Aug 26, 2020 · Abstract page for arXiv paper 2008.11421: Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA.
Nov 9, 2020 · The dedicated memory of hardware accelerators can be insufficient to store all weights and/or intermediate states of large deep learning ...
Aug 26, 2020 · Another general solution to this memory capacity problem, that we discuss in this paper, is to use out-of-core methods, without or with ...
We propose a performance model based on the concurrency analysis of out-of-core training behavior, and derive a strategy that combines layer swapping and ...
Aug 26, 2020 · A performance model based on the concurrency analysis of out-of-core training behavior, and a strategy that combines layer swapping and ...
These algorithms move data back and forth between the CPU and the GPU to free up space on the GPU. KARMA [47] is a framework built over PyTorch that extends ...
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA. SessionMemory Efficient Deep Learning. Authors. Mohamed Wahib · Haoyu Zhang.
Scaling distributed deep learning workloads beyond the memory capacity with KARMA. Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen, Aleksandr Drozd, Jens ...
Session ; 1:00pm - 1:30pm EDT, Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA. Authors. Mohamed Wahib · Haoyu Zhang · Truong ...
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Technical Paper. Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen, ...