187 citations · 237 across the 6 of their papers we have counts for
6 papers
Fast Matrix Multiplication via Compiler-only Layered Data Reorganization and Intrinsic Lowering
Braedy Kuzma, Ivan Korostelev, João P. L. de Carvalho +4
The resurgence of machine learning has increased the demand for high-performance basic linear algebra subroutines (BLAS), which have long depended on libraries to achieve peak perf…
Advancing Direct Convolution using Convolution Slicing Optimization and ISA Extensions
Victor Ferrari, Rafael Sousa, Marcio Pereira +4
Convolution is one of the most computationally intensive operations that must be performed for machine-learning model inference. A traditional approach to compute convolutions is k…
A matrix math facility for Power ISA(TM) processors
José E. Moreira, Kit Barton, Steven Battle +22
Power ISA(TM) Version 3.1 has introduced a new family of matrix math instructions, collectively known as the Matrix-Multiply Assist (MMA) facility. The instructions in this facilit…
Bandwidth-Optimized Parallel Algorithms for Sparse Matrix-Matrix Multiplication using Propagation Blocking
Zhixiang Gu, Jose Moreira, David Edelsohn +1
Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. It is well known that SpGEMM is a memor…
Enabling Massive Deep Neural Networks with the GraphBLAS
Jeremy Kepner, Manoj Kumar, José Moreira +3
Deep Neural Networks (DNNs) have emerged as a core tool for machine learning. The computations performed during DNN training and inference are dominated by operations on the weight…
Mathematical Foundations of the GraphBLAS
Jeremy Kepner, Peter Aaltonen, David Bader +13
The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. Mathematically the Graph- BLAS…