forest classifier for R in SPRINT Lawrence Mitchell EPCC - - PowerPoint PPT Presentation

Lawrence Mitchell EPCC lawrence.mitchell@ed.ac.uk

A parallel random forest classifier for R in SPRINT

Outline

• SPRINT
• Random Forests
• Parallelisation results/lessons
• Future work

SPRINT

• The Simple Parallel R Interface www.r-sprint.org

[Hill et al, BMC Bioinformatics (2008)]

• Enable R users to easily exploit HPC resources
• Motivating use case: analysis of gene expression data
• R library: install.packages(“sprint”)
• Parallel replacements for time-consuming analysis functions
• Written in C with MPI for parallelisation
• Portable

– multicore desktops; clusters; supercomputers; EC2

Example: permutation adjusted p-values

data(golub) library(multtest) ## Can take a long time resT <- mt.maxT(golub, golub.cl, test="t", side="abs") quit(save="no") data(golub) library(sprint) ## Run in parallel resT <- pmaxT(golub, golub.cl, test="t", side="abs") pterminate() quit(save="no")

Supported functionality

• Available now on CRAN

– Pearson Correlation (replacing cor) – Permutation adjusted p-values (replacing mt.maxT, from multtest) – Partioning around medoids (replacing pam)

• Coming soon: implemented, in test phase

– Parallel apply (like apply) – Bootstrapping (replacing boot) – Random Forests (builds on randomForest package) – Rank Products (implements functionality from Bioconductor package RP)

• Under development

– RMA analysis (affy package from Bioconductor)

Classifying data

• Have probe data from 100 patients in two groups
• Want to classify further patient data into correct group

– e.g. Susceptibility to some disease

• Construct classification model using test data
• Predict classification of unseen data
• Random Forests provide such a method

Random Forests

• An ensemble tree classifier
• Bootstrap samples of a dataset

– genetic data typically: O(100) cases; O(10000) probes

• One tree per sample, giving a forest
• Compute classifications over this ensemble

...

Existing implementations

• randomForest [Liaw & Wiener, R News (2002)]

– serial, R version

• parf [Topid et al, Parallel Numerics (2005)]

– task parallel F90, no R version

• randomjungle [Schwarz et al, Bioinformatics (2010)]

– task parallel C++, designed for microarray data, no R version

Parallelisation

• Build on existing serial R package
• Spread the generation of bootstraps across processes
• Combine the results on single (master) process
• 96000 probes; 65 cases

Combining takes time

Combine in parallel

• Do a tree reduction (compare MPI_Reduce)
• 24000 probes; 65 cases

Faster time to solution

• More efficient exploitation of computing resource
• 96000 probes; 65 cases

Lessons

• HPC resources are hard to exploit efficiently
• Profile and benchmark your implementation carefully

Future work in SPRINT

• Better (transparent) support for large datasets: starts October ‘11
• More analysis routines – we need user input here
• More efficient serial random forest (use randomjungle?)
• ...

Thanks

Wellcome Trust grant 086696/Z/08/Z and edikt2 The HECToR distributed CSE service operated by NAG Ltd The Centre for Numerical Algorithms and Intelligent Software DPM Team

• Peter Ghazal
• Thorsten Forster
• Muriel Mewissen

EPCC Team

• Terry Sloan
• Michal Piotrowski
• Lawrence Mitchell
• Savvas Petrou
• Bartek Dobrzelecki
• Jon Hill
• Florian Scharinger

Questions?