Using the PlayStation3 for speeding up metaheuristic optimization

Traditional computer software is written for serial computation. To solve an optimization problem, an algorithm or metaheuristic is constructed and implemented as a serial stream of instructions. These instructions are executed on a central processing unit (CPU) on one computer. Parallel computing uses multiple processing elements simultaneously to solve a problem. This is accomplished by breaking the problem into independent parts so that each processing element can execute its part of the algorithm simultaneously with the others. The processing elements can be diverse and include resources such as a single computer with multiple processors, several networked computers, specialized hardware, or any combination of the above. Today most commodity CPU designs include single instructions for some vector processing on multiple (vectorized) data sets, typically known as SIMD (Single Instruction, Multiple Data). Modern video game consoles and consumer computer-graphics hardware rely heavily on vector processing in their architecture. In 2000, IBM, Toshiba and Sony collaborated to create the Cell Broadband Engine (Cell BE), consisting of one traditional microprocessor (called the Power Processing Element or PPE) and eight SIMD co-processing units, or the so-called Synergistic Processor Elements (SPEs), which found use in the Sony PlayStation3 among other applications The computational power of the Cell BE orPlayStation3 can also be used for scientific computing. Examples and applications have been reported in e.g. Kurzak et al. (2008), Bader et al. (2008), Olivier et al. (2007), Petrini et al. (2007). In this work, the potential of using the PlayStation3 for speeding up metaheuristic optimization is investigated. More specifically, we propose an adaptation of an evolutionary algorithm with embedded simulation for inspection optimization, developed in Van Volsem et al. (2007), Van Volsem (2009a) and Van Volsem (2009b

Statistical analysis of network data and evolution on GPUs: High-performance statistical computing

Network analysis typically involves as set of repetitive tasks that are particularly amenable to poor-man's parallelization. This is therefore an ideal application are for GPU architectures, which help to alleviate the tedium inherent to statistically sound analysis of network data. Here we will illustrate the use of GPUs in a range of applications, which include percolation processes on networks, the evolution of protein-protein interaction networks, and the fusion of different types of biomedical and disease data in the context of molecular interaction networks. We will pay particular attention to the numerical performance of different routines that are frequently invoked in network analysis problems. We conclude with a review over recent developments in the generation of random numbers that address the specific requirements posed by GPUs and high-performance computing needs

An exploration of CUDA and CBEA for a gravitational wave data-analysis application (Einstein@Home)

We present a detailed approach for making use of two new computer hardware architectures -- CBEA and CUDA -- for accelerating a scientific data-analysis application (Einstein@Home). Our results suggest that both the architectures suit the application quite well and the achievable performance in the same software developmental time-frame, is nearly identical.Comment: Accepted for publication in International Conference on Parallel Processing and Applied Mathematics (PPAM 2009

Design knowledge capture for the space station

The benefits of design knowledge availability are identifiable and pervasive. The implementation of design knowledge capture and storage using current technology increases the probability for success, while providing for a degree of access compatibility with future applications. The space station design definition should be expanded to include design knowledge. Design knowledge should be captured. A critical timing relationship exists between the space station development program, and the implementation of this project

Strange Bedfellows in the Personal Computer Industry: Technology Alliances between IBM and Apple

Until recently technological development in the personal computer industry could be characterized by the competition between two basic designs. The current dominant design in this industry is associated with the IBM and Microsoft personal computing architecture. The other version of personal computing originated in the Macintosh computer from Apple Computer Company. In recent years we also see an increasing number of alliances between IBM and Apple. Joint technological development appears to be a major and somewhat surprising objective of these alliances. This paper analyzes the technology alliances between these companies in the context of recent technological changes, focusing on the timing and the objectives of these alliances. Technology partnering between these proponents of competing basic designs are found to only materialize several years after the DOS-based design of IBM and Microsoft had become dominant. This study is of a qualitative and exploratory nature, using both a small data set and two case studies.management and organization theory ;