Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

This paper presents Tofu, a system that partitions very large DNN models across multiple
GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow …

Many large-scale machine learning (ML) systems allow specifying custom ML algorithms by
means of linear algebra programs, and then automatically generate efficient execution …

Task partitioning of a graph-parallel system is traditionally considered equivalent to the
graph partition problem. Such equivalence exists because the properties associated with …

Compiler optimization plays an increasingly important role to boost the performance of
machine learning models for data processing and management. With increasingly complex …

SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning
Page 1 © 2017 IBM Corporation SPOOF: Sum-Product Optimization and Operator Fusion for …

Deep learning systems have become vital tools across many fields, but the increasing model
sizes mean that training must be accelerated to maintain such systems' utility. Current …

Machine learning (ML) training and scoring fundamentally relies on linear algebra programs
and more general tensor computations. Most ML systems utilize distributed parameter …

Array based storage and retrieval systems are demanded in many high dimensional
systems like Big data for their easy maintenance. However, the lack of scalability of the …