Run-time optimizations for replicated dataflows on heterogeneous environments
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Optimizing dataflow applications on heterogeneous environments
Cluster Computing
| Hi-index | 0.00 |
We have been witnessing a continuous growth of both heterogeneous computational platforms (e.g., Cell blades, or the joint use of traditional CPUs and GPUs) and multi- core processor architecture; and it is still an open question how applications can fully exploit such computational potential efficiently. In this paper we introduce a run-time environment and programming framework which supports the implementation of scalable and efficient parallel applications in such heterogeneous, distributed environments. We assess these issues through well-known kernels and actual applications that behave regularly and irregularly, which are not only relevant but also demanding in terms of computation and I/O. Moreover, the irregularity of these, as well as many other applications poses a challenge to the design and implementation of efficient parallel algorithms. Our experimental environment includes dual and octa-core machines augmented with GPUs and we evaluate our framework performance for standalone and distributed executions. The evaluation on a distributed environment has shown near to linear scale-ups for two data mining applications, while the applications performance, when using CPU and GPU, has been improved into around 25%, compared to the GPU-only versions.