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A. Borusan

17.12.2015, 12 Uhr c.t. TU Berlin, EN building, seminar room EN 719 (7th floor), Einsteinufer 17, 10587 Berlin: "SystemML’s Optimizer: Advanced Compilation Techniques for Large-Scale Machine Learning Programs" (Matthias Boehm, IBM Research - Almaden; Sa

Declarative large-scale machine learning (ML) aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations to distributed computations on MapReduce or Spark. The compilation of large-scale ML programs exhibits many opportunities for automatic optimization, which is crucial to achieve both high efficiency and scalability if required. In this talk, we give an up-to-date overview of SystemML's compilation chain and selected optimization techniques. We specially discuss the end-to-end compilation chain, static and dynamic simplification rewrites, operator selection, and dynamic recompilation.