2015/06/24 by Yuchen Zhang, Michael I. Jordan, Zhang, Yuchen +1 · 4 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel Computing and Optimization Techniques #Stochastic Gradient Optimization Techniques #cs.LG
paper · pdf · doi:10.48550/arxiv.1506.07552
redo experiments to learn bigger models; compare Splash with state-of-the-art implementations on Spark
openalex publication_date 2015/06/24 · arxiv created 2015/09/23 · arxiv updated 2015/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems. Splash consists of a programming interface and an execution engine. Using the programming interface, the user develops sequential stochastic algorithms without concerning any detail about distributed computing. The algorithm is then automatically parallelized by a communication-efficient execution engine. We provide theoretical justifications on the optimal rate of convergence for parallelizing stochastic gradient descent. Splash is built on top of Apache Spark. The real-data experiments on logistic regression, collaborative filtering and topic modeling verify that Splash yields order-of-magnitude speedup over single-thread stochastic algorithms and over state-of-the-art implementations on Spark.