IEEE Data(base) Engineering Bulletin · 2014 · 36 citations · 7 references
ComputingComputer ScienceData ScienceCompilersMachine LearningProgramming LanguagesAlgorithm AnalysisData ParallelismDistributed ComputingDistributed Data ProcessingOptimization For Machine LearningScalable Machine LearningData AccessDistributed Machine LearningDistributed Runtime SystemsDynamic CompilationExecution PlansMapreduce AlgorithmsParallel OptimizationRuntime Optimization
SystemML enables declarative, large-scale machine learning (ML) via a high-level language with R-like syntax. Data scientists use this language to express their ML algorithms with full flexibility but without the need to hand-tune distributed runtime execution plans and system configurations. These ML programs are dynamically compiled and optimized based on data and cluster characteristics using ruleand cost-based optimization techniques. The compiler automatically generates hybrid runtime execution plans ranging from in-memory, single node execution to distributed MapReduce (MR) computation and data access. This paper describes the SystemML optimizer, its compilation chain, and selected optimization phases for generating efficient execution plans.
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Joseph M. Hellerstein, Christoper Ré, Florian Schoppmann et al. · Proceedings of the VLDB Endowment · 2012 · 369 citations
SystemML: Declarative machine learning on MapReduce
Amol Ghoting, Rajasekar Krishnamurthy, Edwin Pednault et al. · 2011 · 300 citations
MLI: An API for Distributed Machine Learning
Evan Sparks, Ameet Talwalkar, Virginia Smith et al. · 2013 · 144 citations