This paper presents Tofu, a system that partitions very large DNN models across multiple
GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow …

In this paper we present an approach for quantitative analysis of application-specific
dataflow architectures. The approach allows the designer to rate design alternatives in a …

The widespread use of object-oriented languages and Internet security concerns are just the
beginning. Add embedded systems, multiple memory banks, highly pipelined units …

Deep learning (DL) has gained great success in recent years, leading to state-of-the-art
performance in research community and industrial fields like computer vision and natural …

High Performance Fortran (HPF) is rapidly gaining acceptance as a language for parallel
programming. The goal of HPF is to provide a simple yet efficient machine independent …

The goal of languages like Fortran D or HPF is to provide a simple yet e cient
machineindependent parallel programming model. By shifting much of the burden of …

The ubiquity of hybrid CPU+ GPU architectures has led to renewed interest in automatic
data layout generation owing to the fact that data layouts have a large impact on …

This article describes a knowledge‐based system for automatic parallelization of a wide
class of sequential numerical codes operating on vectors and dense matrices, and for …

Deep learning systems have become vital tools across many fields, but the increasing model
sizes mean that training must be accelerated to maintain such systems' utility. Current …

Application programmers in domains like machine learning, scientific computing, and
computational biology are accustomed to using powerful, high productivity array languages …