Using parallel computing and grid systems for genetic mapping of quantitative traits

Abstract

We present a flexible parallel implementation of the exhaustive grid search algorithm for multidimensional QTL mapping problems. A generic, parallel algorithm is presented and a two-level scheme is introduced for partitioning the work corresponding to the independent computational tasks in the algorithm. At the outer level, a static block-cyclic partitioning is used, and at the inner level a dynamic pool-of-tasks model is used. The implementation of the parallelism at the outer level is performed using scripts, while MPI is used at the inner level. By comparing to results from the SweGrid system to those obtained using a shared memory server, we show that this type of application is highly suitable for execution in a grid framework. © Springer-Verlag Berlin Heidelberg 2007.