Google Research's ALX Optimizing Large-Scale Matrix Factorization On TPUs
Google Research has released a paper detailing the power of a new framework, ALX. ALX aims to increase efficiency for large-scale matrix factorization on TPUs.
Created on April 11|Last edited on April 11
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Google Research has released a paper (and summary blog post on the Google AI Blog) describing a new framework specifically designed for conducting large-scale matrix factorization on TPUs, called ALX.
How ALX works and why it's a good idea to use TPUs for matrix factorization
TPUs have a lot of things that make them stand out apart from the rest of the processor crowd, primarily when it comes to data parallelism and ability to hold and transfer data between cores quickly and reliably. These features and the general scalability of TPU processing is something that ALX aims to specifically exploit for faster matrix factorization processing at large scales.
By reshaping, compacting, splitting, and distributing data between TPU cores through a number of steps, ALX is able to use TPUs to the peak of their ability, greatly outspeeding other processor types.
Scalability, the number of TPUs working together in tandem, is another one of the focusses of the paper and one of ALX's goals. Tests were computed to see how the ALX framework managed at different scales, most importantly paying attention to the raw hypothetical power increase and how the communication overhead, as it gets more complicated with scale, would limit it. Using a dataset, WebGraph (also mentioned extensively through the paper), they found that there is certainly a sweet spot where the benefit of adding more TPUs starts to fall off rapidly compared to the hypothetical linear decrease.
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