Parallel computing can accelerate the solution of computationally expensive optimization problems
Enabling you to select the programming paradigm to work well in a multicore system
Using parallel computing to accelerate the solution of computationally intensive problems
The post contains some helpful hints for parallel computing as well as hints for working with Jacket
University of Illinois researchers explain how climate changes affect the ecosystem
Code downloads , training materials and research papers from ETH Zurich
Examples and reference materials for MATLAB GPU support via Parallel Computing Toolbox.
Using distributed arrays & PCT's SPMD construct to solve large linear algebra problems.
Custom engine calibration tool for extracting highest possible performance from AMG powertrains.
Parallel computing reduces time for analysis of electromechanical devices from 56 hours to under 2.
Recent enhancements to MATLAB® and Image Processing Toolbox™ increase image processing speed
Tips and techniques to make your model run faster
Fulcrum Asset Management Develops Custom Quantitative Risk Management System
Compute a variety of derived market data from raw market data
Assessing code performance, adopting efficient serial programming practices, generating C code, etc.
MIDACO is a global optimization software for mixed integer nonlinear programming (MINLP)
Run your algorithm on a GPU; solve a geometric problem with millions of lines in under a second.
Battery Pack Modeling on a Multicore Real-Time Target
Optimizing Vehicle Suspension Design