The Path to Higher Performance with Scalable Machine Learning

In an earlier post I explored the value of using scalable machine learning to extract value from huge amounts of data. In this post, I will dive down into the technical side of things, particularly the challenges and benefits that come with making algorithms scalable on large clusters of computers. Machine learning algorithms are written to run on single-node systems, or on specialized supercomputer hardware, which I’ll refer to as HPC boxes. They grew up in a world where they didn’t have to scale across multiple nodes. It’s relatively easy to get high performance when running algorithms on a single computer. With distributed computing, things get a great deal harder for some algorithms due to the communications latencies among what could be thousands of server nodes. So why not run…


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