Legislators' guide to local governments : general purpose units /

Includes a listing by subject of the general purpose powers granted to local governments in Illinois. Gives a brief description of the powers with citations to the Illinois Compiled Statutes.

Organizational, direct support maintenance repair parts and special tools list : tent, general purpose, small, FSN 8340-753-6471, medium, FSN 8340-543-7788, large, FSN 8340-285-5599.

Includes index.

Operator, unit, and direct support maintenance manual for tent, general purpose, small, NSN 8340-00-470-2335, medium, NSN 8340-00-482-3963, large, NSN 8340-00-470-2342.

"16 September 1990."

Direct support, and general support, maintenance repair parts and special tools list : shop equipment, general purpose repair, semitrailer mounted (southwest truck body model SGPRSMD) (serial no. Range S-11-204 through S-11-265) (4940-01-006-3229).

"May 1982."

Operator's, organizational direct support, general support and depot maintenance manual : welding set, arc, inert gas shielded, water cooled, aluminum welding, general purpose, (Airco model 2351 1209), FSN 3431-731-4163.

"June 1969."

Organizational and direct support maintenance repair parts and special tools list : welding set, arc, inert gas shielded, water cooled, aluminum welding, general purpose (Airco model 23511209), FSN 3431-731-4163.

"December 1970."

Unit maintenance repair parts and special tools lists : loader, scoop type, DED, 4 x 4, articulated frame steer, 4 1/2 to 5 cu. yd., (CCE), Army model H100C RB, type I with 4 1/2 cu. yd. rock bucket, NSN 3805-01-052-9042, Army model H100C GPB, type II with 5 cu. yd. general purpose bucket, NSN 3805-01-052-9043.

Includes indexes.

Unit maintenance repair parts and special tools list : loader, scoop type, DED 4 x 4, articulated frame steer, 4 1/2 to 5 cu. yd., (CCE), with 4 1/2 cu. yd. rock bucket, NSN 3805-00-602-5006, Clark model 175B, type I and with 5 cu. yd. general purpose bucket, NSN 3805-00-602-5013 Clark model 175B, type II.

Includes indexes.

Operator, organizational, and direct support maintenance manual : tent, general purpose : small, FSN 8340-753-6571, medium, FSN 8340-543-7788, large, FSN 8340-285-5599.

Includes index.

A general-purpose tunable landscape generator

The research literature on metalieuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metalieuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.

A Scalable General Purpose System for Large-Scale Graph Processing

Graph analytics is an important and computationally demanding class of data analytics. It is essential to balance scalability, ease-of-use and high performance in large scale graph analytics. As such, it is necessary to hide the complexity of parallelism, data distribution and memory locality behind an abstract interface. The aim of this work is to build a scalable graph analytics framework that does not demand significant parallel programming experience based on NUMA-awareness.
The realization of such a system faces two key problems:
(i)~how to develop a scale-free parallel programming framework that scales efficiently across NUMA domains; (ii)~how to efficiently apply graph partitioning in order to create separate and largely independent work items that can be distributed among threads.

BarraCUDA - a fast short read sequence aligner using graphics processing units.

BACKGROUND: With the maturation of next-generation DNA sequencing (NGS) technologies, the throughput of DNA sequencing reads has soared to over 600 gigabases from a single instrument run. General purpose computing on graphics processing units (GPGPU), extracts the computing power from hundreds of parallel stream processors within graphics processing cores and provides a cost-effective and energy efficient alternative to traditional high-performance computing (HPC) clusters. In this article, we describe the implementation of BarraCUDA, a GPGPU sequence alignment software that is based on BWA, to accelerate the alignment of sequencing reads generated by these instruments to a reference DNA sequence. FINDINGS: Using the NVIDIA Compute Unified Device Architecture (CUDA) software development environment, we ported the most computational-intensive alignment component of BWA to GPU to take advantage of the massive parallelism. As a result, BarraCUDA offers a magnitude of performance boost in alignment throughput when compared to a CPU core while delivering the same level of alignment fidelity. The software is also capable of supporting multiple CUDA devices in parallel to further accelerate the alignment throughput. CONCLUSIONS: BarraCUDA is designed to take advantage of the parallelism of GPU to accelerate the alignment of millions of sequencing reads generated by NGS instruments. By doing this, we could, at least in part streamline the current bioinformatics pipeline such that the wider scientific community could benefit from the sequencing technology.BarraCUDA is currently available from http://seqbarracuda.sf.net.

GPU-accelerated optimization of fuel treatments for mitigating wildfire hazard

Fuel treatment is considered a suitable way to mitigate the hazard related to potential wildfires on a landscape. However, designing an optimal spatial layout of treatment units represents a difficult optimization problem. In fact, budget constraints, the probabilistic nature of fire spread and interactions among the different area units composing the whole treatment, give rise to challenging search spaces on typical landscapes. In this paper we formulate such optimization problem with the objective of minimizing the extension of land characterized by high fire hazard. Then, we propose a computational approach that leads to a spatially-optimized treatment layout exploiting Tabu Search and General-Purpose computing on Graphics Processing Units (GPGPU). Using an application example, we also show that the proposed methodology can provide high-quality design solutions in low computing time. © 2013 The Authors. Published by Elsevier B.V.

Using GPU to exploit parallelism on cryptography

In this article we explore the NVIDIA graphical processing units (GPU) computational power in cryptography using CUDA (Compute Unified Device Architecture) technology. CUDA makes the general purpose computing easy using the parallel processing presents in GPUs. To do this, the NVIDIA GPUs architectures and CUDA are presented, besides cryptography concepts. Furthermore, we do the comparison between the versions executed in CPU with the parallel version of the cryptography algorithms Advanced Encryption Standard (AES) and Message-digest Algorithm 5 (MD5) wrote in CUDA. © 2011 AISTI.

Técnicas de paralelização em GPGPU aplicadas em algoritmo para remoção de ruído multiplicativo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)