New computational techniques, 'HighLight' and 'Tailors and Swiftiles,' could dramatically boost the speed and performance of high-performance computing applications like graph analytics or generative AI.
The work, from MIT and NIVIDIA, aims to accelerate sparse tensors for AI models by introducing more efficient and flexible ways to take advantage of sparsity.Researchers from MIT and NVIDIA have developed two techniques that accelerate the processing of sparse tensors, a type of data structure that's used for high-performance computing tasks.
The MIT and NVIDIA researchers crafted two solutions to address these problems. For one, they developed a technique that allows the hardware to efficiently find the nonzero values for a wider variety of sparsity patterns. To make it easier to find the remaining nonzero values in a model with billions of individual values, researchers often restrict the location of the nonzero values so they fall into a certain pattern. However, each hardware accelerator is typically designed to support one specific sparsity pattern, limiting its flexibility.
"In the end, the HighLight accelerator is able to efficiently accelerate dense models because it does not introduce a lot of overhead, and at the same time it is able to exploit workloads with different amounts of zero values based on hierarchical structured sparsity," Wu explains. But in a sparse tensor, many of the data values are zero, so an even larger tile can fit into the buffer than one might expect based on its capacity. Zero values don't need to be stored.
In a sparse tensor, a tile size can be chosen such that usually the tiles will have enough zeros that most still fit into the buffer. But occasionally, a tile will have more nonzero values than will fit. In this case, those data are bumped out of the buffer.
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