| Autor: |
Rolf E. Andreassen, Weeraddana Manjula de Silva, Brian T. Meadows, Michael D. Sokoloff, Karen A. Tomko |
| Jazyk: |
angličtina |
| Rok vydání: |
2014 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 2, Pp 160-176 (2014) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2014.2306895 |
| Popis: |
GooFit is a thread-parallel, GPU-friendly function evaluation library, nominally designed for use with the maximum likelihood fitting program MINUIT. In this use case, it provides highly parallel calculations of normalization intergrals and log (likelihood) sums. A key feature of the design is its use of the Thrust library to manage all parallel kernel launches. This allows GooFit to execute on any architecture for which Thrust has a backend, currently, including CUDA for nVidia GPUs and OpenMP for single- and multicore CPUs. Running on an nVidia C2050, GooFit executes 300 times more quickly for a complex high energy physics problem than does the prior (algorithmically equivalent) code running on a single CPU core. The design and implementation choices, discussed in detail, can help to guide developers of other highly parallel, compute-intensive libraries. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
