1 citations · 1 across the 5 of their papers we have counts for
5 papers
NUNA: Characterizing and Mitigating Non-Uniform Network Access in Multi-Die GPU Scale-Up Systems
Conor James Green, William Won, Tuan Ta +1
Graphics processing unit (GPU) architectures are growing in size to meet the increasing compute and memory requirements. As GPU sizes increase, intra-socket wire transfer delay inc…
ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling
William Won, Jinsun Yoo, Tuan Ta +16
Distributed machine learning (ML) is a key paradigm for today's large-scale artificial intelligence applications. As model inference arises as an important use case, faithful model…
Throughput-Optimized Networks at Scale
Conor Green, Conor James Green, Mithuna Thottethodi +1
Data center network design plays a critical role in AI training by supporting scaling to thousands of accelerators. An open problem, designing a near-optimal throughput-oriented ne…
NetSmith: An Optimization Framework for Machine-Discovered Network Topologies
Conor Green, Mithuna Thottethodi
Over the past few decades, network topology design for general purpose, shared memory multicores has been primarily driven by human experts who use their insights to arrive at netw…
SafeBet: Secure, Simple, and Fast Speculative Execution
Conor Green, Cole Nelson, Mithuna Thottethodi +1
Spectre attacks exploit microprocessor speculative execution to read and transmit forbidden data outside the attacker's trust domain and sandbox. Recent hardware schemes allow pote…