Name
Abstract | ||
|---|---|---|
We introduce a version of the epistasis test in FaST-LMM for clusters of multithreaded processors. This new software maintains the sensitivity of the original FaST-LMM while delivering acceleration that is close to linear on 12-16 nodes of two recent platforms, with respect to improved implementation of FaST-LMM presented in an earlier work. This efficiency is attained through several enhancements on the original single-node version of FaST-LMM, together with the development of a message passing interface (MPI)-based version that ensures a balanced distribution of the workload as well as a multigraphics processing unit (GPU) module that can exploit the presence of multiple GPUs per node. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1089/cmb.2018.0087 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
clusters of computers,epistasis,FaST-LMM,genome-wide association studies (GWAS),GPUs,multicore processors | Cluster (physics),Epistasis,Parallel computing,Software,Artificial intelligence,Multi-core processor,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
25.0 | 8 | 1066-5277 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Héctor Martínez | 1 | 19 | 4.26 |
| Sergio Barrachina | 2 | 319 | 38.08 |
| Maribel Castillo | 3 | 176 | 29.03 |
| Enrique S. Quintana-Ortí | 4 | 1317 | 150.59 |
| Jordi Rambla de Argila | 5 | 0 | 0.34 |
| Xavier Farré | 6 | 0 | 0.34 |
| Arcadi Navarro | 7 | 7 | 4.95 |