9 citations · 9 across the 5 of their papers we have counts for
5 papers
Optimus: A Generic Operator-Level PyTorch Model Transformation Framework
Menglu Yu, Jiaqi Xu, Yuzhen Huang +19
In large-scale industrial applications, deep learning models that power recommendation and ranking have complex and diverse model architectures. These models are continuously devel…
On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant GPU Clusters with Communication Contention
Menglu Yu, Bo Ji, Hridesh Rajan +1
Powered by advances in deep learning (DL) techniques, machine learning and artificial intelligence have achieved astonishing successes. However, the rapidly growing needs for DL al…
GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs
Menglu Yu, Ye Tian, Bo Ji +3
Fueled by advances in distributed deep learning (DDL), recent years have witnessed a rapidly growing demand for resource-intensive distributed/parallel computing to process DDL com…
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
Chris Cummins, Bram Wasti, Jiadong Guo +9
Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domain…
Transformation Importance with Applications to Cosmology
Chandan Singh, Wooseok Ha, Francois Lanusse +3
Machine learning lies at the heart of new possibilities for scientific discovery, knowledge generation, and artificial intelligence. Its potential benefits to these fields requires…