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Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System
Processor-centric architectures (e. g., CPU, GPU) commonly used for modern ML training workloads are limited by the data movement bottleneck, i. e., due to repeatedly accessing the training dataset.
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