Sparse Operation

Sparse operation research focuses on optimizing computations involving sparse matrices, prevalent in machine learning models like graph neural networks and Mixture-of-Experts, to improve training speed and efficiency. Current research emphasizes developing specialized libraries and algorithms, such as those employing block-sparse structures and randomized computations, for various hardware architectures (GPUs, IPUs). These advancements are crucial for enabling the training and deployment of larger, more complex models while mitigating the computational and memory burdens associated with dense matrix operations, impacting fields like natural language processing and recommendation systems.