Scientific workloads have traditionally exploited high levels of sparsity to accelerate
computation and reduce memory requirements. While deep neural networks can be made …

We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs.
Our system ismotivated by the limitations of current frameworks, which restrict the dynamic …

Tensor algebra is a powerful tool with applications in machine learning, data analytics,
engineering and the physical sciences. Tensors are often sparse and compound operations …

Support Vector Machines (SVMs) are classic supervised learning models for classification,
regression and distribution estimation. A survey conducted by Kaggle in 2017 shows that …

Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous
applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage …

Phenomenally successful in practical inference problems, convolutional neural networks
(CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The …

Deep neural networks have become the compelling solution for the applications such as
image classification, object detection, speech recognition, and machine translation …

This paper presents a scalable high-performance software library to be used for graph
analysis and data mining. Large combinatorial graphs appear in many applications of high …

The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building
block for many numerical linear algebra kernel operations or graph traversal applications …

Tensor factorizations are powerful tools in many machine learning and data analytics
applications. Tensors are often sparse, which makes sparse tensor factorizations memory …