Name
Abstract | ||
|---|---|---|
Despite many efforts to better utilize the potential of GPUs and CPUs, it is far from being fully exploited. Although many tasks can be easily sped up by using accelerators, most of the existing schedulers are not flexible enough to really optimize the resource usage of the complete system. The main reasons are (i) that each processing unit requires a specific program code and that this code is often not provided for every task, and (ii) that schedulers may follow the run-until-completion model and, hence, disallow resource changes during runtime. In this paper, we present VarySched, a configurable task scheduler framework tailored to efficiently utilize all available computing resources in a system. VarySched allows a more fine-grained task-to-resource placement which is even further enhanced by allowing the tasks to migrate to another resource during their runtime. In addition, VarySched can manage multiple scheduling strategies - optimizing, for instance, throughput or energy efficiency - and switch between them at any time. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CLUSTER.2016.19 | 2016 IEEE International Conference on Cluster Computing (CLUSTER) |
Keywords | Field | DocType |
scheduling,energy aware,heterogeneous computing | Kernel (linear algebra),Program code,Public records,Computer science,Efficient energy use,Scheduling (computing),Parallel computing,Real-time computing,Schedule,Throughput,Processor scheduling,Distributed computing | Conference |
ISSN | ISBN | Citations |
1552-5244 | 978-1-5090-3654-7 | 1 |
PageRank | References | Authors |
0.37 | 13 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tim Süß | 1 | 6 | 1.18 |
| Nils Döring | 2 | 1 | 0.71 |
| Ramy Gad | 3 | 10 | 2.35 |
| Lars Nagel | 4 | 76 | 13.58 |
| André Brinkmann | 5 | 403 | 34.79 |
| dustin feld | 6 | 7 | 2.53 |
| Thomas Soddemann | 7 | 8 | 1.21 |
| Stefan Lankes | 8 | 152 | 26.39 |