Name
Abstract | ||
|---|---|---|
The number of computational units integrated in a single processor is rapidly increasing. This suggests that applications will require efficient and effective ways to exploit the parallelism to achieve the performance offered by large-scale multicore processors. The efficient parallelization of the applications relies on the programming and execution models. On the one hand, the programming model must address the effort needed to extract parallelism for such processors. On the other hand, the execution model must handle the high levels of parallelism from the applications while efficiently exploiting the resources of the processors. In this work we use the Data-Flow model to achieve high levels of parallelism in an effort to scale the performance on the 48-core Intel Single-chip Cloud Computing (SCC) processor. We propose TFluxSCC, a software platform for execution of Data-Flow applications on the Intel SCC processor. TFluxSCC is based on the TFlux Data-Driven Multithreading (DDM) platform that was developed for commodity multicore systems. What we propose in this work is an efficient implementation of the DDM model on a clustered many-core that is used as a case study to achieve high degree of parallelism. With TFluxSCC we achieve scalable performance in a cluster of many simple cores using global address space without the need of cache-coherency support. Our scalability study shows that applications can scale, with speedup results ranging from 30x to 48x for 48 cores. The findings of this work provide insight towards what a Data-Flow implementation requires from many-cores and what it can offer to these processors in order to scale the performance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2597917.2597953 | Conf. Computing Frontiers |
Keywords | Field | DocType |
design,experimentation,parallel architectures,measurement,performance,heterogeneous computing | Instruction-level parallelism,Multithreading,Computer science,Degree of parallelism,Task parallelism,Parallel computing,Real-time computing,Data parallelism,Scalable parallelism,Execution model,Multi-core processor | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Diavastos | 1 | 9 | 4.48 |
| Giannos Stylianou | 2 | 1 | 1.03 |
| Pedro Trancoso | 3 | 377 | 43.79 |