Designing transformer oil immersion cooling servers for machine learning and first principle calculations

Keisuke Takahashi, Itsuki Miyazato, Satoshi Maeda, Lauren Takahashi, Mohammad Ali Haider
<span title="2022-05-17">2022</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
A transfomer oil immersion cooling server is designed and constructed for machine learning applications and first principle calculations that are carried out for materials-related research. CPU, motherboard, random access memory, hard disk drive, solid state drive, graphic card, and the power supply unit are submerged into the transformer oil in order to cool the entire system. Benchmark tests reveal that overall performance is improved while performance times for multicore calculations are
more &raquo; ... atically improved. Furthermore, calculation times for machine learning with large data sets and density functional theory calculations are shortened during single core calculations. Thus, a transformer oil immersion cooling server is proposed to be an alternative cooling system used for improving the performance of first principle calculations and machine learning.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0266880">doi:10.1371/journal.pone.0266880</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35580082">pmid:35580082</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9113583/">pmcid:PMC9113583</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hcqa2iwlorgz5jcifwyqv5yp7m">fatcat:hcqa2iwlorgz5jcifwyqv5yp7m</a> </span>
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