Search

The architectural landscape of high-performance computing stretches from superscalar uniprocessor to explicitly parallel systems to dedicated hardware implementations of algorithms. Single-purpose hardware can achieve the highest performance and uniprocessors can be the most programmable. Between these extremes, programmable and reconfigurable architectures provide a wide range of choice in flexibility, programmability, computational density, and performance. The UCSC Kestrel parallel processor strives to attain single-purpose performance while maintaining user programmability. Kestrel is a single-instruction stream, multiple- data stream (SIMD) parallel processor with a 512-element linear array of 8-bit processing elements. The system design focuses on efficient high-throughput DNA and protein sequence analysis, but its programmability enables high performance on computational chemistry, image processing, machine learning, and other applications. The Kestrel system has had unexpected longevity in its utility due to a careful design and analysis process. Experience with the system leads to the conclusion that programmable SIMD architectures can excel in both programmability and performance. This paper presents the architecture, implementation, applications, and observations of the Kestrel project at the University of California at Santa Cruz. [ABSTRACT FROM AUTHOR]

A novel pattern recognition algorithm called an orthogonal kernel machine (OKM) is presented for the prediction of functional sites in proteins. Two novelties in OKM are that the kernel function is specially designed for measuring the similarity between a pair of protein sequences and the kernels are selected using the orthogonal method. Based on a set of well-recognized orthogonal kernels, this algorithm demonstrates its superior performance compared with other methods. An application of this algorithm to a real problem is presented. [ABSTRACT FROM AUTHOR]