LIU, V, ALEXANDROV, "Mixed Monte Carlo Parallel Algorithms for Matrix Computation", LNCS, Springer, 2330: 609-618, 2002.Fathi, B., Liu, B., Alexandrov, V.: Mixed Monte Carlo Parallel Algorithms for Matrix Computations. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J., Hoekstra, A...
In the next section, we look at some simple modifications we can make to the memory address computations to recover much of that lost performance.Example 39-2. CUDA C Code for the Work-Efficient Sum Scan of Algorithms 3 and 4.The highlighted blocks are discussed in Section 39.2.3....
Those computations include the evaluation of the inverse, the determinant, and the characteristic polynomial of a matrix. Recently, processor efficiency of the previous parallel algorithms for numerical matrix inversion has been substantially improved in (Pan and Reif, 1987), reaching optimum estimates ...
Parallel Computing Toolbox™ lets you solve compute- and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—enable you to scale MATLAB®applications without CUDA®or ...
Parallel Computing Toolbox enables you to harness a multicore computer, GPU, cluster, grid, or cloud to solve computationally and data-intensive problems. The toolbox includes high-level APIs and parallel language for for-loops, queues, execution on CUDA
However, with the advent of hybrid multiplex algorithms, the use of parallel algorithms is both possible and, in the minds of some, preferable. A parallel algorithm follows somewhat the same steps as listed previously for serial algorithms. The primary difference is that rather than employing a ...
The workshop aims at being a forum for an exchange of ideas, insights and experiences in different areas of parallel computing (Multicores, Manycores and GPUs) and applications in which matrix algorithms are employed. The PMAA18 workshop will bring together experts and researchers from diverse ...
A disadvantage of both the second and third schemes is that the GPU's native trilinear filtering cannot be used for high-quality volume rendering of the data. Fortunately, alternate volume rendering algorithms can efficiently render high-quality, filtered images from these complex 3D...
Parallel Computing Toolbox enables you to harness a multicore computer, GPU, cluster, grid, or cloud to solve computationally and data-intensive problems. The toolbox includes high-level APIs and parallel language for for-loops, queues, execution on CUDA
We use the algorithms to estimate disaggregated input/output tables and a multi-regional trade flow table of the U.S. The larger problem solved has approximately 12 000 constraints and over 370 000 nonlinear variables. This is the first of two papers that aim at the solution of very large ...