Search Results for « Parallelization »

SUMMARY: Multiple sequence alignment (MSA) is an important step in comparative sequence analyses. Parallelization is a key technique for reducing the time required for large-scale sequence analyses. The three calculation stages, all-to-all comparison, progressive alignment and iterative refinement, of the MAFFT MSA program were parallelized using the POSIX Threads library. Two natural parallelization strategies (best-first and simple hill-climbing) were implemented for the iterative refinement stage. Based on comparisons of the objective scores and benchmark scores between the two approaches, we selected a simple hill-climbing approach as the default. AVAILABILITY: The parallelized version of MAFFT is available at http://mafft.cbrc.jp/alignment/software/. This version currently supports the Linux operating system only.

BACKGROUND: Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4). RESULTS: Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers. CONCLUSIONS: Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.

A new set of benchmarks has been developed for the performance evaluation of highly parallel supercom puters. These consist of five "parallel kernel" bench marks and three "simulated application" benchmarks. Together they mimic the computation and data move ment characteristics of large-scale computational fluid dynamics applications. The principal distinguishing feature of these benchmarks is their "pencil and paper" specification—all details of these benchmarks are specified only algorithmically. In this way many of the difficulties associated with conventional bench- marking approaches on highly parallel systems are avoided.

Parallel computers with tens of thousands of processors are typically programmed in a data parallel style, as opposed to the control parallel style used in multiprocessing. The success of data parallel algorithms—even on problems that at first glance seem inherently serial—suggests that this style of programming has much wider applicability than was previously thought.

Abstract: Parallel database machine architectures have evolved from the use of exotic hardware to a software parallel dataflow architecture based on conventional shared-nothing hardware. These new designs provide impressive speedup and scaleup when processing relational database queries. This paper reviews the techniques used by such systems, and surveys current commercial and research systems. 1.

The parallel package allows parallel processing of tasks that are not interdependent. This allows all flavors of Stata to take advantage of multiprocessor machines. Even Stata/MP users can benefit because many community-contributed programs are not automatically parallelized but could be under our framework.

Parallel imaging is a robust method for accelerating the acquisition of magnetic resonance imaging (MRI) data, and has made possible many new applications of MR imaging. Parallel imaging works by acquiring a reduced amount of k-space data with an array of receiver coils. These undersampled data can be acquired more quickly, but the undersampling leads to aliased images. One of several parallel imaging algorithms can then be used to reconstruct artifact-free images from either the aliased images (SENSE-type reconstruction) or from the undersampled data (GRAPPA-type reconstruction). The advantages of parallel imaging in a clinical setting include faster image acquisition, which can be used, for instance, to shorten breath-hold times resulting in fewer motion-corrupted examinations. In this article the basic concepts behind parallel imaging are introduced. The relationship between undersampling and aliasing is discussed and two commonly used parallel imaging methods, SENSE and GRAPPA, are explained in detail. Examples of artifacts arising from parallel imaging are shown and ways to detect and mitigate these artifacts are described. Finally, several current applications of parallel imaging are presented and recent advancements and promising research in parallel imaging are briefly reviewed.