656 research outputs found
Spartan Daily January 28, 2013
Volume 140, Issue 2https://scholarworks.sjsu.edu/spartandaily/1369/thumbnail.jp
Approaches for MATLAB Applications Acceleration Using High Performance Reconfigurable Computers
A lot of raw computing power is needed in many scientific computing applications and simulations. MATLAB®†is one of the popular choices as a language for technical computing. Presented here are approaches for MATLAB based applications acceleration using High Performance Reconfigurable Computing (HPRC) machines. Typically, these are a cluster of Von Neumann architecture based systems with none or more FPGA reconfigurable boards. As a case study, an Image Correlation Algorithm has been ported on this architecture platform. As a second case study, the recursive training process in an Artificial Neural Network (ANN) to realize an optimum network has been accelerated, by porting it to HPC Systems. The approaches taken are analyzed with respect to target scenarios, end users perspective, programming efficiency and performance. Disclaimer: Some material in this text has been used and reproduced with appropriate references and permissions where required. †MATLAB® is a registered trademark of The Mathworks, Inc. ©1994-2003
Military Transformation and the Defense Industry after Next
Though still adjusting to the end of the Cold War, the defense industry is now confronted with the prospect of military transformation. Since the terrorist attacks on 11 September 2001, many firms have seen business improve in response to the subsequent large increase in the defense budget. But in the longer run, the defense sector\u27s military customers intend to reinvent themselves for a future that may require the acquisition of unfamiliar weapons and support systems.https://digital-commons.usnwc.edu/usnwc-newport-papers/1016/thumbnail.jp
Automatic parallelization of array-oriented programs for a multi-core machine
Abstract We present the work on automatic parallelization of array-oriented programs for multi-core machines. Source programs written in standard APL are translated by a parallelizing APL-to-C compiler into parallelized C code, i.e. C mixed with OpenMP directives. We describe techniques such as virtual operations and datapartitioning used to effectively exploit parallelism structured around array-primitives. We present runtime performance data, showing the speedup of the resulting parallelized code, using different numbers of threads and different problem sizes, on a 4-core machine, for several examples
Hybrid analysis of memory references and its application to automatic parallelization
Executing sequential code in parallel on a multithreaded machine has been an
elusive goal of the academic and industrial research communities for many years. It
has recently become more important due to the widespread introduction of multicores
in PCs. Automatic multithreading has not been achieved because classic, static
compiler analysis was not powerful enough and program behavior was found to be, in
many cases, input dependent. Speculative thread level parallelization was a welcome
avenue for advancing parallelization coverage but its performance was not always optimal
due to the sometimes unnecessary overhead of checking every dynamic memory
reference.
In this dissertation we introduce a novel analysis technique, Hybrid Analysis,
which unifies static and dynamic memory reference techniques into a seamless compiler
framework which extracts almost maximum available parallelism from scientific
codes and incurs close to the minimum necessary run time overhead. We present how
to extract maximum information from the quantities that could not be sufficiently
analyzed through static compiler methods, and how to generate sufficient conditions
which, when evaluated dynamically, can validate optimizations.
Our techniques have been fully implemented in the Polaris compiler and resulted
in whole program speedups on a large number of industry standard benchmark applications
The Utah Statesman, January 11, 2010
Weekly student newspaper of Utah State University in Logan.https://digitalcommons.usu.edu/newspapers/1358/thumbnail.jp
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