PyTorch Distributed
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Magnum.np.distributed: Accelerating Finite Difference Micromagnetic Simulations with Multiple GPUs
Announce Type: new Abstract: Micromagnetic simulations are essential tools in nanomagnetism and spintronics research. Although widely adopted solvers like Mumax3 and the Python-native magnum.np use GPU acceleration to improve performance, these tools are limited to single-device computation. In this work, we present the first Python-native multi-GPU micromagnetic framework by extending magnum.np with PyTorch Distributed.
AI Agent Guidelines for CS336 at Stanford
This file provides instructions for AI coding assistants (like ChatGPT, Claude Code, GitHub Copilot, Cursor, etc.) working with students in CS336. AI agents should function as teaching aids that help students learn through explanation, guidance, and feedback—not by completing assignments for them.
CS336: Language Modeling from Scratch
Course Staff Logistics - Lectures: Monday/Wednesday 3:00-4:20pm in Skilling Auditorium - Recordings: YouTube playlist - Office hours: - Percy Liang: Fridays 11am-12pm in Gates 366 - Tatsu Hashimoto: Tuesdays 11-12am in Gates 364 - Marcel Rød: Tuesdays 4:30-5:30pm in Gates 498, Wednesdays 4:30-5:30pm in Gates 415 - Herman Brunborg: Wednesdays 1:30-2:30pm, Fridays 1:30-2:30pm, location Gates 392 - Steven Cao: Mondays 4:30-5:30pm, Thursdays 9:30-10:30am, Gates 200 - Contact: Students should ask...
StageFrontier: Synchronization-Aware Stage Accounting for Distributed ML Training
new Abstract: When a distributed training job slows down, the hard part is knowing where to look. Synchronization hides the cause: a stall on one rank shows up as a wait on the others, so a data delay on a single rank can surface as backward time across the group. The cheap dashboards that run all the time -- per-stage averages and maxima -- misread this, double-counting the same exposed delay or burying the slow rank in an average, while full profilers see it clearly but are far too heavy...