A machine-learning algorithm demonstrated the capability to process data that exceeds a computer's available memory by identifying a massive data set's key features and dividing them into manageable batches that don't choke computer hardware. The algorithm set a world record for factorizing huge data sets during a test run on the world's fifth-fastest supercomputer. Equally efficient on laptops and supercomputers, the highly scalable algorithm solves hardware bottlenecks that prevent processing information from data-rich applications in cancer research, satellite imagery, social media networks, national security science and earthquake research, to name just a few.
A machine-learning algorithm demonstrated the capability to process data that exceeds a computer's available memory by identifying a massive data set's key features and dividing them into manageable batches that don't choke computer hardware. Developed at Los Alamos National Laboratory, the algorithm set a world record for factorizing huge data sets during a test run on Oak Ridge National Laboratory's Summit, the world's fifth-fastest supercomputer.
"Traditional data analysis demands that data fit within memory constraints. Our approach challenges this notion," said Manish Bhattarai, a machine learning scientist at Los Alamos and co-author of the paper."We have introduced an out-of-memory solution. When the data volume exceeds the available memory, our algorithm breaks it down into smaller segments. It processes these segments one at a time, cycling them in and out of the memory.
The Los Alamos implementation takes advantage of hardware features such as GPUs to accelerate computation and fast interconnect to efficiently move data between computers. At the same time, the algorithm efficiently gets multiple tasks done simultaneously.Non-negative matrix factorization is another installment of the high-performance algorithms developed under the SmartTensors project at Los Alamos.
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