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New MLPerf Benchmark Measures Machine Learning Inference Performance

Today a consortium involving over 40 leading companies and university researchers introduced MLPerf Inference v0.5, the first industry standard machine learning benchmark suite for measuring system performance and power efficiency. “Our goal is to create common and relevant metrics to assess new machine learning software frameworks, hardware accelerators, and cloud and edge computing platforms in real-life situations,” said David Kanter, co-chair of the MLPerf inference working group. “The inference benchmarks will establish a level playing field that even the smallest companies can use to compete.”

Scalable and Distributed DNN Training on Modern HPC Systems

DK Panda from Ohio State University gave this talk at the Swiss HPC Conference. “We will provide an overview of interesting trends in DNN design and how cutting-edge hardware architectures are playing a key role in moving the field forward. We will also present an overview of different DNN architectures and DL frameworks. Most DL frameworks started with a single-node/single-GPU design.”

Deep Learning and Automatic Differentiation from Theano to PyTorch

Inquisitive minds want to know what causes the universe to expand, how M-theory binds the smallest of the small particles or how social dynamics can lead to revolutions. “The way that statisticians answer these questions is with Approximate Bayesian Computation (ABC), which we learn on the first day of the summer school and which we combine with High Performance Computing. The second day focuses on a popular machine learning approach ‘Deep-learning’ which mimics the deep neural network structure in our brain, in order to predict complex phenomena of nature.”