Machine Learning for CUDA+MPI Design Rules

no code implementations • 4 Mar 2022 • Carl Pearson, Aurya Javeed, Karen Devine

A decision tree is trained on the features and labels to produce design rules for each class; these rules can be used by systems experts to guide their implementations.

BIG-bench Machine Learning

TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes

1 code implementation • 28 Dec 2020 • Carl Pearson, Kun Wu, I-Hsin Chung, JinJun Xiong, Wen-mei Hwu

MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications.

Distributed, Parallel, and Cluster Computing

At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation

1 code implementation • 28 Jul 2020 • Mert Hidayetoglu, Carl Pearson, Vikram Sharma Mailthody, Eiman Ebrahimi, JinJun Xiong, Rakesh Nagi, Wen-mei Hwu

This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020.

SCOPE: C3SR Systems Characterization and Benchmarking Framework

2 code implementations • 18 Sep 2018 • Carl Pearson, Abdul Dakkak, Cheng Li, Sarah Hashash, JinJun Xiong, Wen-mei Hwu

This report presents the design of the Scope infrastructure for extensible and portable benchmarking.

Performance