Summary
Optimize AI Workloads with Efficient GPU Sharing
GPUs are essential for accelerating machine learning workloads, but resource constraints often limit scalability. The paper "nvshare: Practical GPU Sharing without Memory Size Constraints" introduces an innovative approach to GPU resource management. By enabling virtual GPU memory oversubscription through transparent handling of GPU page faults, nvshare allows multiple applications to utilize the entire physical GPU memory without encountering out-of-memory errors.
Join us as we discuss this paper together, explore its implications, and exchange ideas with fellow AI practitioners.
The access link for online participation will be posted here the day before the event.
Please read the paper in advance: https://dl.acm.org/doi/10.1145/3639478.3640034
About Our Paper Reading Sessions
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Important Information
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The AI Service Centre Berlin-Brandenburg is a project of the Hasso Plattner Institute funded by the Federal Ministry of Education and Research. Its aim is to lower the barriers for using AI in business and society.
Requirements
Join the AI Maker Community Slack Workspace: The communication during the session will happen through our Slack Workspace, in the #ai-maker-sessions channel: https://join.aimaker.community
Please register for this free event on Eventbrite.
Event Details
This is an online event. We will post a link to the session in Slack shortly before the session starts.