Two teams, equal priority. Team A runs small jobs continuously. Team B needs to run one large job. Every time GPUs free up, Team A's jobs fit first. Team B waits indefinitely. The problem: There is no memory of who just ran versus who has been waiting. NVIDIA Run:ai v2.24 introduces time-based fairshare to solve this. The scheduler can now track historical usage. Teams that consumed more recently get lower priority for over-quota allocation. Teams that have been waiting get boosted. Result: prop
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Two teams, equal priority. Team A runs small jobs continuously. Team B needs to run one large job. Every time GPUs free up, Team A's jobs fit first. Team B waits
NVIDIA Data Center. . Two teams, equal priority. Team A runs small jobs continuously. Team B needs to run one large job. Every time GPUs free up, Team A's jobs fit first. Team B waits indefinitely. The problem: There is no memory of who just ran versus who has been waiting. NVIDIA Run:ai v2.24 introduces time-based fairshare to solve this. The ...
1.7K views1 month ago
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