Statement of Research — Alexandre Evfimievski

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SystemML: Declarative Machine Learning on Spark

The rising need for custom machine learning (ML) algorithms and the growing data sizes that require the exploitation of distributed, data-parallel frameworks such as MapReduce or Spark, pose significant productivity challenges to data scientists. Apache SystemML addresses these challenges through declarative ML by (1) increasing the productivity of data scientists as they are able to express cu...

SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning

Systems for declarative large-scale machine learning (ML) algorithms aim at high-level algorithm specification and automatic optimization of runtime execution plans. State-ofthe-art compilers rely on algebraic rewrites and operator selection, including fused operators to avoid materialized intermediates, reduce memory bandwidth requirements, and exploit sparsity across chains of operations. How...

Declarative Machine Learning - A Classification of Basic Properties and Types

Declarative machine learning (ML) aims at the high-level specification of ML tasks or algorithms, and automatic generation of optimized execution plans from these specifications. The fundamental goal is to simplify the usage and/or development of ML algorithms, which is especially important in the context of large-scale computations. However, ML systems at different abstraction levels have emer...

On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML

Many large-scale machine learning (ML) systems allow specifying custom ML algorithms by means of linear algebra programs, and then automatically generate efficient execution plans. In this context, optimization opportunities for fused operators—in terms of fused chains of basic operators—are ubiquitous. These opportunities include (1) fewer materialized intermediates, (2) fewer scans of input d...