QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent
Year: 2016
Type: preprint
Abstract: Parallel implementations of stochastic gradient descent (SGD) have receivedsignificant research attention, thanks to excellent scalability properties of
this algorithm, and to its efficiency in the context of training deep neural
networks. A fundamental barrier for parallelizing large-scale SGD is the fact
that the cost of communicating the gradient updates between nodes can be very
large. Consequently, lossy compresion heuristics have been proposed, by which
nodes only communicate quantized gradients. Although effective in practice,
these heuristics do not always provably converge, and it is not clear whether
they are optimal. In this paper, we propose Quantized SGD (QSGD), a family of compression
schemes which allow the compression of gradient updates at each node, while
guaranteeing convergence under standard assumptions. QSGD allows the user to
trade off compression and convergence time: it can communicate a sublinear
number of bits per iteration in the model dimension, and can achieve
asymptotically optimal communication cost. We complement our theoretical
results with empirical data, showing that QSGD can significantly reduce
communication cost, while being competitive with standard uncompressed
techniques on a variety of real tasks.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2531786980