Name
Abstract | ||
|---|---|---|
Algebraic Dynamic Programming (ADP) is a framework to encode a broad range of optimization problems, including common bioinformatics problems like RNA folding or pairwise sequence alignment. The ADP compiler translates such ADP programs into C. As all the ADP problems have similar data dependencies in the dynamic programming tables, a generic parallelization is possible. We updated the compiler to include a parallel backend, launching a large number of independent threads. Depending on the application, we report speedups ranging from 6.1× to 25.8× on a Nvidia GTX 280 through the CUDA libraries. |
Year | Venue | Field |
|---|---|---|
2009 | PPAM | Algebraic number,Computer science,CUDA,Theoretical computer science,Ranging,Computational science,Optimization problem,Distributed computing,Dynamic programming,ENCODE,Parallel computing,Compiler,Thread (computing) |
DocType | Volume | ISSN |
Conference | 6068 | 0302-9743 |
ISBN | Citations | PageRank |
3-642-14402-0 | 9 | 0.56 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Steffen | 1 | 253 | 14.77 |
| Robert Giegerich | 2 | 1616 | 130.26 |
| Mathieu Giraud | 3 | 124 | 15.28 |