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Scaling Machine Learning Software with Allinea Tools

“The majority of deep learning frameworks provide good out-of-the-box performance on a single workstation, but scaling across multiple nodes is still a wild, untamed borderland. This discussion follows the story of one researcher trying to make use of a significant compute resource to accelerate learning over a large number of CPUs. Along the way we note how to find good multiple-CPU performance with Theano* and TensorFlow*, how to extend a single-machine model with MPI and optimize its performance as we scale out and up on both Intel Xeon and Intel Xeon Phi architectures.”

14 Students Awarded SIGHPC/Intel Computational and Data Science Fellowships

Today SIGHPC announced the first-ever recipients of the ACM SIGHPC/Intel Computational and Data Science Fellowship. The fellowship is funded by Intel and was announced at the high performance computing community’s SC conference in November of last year. Established to increase the diversity of students pursuing graduate degrees in data science and computational science, the fellowship […]