Communication-Computation Efficient Gradient Coding
It addresses efficiency in distributed learning for practitioners by incrementally improving on existing coded schemes.
This paper tackles the problem of reducing running time in distributed learning by developing gradient coding techniques that optimize the tradeoff between computation load, straggler tolerance, and communication cost, achieving a 32% reduction in running time compared to uncoded schemes and 23% compared to prior coded schemes while maintaining generalization error.
This paper develops coding techniques to reduce the running time of distributed learning tasks. It characterizes the fundamental tradeoff to compute gradients (and more generally vector summations) in terms of three parameters: computation load, straggler tolerance and communication cost. It further gives an explicit coding scheme that achieves the optimal tradeoff based on recursive polynomial constructions, coding both across data subsets and vector components. As a result, the proposed scheme allows to minimize the running time for gradient computations. Implementations are made on Amazon EC2 clusters using Python with mpi4py package. Results show that the proposed scheme maintains the same generalization error while reducing the running time by $32\%$ compared to uncoded schemes and $23\%$ compared to prior coded schemes focusing only on stragglers (Tandon et al., ICML 2017).