We have developed a high performance hybridized parallel Finite Difference Time Domain (FDTD) algorithm featuring both OpenMP shared memory programming and MPI message passing. Our goal is to effectively model the optical characteristics of a novel light source created by utilizing a new class of materials known as photonic bandgap crystals. Our method is based on the solution of the second order discretized Maxwell’s equations in space and time. This novel hybrid parallelization scheme allows us to take advantage of the new generation parallel machines possessing connected SMP nodes. By using parallel computations, we are able to complete a calculation on 24 processors in less than a day, where a serial version would have taken over three weeks. In this paper we present a detailed study of this hybrid scheme on an SGI Origin 2000 distributed shared memory ccNUMA system along with a complete investigation of the advantages versus drawbacks of this method.

Abstract. The computation of ever larger as well as more accurate phylogenetic (evolutionary) trees with the ultimate goal to compute the tree of life represents one of the grand challenges in High Performance Computing (HPC) Bioinformatics. Unfortunately, the size of trees which can be computed in reasonable time based on elaborate evolutionary models is limited by the severe computational cost inherent to these methods. There exist two orthogonal research directions to overcome this challenging computational burden: First, the development of novel, faster, and more accurate heuristic algorithms and second, the application of high performance computing techniques. The goal of this chapter is to provide a comprehensive introduction to the field of computational evolutionary biology to an audience with computing background, interested in participating in research and/or commercial applications of this field. Moreover, we will cover leading-edge technical and algorithmic developments in the field and discuss open problems and potential solutions.

Abstract—Numerical linear algebra operations are key primitives in scientific computing. Performance optimizations of such operations have been extensively investigated. With the rapid advances in technology, hardware acceleration of linear algebra applications using field-programmable gate arrays (FPGAs) has become feasible. In this paper, we propose FPGA-based designs for several basic linear algebra operations, including dot product, matrix-vector multiplication, matrix multiplication, and matrix factorization. By identifying the parameters for each operation, we analyze the trade-offs and propose a high-performance design. In the implementations of the designs, the values of the parameters are determined according to the hardware constraints, such as the available chip area, the size of available memory, the memory bandwidth, and the number of I/O pins. The proposed designs are implemented on Xilinx Virtex-II Pro FPGAs. Experimental results show that our designs scale with the available hardware resources. Also, the performance of our designs compares favorably with that of general-purpose processor-based designs. We also show that, with faster floating-point units and larger devices, the performance of our designs increases accordingly. Index Terms—Reconfigurable hardware, computations on matrices, parallel algorithms. Ç 1

Abstract. Many large-scale optimization problems rely on graph theoretic solutions; yet high-performance computing has traditionally focused on regular applications with high degrees of locality. We describe our novel methodology for designing and implementing irregular parallel algorithms that attain significant performance on high-end computer systems. Our results for several fundamental graph theory problems are the first ever to achieve parallel speedups. Specifically, we have demonstrated for the first time that significant parallel speedups are attainable for arbitrary instances of a variety of graph problems and are developing a library of fundamental routines for discrete optimization (especially in computational biology) on shared-memory systems. Phylogenies derived from gene order data may prove crucial in answering some fundamental questions in biomolecular evolution. Highperformance algorithm engineering offers a battery of tools that can reduce, sometimes spectacularly, the running time of existing approaches.

This is the second annual progress report for the ALCOM-FT project, supported by the European