3 papers
cs.DC2026
Eidola: Modeling Multi-GPU Network Communication Traffic in Distributed AI Workloads
Ranganath R. Selagamsetty, Matthew Poremba, Bradford M. Beckmann +2
As distributed AI workloads grow in scale, multi-GPU systems have become essential for training large models. Although techniques like kernel fusion and overlapping communication w…
cs.AR2020
MicroGrad: A Centralized Framework for Workload Cloning and Stress Testing
Gokul Subramanian Ravi, Ramon Bertran, Pradip Bose +1
We present MicroGrad, a centralized automated framework that is able to efficiently analyze the capabilities, limits and sensitivities of complex modern processors in the face of c…
cs.LG2019
BlurNet: Defense by Filtering the Feature Maps
Ravi Raju, Mikko Lipasti
Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adversarial examples, stemm…