6 citations · 7 across the 7 of their papers we have counts for
3 papers · 1 filter
Entropy-Constrained Adaptive Stochastic Quantization
Ran Ben Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher +1
Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input while preserving unbiasedness. I…
DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce
Wenchen Han, Shay Vargaftik, Michael Mitzenmacher +1
Multi-hop all-reduce is the de facto backbone of large model training. As the training scale increases, the network often becomes a bottleneck, motivating the reduction of the volu…
Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression
Wenchen Han, Shay Vargaftik, Michael Mitzenmacher +2
Gradient aggregation has long been identified as a major bottleneck in today's large-scale distributed machine learning training systems. One promising solution to mitigate such bo…