Regression analysis is a common set of statistical procedures for estimating the relationships between variables. Problems that involve very large amounts of data with a high number of variables can be computationally intensive. This project is investigating how scalable, distributed computer systems and associated algorithms/software perform on such large scale problems. This will inform current best practice in terms of algorithms, architectures, and implementations.

Explaining the science

Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target or output) variable and independent (predictor or input) variables. This is used for forecasting, time series modelling, machine learning, and finding causal relationships between variables. For example, the relationship between rash driving and number of road accidents.

The overall goal of regression is to examine whether recorded data effectively predict some other outcome variable, and in which ways particular variables impact the outcome variable. The statistical and computational performance of regression analysis methods in practice depends on the model relating the variables, the data actually recorded and the algorithm used to produce estimators and associated quantities.

This project is looking at the effectiveness of running regression analysis for large datasets with a high number of variables, on distributed computing systems. Parallel distributed computing is a type of computation in which many calculations, or the execution of processes, are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. Related to this, a computer cluster is a set of connected computers that work together so that, in many respects, they can be viewed as a single system. Each node in the system is set to perform tasks that are controlled and scheduled by system software.

Project aims

The ultimate goal of this project is to critically understand how well different, readily available, large-scale regression algorithms, software, and frameworks perform on distributed systems. This understanding will help isolate computational and statistical performance issues.

Challenging benchmark datasets be developed to add additional focus, and there is the potential for more sophisticated, but less readily-available algorithms to be analysed for comparative purposes.

This project aligns to the Institute’s strategic priorities in establishing leadership and providing guidance for common data analysis tasks at scale. It also feeds into the larger data science at scale programme looking at the performance and usability of modern hardware and algorithms.


In collaboration with Cray, the analysis in this project will be conducted on their Urika-GX agile analytics platform. The skills and software developed by the investigation will then be applied to large and challenging datasets.

Throughout the project, documentation will be written that will enable other data scientists to perform large scale regressions with greater ease, and understand the implications of using different architectures, frameworks, algorithms, and implementations.