Abstract-Mapping short next-generation reads to reference genomes is an important element in SNP calling and expression studies. A major limitation to large-scale whole-genome mapping is the large memory requirements for the algorithm and the long run-time necessary for accurate studies. Several parallel implementations have been performed to distribute memory on different processors and to equally share the processing requirements. These approaches are compared with respect to their memory footprint, load balancing, and accuracy. When using MPI with multi-threading, linear speedup can be achieved for up to 256 processors.
Using MPI with multi-threading to parallelize the GNUMAP is introduced in REF , where the performance can be linearly scaled up to 256 processors across 32 nodes.
Parallel Mapping Approaches for GNUMAP
2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum
2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum
Medicine Computer Science