COMPSs GPU Cache K-Means Final
Download RO-CrateName: K-Means GPU Cache ON
Contact Person: cristian.tatu@bsc.es
Access Level: public
License Agreement: Apache2
Platform: COMPSs
Machine: Minotauro-MN4
K-Means running on the GPU leveraging COMPSs GPU Cache for deserialization speedup.
Launched using 32 GPUs (16 nodes). Parameters used: K=40 and 32 blocks of size (1_000_000, 1200).
It creates a block for each GPU. Total dataset shape is (32_000_000, 1200).
Version dislib-0.9
Average task execution time: 16 seconds
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Version History
Final (earliest) Created 22nd Mar 2024 at 19:01 by Cristian Tatu
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Final-943cb11
Creators and SubmitterCreator
Additional credit
The Workflows and Distributed Computing Team (https://www.bsc.es/discover-bsc/organisation/scientific-structure/workflows-and-distributed-computing/)
Submitter
Views: 1057 Downloads: 217
Created: 22nd Mar 2024 at 19:01
Last updated: 25th Mar 2024 at 11:33
AttributionsNone
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https://orcid.org/0009-0003-8848-9436
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Teams: Cluster Emergent del Cervell Humà, Workflows and Distributed Computing, Pillar I: Manufacturing, Pillar II: Climate, Pillar III: Urgent computing for natural hazards, eFlows4HPC general, COMPSs Tutorials
Web page: https://eflows4hpc.eu
Distributed computing aims to offer tools and mechanisms that enable the sharing, selection, and aggregation of a wide variety of geographically distributed computational resources in a transparent way. The research done in this team is based on the past expertise of the group, and on extending it towards the aspects of distributed computing that can benefit from this expertise. The team at BSC has a strong focus on programming models and resource management and scheduling in distributed computing ...
