SparseLNR
Adhitha Dias, Kirshanthan Sundararajah, Charitha Saumya, Milind V. Kulkarni · 2022
Sparse tensor algebra computations have become important in many real-world applications like machine learning, scientific simulations, and data mining. Hence, automated code generation and performance optimizations for tensor algebra kernels are paramount. Recent advancements such as the Tensor Algebra Compiler (TACO) greatly generalize and automate the code generation for tensor algebra expressions. However, the code generated by TACO for many important tensor computations remains suboptimal due to the absence of a scheduling directive to support transformations such as distribution/fusion.