Name
Abstract | ||
|---|---|---|
The ever-growing demand for computing resources has reached a wide range of application domains. Even though the ubiquitous availability of cloud-based GPU instances provides an abundance of computing resources, the programmatic complexity of utilizing heterogeneous hardware in a scale-out scenario is not yet addressed sufficiently. We deal with this issue by introducing the CloudCL framework, which enables developers to focus their implementation efforts on compute kernels without having to consider inter-node communication. Using CloudCL, developers can access the resources of an entire cluster as if they were local resources. The framework also facilitates the development of cloud-native application behavior by supporting dynamic addition and removal of resources at runtime. The combination of a straightforward job design and the corresponding job scheduling framework make sure that cluster resources are used efficiently and fairly. In an extensive performance evaluation, we demonstrate that the framework provides close-to-linear scale-out capabilities in multi-node deployment scenarios. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CANDAR.2017.49 | 2017 Fifth International Symposium on Computing and Networking (CANDAR) |
Keywords | Field | DocType |
GPU Computing,Distributed Computing,OpenCL,Cloud Computing | Kernel (linear algebra),Software deployment,Job design,Computer science,Symmetric multiprocessor system,Job scheduler,Dynamic priority scheduling,Java,Cloud computing,Distributed computing | Conference |
ISSN | ISBN | Citations |
2379-1888 | 978-1-5386-2088-5 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Max Plauth | 1 | 26 | 7.53 |
| Florian Rosler | 2 | 0 | 0.68 |
| Andreas Polze | 3 | 268 | 51.57 |