The stochastic gradient descent (SGD) is used for a whole variety of supply chain problems from demand forecasting to pricing optimization. From a software performance perspective, the crux of the SGD problem is to increase the wall-clock rate of descent while preserving the determinism of the execution. Indeed, as far as parallelization is concerned, indeterminism is the default; it takes effort to achieve a reproducible flavor of the algorithm. The report introduces a technique that delivers a 5x speed-up at a 6x increase of compute costs.
The stochastic gradient descent (SGD) is used for a whole variety of supply chain problems from demand forecasting to pricing optimization. From a software performance perspective, the crux of the SGD problem is to increase the wall-clock rate of descent while preserving the determinism of the execution. Indeed, as far as parallelization is concerned, indeterminism is the default; it takes effort to achieve a reproducible flavor of the algorithm. The report introduces a technique that delivers a 5x speed-up at a 6x increase of compute costs.