To take care of this issue, a team of MIT researchers, in collaboration with Adobe Research and French Alternative Energies and Atomic Energy Commission, has created a new system named Tensor Algebra Compiler (Taco). In computer science, Tensor term is used for a higher dimensional matrix.
New Taco code offers a 100-times increase in speed over existing, non-optimized software packages. The performance of the system competes with the earlier-mentioned hand-optimized code for dealing with sparse data. This means a programmer needs to do lesser work on his end.
Removing the need for customized sparse matrix operations, the team has given us the “ability to generate code for any tensor-algebra expression when the matrices are sparse,” according to Saman Amarasinghe, an MIT professor of electrical engineering and computer science.
With Taco, a programmer simply needs to specify the size of a tensor and the location of the file from which it should import values. It then builds a hierarchical map that discards zero pairs and fastens the computation.
Moreover, Taco uses a better indexing scheme to only store nonzero value of sparse tensors. A publicly released tensor by Amazon, which takes up about 107 exabytes of data with zeroes, takes up only 13GB without zeroes.
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You can read more about this recent advancement in sparse-matrix computation on MIT News website.