper argues that MPI has succeeded because it addresses all of the important issues in providing a parallel programming model.Comment: 12 pages, 1 figur

Components and Interfaces of a Process Management System for Parallel Programs

Parallel jobs are different from sequential jobs and require a different type of process management. We present here a process management system for parallel programs such as those written using MPI. A primary goal of the system, which we call MPD (for multipurpose daemon), is to be scalable. By this we mean that startup of interactive parallel jobs comprising thousands of processes is quick, that signals can be quickly delivered to processes, and that stdin, stdout, and stderr are managed intuitively. Our primary target is parallel machines made up of clusters of SMPs, but the system is also useful in more tightly integrated environments. We describe how MPD enables much faster startup and better runtime management of parallel jobs. We show how close control of stdio can support the easy implementation of a number of convenient system utilities, even a parallel debugger. We describe a simple but general interface that can be used to separate any process manager from a parallel library, which we use to keep MPD separate from MPICH.Comment: 12 pages, Workshop on Clusters and Computational Grids for Scientific Computing, Sept. 24-27, 2000, Le Chateau de Faverges de la Tour, Franc

Session-Based Programming for Parallel Algorithms: Expressiveness and Performance

This paper investigates session programming and typing of benchmark examples to compare productivity, safety and performance with other communications programming languages. Parallel algorithms are used to examine the above aspects due to their extensive use of message passing for interaction, and their increasing prominence in algorithmic research with the rising availability of hardware resources such as multicore machines and clusters. We contribute new benchmark results for SJ, an extension of Java for type-safe, binary session programming, against MPJ Express, a Java messaging system based on the MPI standard. In conclusion, we observe that (1) despite rich libraries and functionality, MPI remains a low-level API, and can suffer from commonly perceived disadvantages of explicit message passing such as deadlocks and unexpected message types, and (2) the benefits of high-level session abstraction, which has significant impact on program structure to improve readability and reliability, and session type-safety can greatly facilitate the task of communications programming whilst retaining competitive performance

MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data

In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO 1 , a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms