Algorithm breaks the exabyte barrier

Machine learning masters massive data sets

A machine-learning algorithm demonstrated the aptitude to course of information that exceeds a pc’s obtainable reminiscence by figuring out a large information set’s key options and dividing them into manageable batches that do not choke pc {hardware}. Developed at Los Alamos Nationwide Laboratory, the algorithm set a world file for factorizing large information units throughout a check run on Oak Ridge Nationwide Laboratory’s Summit, the world’s fifth-fastest supercomputer. 

Equally environment friendly on laptops and supercomputers, the extremely scalable algorithm solves {hardware} bottlenecks that forestall processing info from data-rich purposes in cancer research, satellite imagery, social media networks, nationwide safety science and earthquake analysis, to call only a few.

“We developed an ‘out-of-memory’ implementation of the non-negative matrix factorization technique that permits you to factorize bigger data sets than beforehand potential on a given {hardware},” stated Ismael Boureima, a computational physicist at Los Alamos Nationwide Laboratory. Boureima is first writer of the paper in The Journal of Supercomputing on the record-breaking algorithm.

“Our implementation merely breaks down the big data into smaller items that may be processed with the obtainable assets. Consequently, it is a useful gizmo for maintaining with exponentially rising information units.”

“Conventional information evaluation calls for that information match inside reminiscence constraints. Our method challenges this notion,” stated Manish Bhattarai, a machine studying scientist at Los Alamos and co-author of the paper.

“We’ve launched an out-of-memory resolution. When the info quantity exceeds the obtainable reminiscence, our algorithm breaks it down into smaller segments. It processes these segments separately, biking them out and in of the reminiscence. This method equips us with the distinctive means to handle and analyze extraordinarily massive information units effectively.”

The distributed algorithm for contemporary and heterogeneous high-performance pc methods will be helpful on {hardware} as small as a desktop pc, or as massive and sophisticated as Chicoma, Summit or the upcoming Venado supercomputers, Boureima stated.

“The query is now not whether or not it’s potential to factorize a bigger matrix, reasonably how lengthy is the factorization going to take,” Boureima stated.

The Los Alamos implementation takes benefit of {hardware} options comparable to GPUs to speed up computation and quick interconnect to effectively transfer information between computer systems. On the similar time, the algorithm effectively will get a number of duties executed concurrently.

Non-negative matrix factorization is one other installment of the high-performance algorithms developed underneath the SmartTensors mission at Los Alamos.

In machine studying, non-negative matrix factorization can be utilized as a type of unsupervised studying to tug which means from information, Boureima stated. “That is essential for machine learning and data analytics as a result of the algorithm can determine explainable latent options within the information which have a selected which means to the consumer.”

The record-breaking run

Within the record-breaking run by the Los Alamos workforce, the algorithm processed a 340-terabyte dense matrix and an 11-exabyte sparse matrix, utilizing 25,000 GPUs.

“We’re reaching exabyte factorization, which nobody else has executed, to our data,” stated Boian Alexandrov, a co-author of the brand new paper and a theoretical physicist at Los Alamos who led the workforce that developed the SmartTensors synthetic intelligence platform.

Decomposing or factoring information is a specialised data-mining method geared toward extracting pertinent info, simplifying the info into comprehensible codecs.

Bhattarai additional emphasised the scalability of their algorithm, remarking, “In distinction, typical strategies usually grapple with bottlenecks, primarily because of the lag in information switch between a pc’s processors and its reminiscence.”

“We additionally confirmed you do not essentially want large computer systems,” Boureima stated. “Scaling to 25,000 GPUs is nice should you can afford it, however our algorithm shall be helpful on desktop computer systems for one thing you could not course of earlier than.” 

Extra info: Ismael Boureima et al, Distributed out-of-memory NMF on CPU/GPU architectures, The Journal of Supercomputing (2023). DOI: 10.1007/s11227-023-05587-4

 Quotation: Machine studying masters large information units: Algorithm breaks the exabyte barrier (2023, September 11) retrieved 11 September 2023 from https://techxplore.com/information/2023-09-machine-masters-massive-algorithm-exabyte.html 

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