The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.
Gradient Sparsification is a technique for distributed training that sparsifies stochastic gradients to reduce the communication cost, with minor increase in the number of iterations. The key idea behind our sparsification technique is to drop some coordinates of the stochastic gradient and appropriately amplify the remaining coordinates to ensure the unbiasedness of the sparsified stochastic gradient. The sparsification approach can significantly reduce the coding length of the stochastic gradient and only slightly increase the variance of the stochastic gradient.
PowerSGD is a distributed optimization technique that computes a low-rank approximation of the gradient using a generalized power iteration (known as subspace iteration). The approximation is computationally light-weight, avoiding any prohibitively expensive Singular Value Decomposition. To improve the quality of the efficient approximation, the authors warm-start the power iteration by reusing the approximation from the previous optimization step.
BAGUA is a communication framework whose design goal is to provide a system abstraction that is both flexible and modular to support state-of-the-art system relaxation techniques of distributed training. The abstraction goes beyond parameter server and Allreduce paradigms, and provides a collection of MPI-style collective operations to facilitate communications with different precision and centralization strategies.