Abstract
The work proposes ffMDF, a lightweight dynamic run-time support able to achieve high performance in the execution of dense linear algebra kernels on shared-cache multi-core. ffMDF implements a dynamic macro-data-flow interpreter processing DAG graphs generated on-the-fly out of standard numeric kernel code. The experimental results demonstrate that the performance obtained using ffMDF on both fine-grain and coarse-grain problems is comparable with or even better than that achieved by de-facto standard solutions (notably PLASMA library), which use separate run-time supports specifically optimised for different computational grains on modern multi-core.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2014 |
| Publisher | ACTA Press |
| Pages | 283-292 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 12th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2014 - , Austria Duration: 17 Feb 2014 → 19 Feb 2014 |
Conference
| Conference | 12th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2014 |
|---|---|
| Country/Territory | Austria |
| Period | 17/02/14 → 19/02/14 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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