Powering statistical genetics with the grid
John‐Paul Robinson, Purushotham Bangalore, Jelai Wang, Tapan S. Mehta · 2008
Many computationally intense workflows are composed of the same algorithm applied to many data sets. For example, it is good practice in statistical genetics to assess the validity of a method by simulating thousands of datasets of known properties. Further each simulation may involve using permutation tests that necessitate repeating analyses thousands of times per data set. Improvements in the overall throughput of this workflow can be achieved with a straightforward increase in the number of computations that can take place simultaneously. High performance compute clusters have significantly improved the ability to run many such computations simultaneously and have shown the adaptability of these workflows to ever-increasing processing capacity.