Most scientists do their data analysis using commercial software over which they have little control. Yet, to take advantage of the multi-core processors new computers ship with, algorithms must be designed to run in parallel.
Luckily, many of the more popular scientific software packages have gone parallel. Some even offer versions or toolboxes that manage clusters or grids. iSGTW scoured the world of scientific software for the latest information on parallelization in scientific software.
Read on to find out more!