Search Results for "parallel computing datamaning"

...Currently, the main focus of these is computational quantum mechanics, analysis and simulation of molecular spectra, and general-purpose approximants. The package provides the most reliable modern strategies for linear and non-linear model optimization, regularization, and hypothesis tests. Parallel computing is supported.

A coupled dual porosity Stokes model implemented with FEniCS. References: 1. C. Douglas, X. Hu, B. Bai, X. He, M. Wei and J. Hou, "A Data Assimilation Enabled Model for Coupling Dual Porosity Flow with Free Flow," 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Wuxi, 2018, pp. 304-307. doi: 10.1109/DCABES.2018.00085 2. Hu X., Douglas C.C. (2019) An Implementation of a Coupled Dual-Porosity-Stokes Model with...

Mapgraph is SYSTAP’s disruptive new technology to exploit the main memory bandwidth advantages of GPUs. The early work was co-developed with the University of Utah SCI Institute and has its pedigree in the UINTAH software running on over 750M cores on the TITAN Super Computer. Today, SYSTAP has commercialized this technology into it’s Blazegraph Accelerator and Blazegraph HPC products. Checkout our options for GPU acceleration of graphs or contact us to learn more: ...

This software computes Gauss-Legendre quadrature nodes and weights using the formulas developed in "Iteration-Free Computation of Gauss-Legendre Quadrature Nodes and Weights", I. Bogaert, published in the SIAM Journal of Scientific Computing (Permalink: http://dx.doi.org/10.1137/140954969). The key features are: - Speed: due to the simplified formulas and the O(1) complexity computation of individual Gauss-Legendre quadrature nodes and weights. The latter also makes this software perfectly compatible with parallel computing paradigms. - Accuracy: the error on the nodes and weights is smaller than a few ulps (see the paper for details).

Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic...

R packages supporting parallel computing.

Parallel Colt is a multithreaded version of Colt - a library for high performance scientific computing in Java. It contains efficient algorithms for data analysis, linear algebra, multi-dimensional arrays, Fourier transforms, statistics and histogramming

BSPonMPI is an implementation of the BSPlib standard on top of MPI. Both MPI and BSPlib are API's of communication routines meant for parallel computing, but BSPlib is easier to learn and its performance easier to predict.

NetWorkSpaces, developed by Scientific Computing Associates Inc., provides a framework to coordinate programs written in R. It allows users to write parallel programs in R easily.

CPPScaLapack is a C++ class wrapper for PBLAS and ScaLAPACK with MPI. CPPScaLapack provides a user-friendly interface of high-speed parallel matrix calculation with BLAS and LAPACK techonologies for programers concerning with large-scale computing.

MPIRuby is a Ruby version of the MPI supercomputing standard interface. It enables massively parallel and distributed algorithms to be written in a platform-independent manner suitable for large-scale computation-intensive experiments. MPI is the \"Mes

PPAT, or Parallel Path following Algorithm using Triangles, is a reliable parallel tool to trace the level curves of any continuous, not necessarily smooth, function f(z): C → R.