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MatlabMPI

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Published in:
Journal of Parallel and Distributed Computing, Vol. 64, No. 8, pp. 997-1005.

Summary

In many projects the true costs of high performance computing are currently dominated by software. Addressing these costs may require shifting to higher level languages such as Matlab. MatlabMPI is a Matlab implementation of the Message Passing Interface (MPI) standard and allows any Matlab program to exploit multiple processors. MatlabMPI currently implements the basic six functions that are the core of the MPI point-to-point communications standard. The key technical innovation of MatlabMPI is that it implements the widely used MPI “look and feel” on top of standard Matlab file I/O, resulting in an extremely compact (?350 lines of code) and “pure” implementation which runs anywhere Matlab runs, and on any heterogeneous combination of computers. The performance has been tested on both shared and distributed memory parallel computers (e.g. Sun, SGI, HP, IBM, Linux, MacOSX and Windows). MatlabMPI can match the bandwidth of C based MPI at large message sizes. A test image filtering application using MatlabMPI achieved a speedup of ?300 using 304 CPUs and ?15% of the theoretical peak (450 Gigaflops) on an IBM SP2 at the Maui High Performance Computing Center. In addition, this entire parallel benchmark application was implemented in 70 software-lines-of-code, illustrating the high productivity of this approach. MatlabMPI is available for download on the web.
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Summary

In many projects the true costs of high performance computing are currently dominated by software. Addressing these costs may require shifting to higher level languages such as Matlab. MatlabMPI is a Matlab implementation of the Message Passing Interface (MPI) standard and allows any Matlab program to exploit multiple processors. MatlabMPI...

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Cluster Computing for Embedded/Real-Time Systems

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Published in:
Cluster Computing White Paper

Summary

Cluster computing is not a new area of computing. It is, however, evident that there is agrowing interest in its usage in all areas where applications have traditionally used parallelor distributed computing platforms. The mounting interest has been fuelled in part by theavailability of powerful microprocessors and high-speed networks as off-the-shelf commoditycomponents as well as in part by the rapidly maturing software components available tosupport high performance and high availability applications.This rising interest in clusters led to the formation of an IEEE Computer Society Task Forceon Cluster Computing (TFCC1) in early 1999. An objective of the TFCC was to act both as amagnet and a focal point for all cluster computing related activities. As such, an earlyactivity that was deemed necessary was to produce a White Paper on cluster computing andits related technologies.Generally a White Paper is looked upon as a statement of policy on a particular subject. Theaim of this White Paper is to provide a relatively unbiased report on the existing, new andemerging technologies as well as the surrounding infrastructure deemed important to thecluster computing community. This White Paper is essentially a snapshot of cluster-relatedtechnologies and applications in year 2000.This White Paper provides an authoritative review of all the hardware and softwaretechnologies that can be used to make up a cluster now or in the near future. Thesetechnologies range from the network level, through the operating system and middlewarelevels up to the application and tools level. The White Paper also tackles the increasinglyimportant areas of High Availability and Embedded/Real Time applications, which are bothconsidered crucial areas for future clusters.The White Paper has been broken down into twelve chapters, each of which has been puttogether by academics and industrial researchers who are both experts in their fields andwhere willing to volunteer their time and effort to put together this White Paper.On a personal note, I would like to thank all the contributing authors for finding the time toput the effort into their chapters and making the overall paper an excellent state-of-the-artreview of clusters. In addition, I would like to thank the reviewers for their timely comments.
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Summary

Cluster computing is not a new area of computing. It is, however, evident that there is agrowing interest in its usage in all areas where applications have traditionally used parallelor distributed computing platforms. The mounting interest has been fuelled in part by theavailability of powerful microprocessors and high-speed networks as...

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The Vector, Signal, and Image Processing Library (VSIPL): an Open Standard for Astronomical Data Processing

Published in:
Bulletin of the American Astronomical Society, Vol. 31, p.1497

Summary

The Vector/Signal/Image Processing Library (VSIPL) is a DARPA initiated effort made up of industry, government and academic representatives who have defined an industry standard API for vector, signal, and image processing primitives for real-time signal processing on high performance systems. VSIPL supports a wide range of data types (int, float, complex, ...) and layouts (vectors, matrices and tensors) and is ideal for astronomical data processing. The VSIPL API is intended to serve as an open, vendor-neutral, industry standard interface. The object-based VSIPL API abstracts the memory architecture of the underlying machine by using the concept of memory blocks and views. Early experiments with VSIPL code conversions have been carried out by the High Performance Computing Program team at the UCSD. Commercially, several major vendors of signal processors are actively developing implementations. VSIPL has also been explicitly required as part of a recent Rome Labs teraflop procurement. This poster presents the VSIPL API, its functionality and the status of various implementations.
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Summary

The Vector/Signal/Image Processing Library (VSIPL) is a DARPA initiated effort made up of industry, government and academic representatives who have defined an industry standard API for vector, signal, and image processing primitives for real-time signal processing on high performance systems. VSIPL supports a wide range of data types (int, float...

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