New Findings from Federal University Rio Grande do Sul in the Area of Machine Le arning Described (Allok: a Machine Learning Approach for Efficient Graph Executi on On Cpu-gpu Clusters)
New Findings from Federal University Rio Grande do Sul in the Area of Machine Le arning Described (Allok: a Machine Learning Approach for Efficient Graph Executi on On Cpu-gpu Clusters)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Port o Alegre, Brazil, by NewsRx correspondents, research stated, "The unprecedented increase in interconnected data has driven the development of efficient graph an alytics for extensive data analysis, resulting in improvements across various do mains. Prior work has focused on optimizing graph execution for both CPUs and GP Us while overlooking the scalability of graph applications and the selection of an ideal architecture." Financial support for this research came from Conselho Nacional de Desenvolvimen to Cientfico e Tecnolgico. Our news editors obtained a quote from the research from Federal University Rio Grande do Sul, "Thus, we propose Allok, a flexible graph processing framework th at aids in selecting the optimal processing architecture (CPU or GPU) for a batc h of graph applications while also optimizing number of threads on CPUs. Allok r elies solely on high-level graph features to make decisions without the need for further application execution."
Key words
Porto Alegre/Brazil/South America/Cyb orgs/Emerging Technologies/Machine Learning/Federal University Rio Grande do Sul
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