282 citations · 1k across the 43 of their papers we have counts for
48 papers
Computron: Serving Distributed Deep Learning Models with Model Parallel Swapping
Daniel Zou, Xinchen Jin, Xueyang Yu +2
Many of the most performant deep learning models today in fields like language and image understanding are fine-tuned models that contain billions of parameters. In anticipation of…
Distributed-Memory Randomized Algorithms for Sparse Tensor CP Decomposition
Vivek Bharadwaj, Osman Asif Malik, Riley Murray +2
Candecomp / PARAFAC (CP) decomposition, a generalization of the matrix singular value decomposition to higher-dimensional tensors, is a popular tool for analyzing multidimensional…
Proposed Consistent Exception Handling for the BLAS and LAPACK
James Demmel, Jack Dongarra, Mark Gates +7
Numerical exceptions, which may be caused by overflow, operations like division by 0 or sqrt(-1), or convergence failures, are unavoidable in many cases, in particular when softwar…
Communication Bounds for Convolutional Neural Networks
Anthony Chen, James Demmel, Grace Dinh +2
Convolutional neural networks (CNNs) are important in a wide variety of machine learning tasks and applications, so optimizing their performance is essential. Moving words of data…
Distributed-Memory Sparse Kernels for Machine Learning
Vivek Bharadwaj, Aydin Buluç, James Demmel
Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times Dense Matrix Multiplication (SpMM) appear in diverse settings, such as collaborative filtering, document cl…
Non-smooth Bayesian Optimization in Tuning Problems
Hengrui Luo, James W. Demmel, Younghyun Cho +2
Building surrogate models is one common approach when we attempt to learn unknown black-box functions. Bayesian optimization provides a framework which allows us to build surrogate…