RESEARCH STATEMENT: High Performance Computing Bioinformatics Alexandros Stamatakis ICS-FORTH...

RESEARCH STATEMENT: High Performance Computing

Bioinformatics

Alexandros StamatakisICS-FORTH

stamatak@ics.forth.gr

Outline ABOUT PART I: Phylogenetic Inference

introduction maximum likelihood solutions current & future directions

PART II: Areas of Common Interest

1997/1998

2001-2004

About DFG offered PostDoc Grant for ICS Rejected 2-year PostDoc contract

offered by CBU Bergen Funded by DAAD PostDoc Grant:

Independence

Independence Autism

Independence Autism Today’s goal: “What do I work on …

Independence Autism Today’s goal: “What do I work on &

what can we work on together?”

Independence Autism Today’s goal: “What do I work on &

what can we work on together?” This presentation is an informal one!

Phylogenetic Analysis Motivation

Tree-of-life New insights in medical & biological

research CIPRES: NSF-funded 11.6 million $ tree-of-

life project (www.phylo.org)

Tree-of-life New insights in medical & biological

research CIPRES: NSF-funded 11.6 million $ tree-of-

life project (www.phylo.org)

What about a European tree-of-life project?

Tree-of-life New insights in medical & biological research CIPRES: NSF-funded 11.6 million $ tree-of-life

project (www.phylo.org) Applications of phylogenetic trees

Bader et al (2001) Industrial applications of high-performance computing for phylogeny reconstruction.

Baker et al (1994) Which whales are hunted? A molecular genetic aproach to whaling.

Phylogenetic Methods Input: “good” multiple Alignment Output: unrooted binary tree Various models for phylogenetic inference

Models differ in computational complexity & accuracy of final trees

Fast & simple models Neighbor Joining Maximum Parsimony (MP)

Slow & complex models Maximum Likelihood (ML) Bayesian Methods

Example: Phylogeny of great Apes

Orangutan Gorilla Chimp Human

common ancestor time

The number of trees

The number of trees explodes!

BANG !

The Algorithmic Problem Number of potential trees grows

exponentially

# Taxa # Trees

10 2.027.025

15 7.905.853.580.625

50 2.84 * 10^76

The Algorithmic Problem Number of potential trees grows

exponentially

# Taxa # Trees

10 2.027.025

15 7.905.853.580.625

50 2.84 * 10^76

This is the number of atoms in

the universe 10^80

Maximum LikelihoodMaximum Likelihood

calculates:

1. Topologies

2. Branch lengths v[i]

3. Likelihood of the tree

v2 v3 v4

calculates:

1. Topologies

Goal: Obtain topology with maximum likelihood value

Problem I: Number of possible topologies is exponential in n

Problem II: Computation of likelihood function is expensive

Problem III: 99.99% accuracy required

Solution: algorithmic optimizations + new heuristics + HPC

v2 v3 v4

calculates:

1. Topologies

v2 v3 v4

calculates:

1. Topologies

v2 v3 v4

Only results are reported !

introduction maximum likelihood solutions

program development algorithmic optimization heuristics HPC solutions

current & future directions PART II: Areas of Common Interest

Directions

Technical Innovation

Algorithmic Innovation

Directions

Technical innovation drags behind

Making Ends Meet

Major Advances

RAxML Phylogeny Program Development

Develop fast sequential algorithm withnew heuristics & optimizations

Phylogenetics are an algorithmic discipline

Parallel program

Iterate

Algorithmic Optimization Acceleration of the likelihood function by

detection of equal patterns and re-using previously computed values

Performance improvement of up to 65% References

Stamatakis et al. “Accelerating Parallel Maximum Likelihood-based Phylogenetic Tree Calculations using Subtree Equality Vectors”, Supercomputing 2002.

Stamatakis et al. “AxML: A Fast Program for Sequential and Parallel Phylogenetic Tree Calculations based on the Maximum Likelihood Method”, CSB2002.

New Heuristics New heuristics to accelerate tree search Algorithm:

1. Lazy pre-scoring of many alternative topologies2. Store best 20-30 pre-scored trees in a list3. Thorough evaluation of those 20-30 best trees

Since October 2003 fastest & best ML-program on real world alignment data

Reference Stamatakis et al. “RAxML-III: A Fast Program for

Maximum Likelihood-based Inference of Large Phylogenetic Trees”, Bioinformatics, 21(4):456-463.

Simulated Annealing Combination of hill-climbing & simulated

annealing Not significantly slower than hill climbing Builds consensus trees on the fly More likely to avoid local optima Finds better trees for large alignments

(≥ 500 sequences) Reference

Stamatakis. “An Efficient Program for phylogenetic Inference Using Simulated Annealing”, IPDPS2005.

Parallel MPI Implementation

Non-deterministic parallel implementation with very low communication costs

Due to non-determinism partially superlinear speedups

Also available as distributed http-based program

Largest ML-analysis to data containing 10.000 organisms on RRZE PC-Cluster

Reference Stamatakis et al. “Parallel Inference of a 10.000-

taxon Phylogeny with Maximum Likelihood”, EuroPar 2004.

Shared Memory Parallelism

P[i] = f( g(Q[i]) , g(R[i]) )

virtual root

P[i] = f( g(Q[i]) , g(R[i]) )

virtual root

This operation uses ≥ 90% of total execution time !

P[i] = f( g(Q[i]) , g(R[i]) )

virtual root

This operation uses ≥ 90% of total execution time ! simple fine-grained parallelisation

virtual root

OpenMP Parallelisation of RAxML

OpenMP little effort required to parallelize program (1 week!)

Can help to solve memory problems for very long/large alignments

Can easily be applied to other programs such as PHYML

Hybrid parallelisation possible

OpenMP Parallelisation of RAxML

OpenMP little effort required to parallelize program (1 week!)

Can help to solve memory problems for very long/large alignments

Can easily be applied to other programs such as PHYML

Hybrid parallelisation possible Performance extremely HW-dependent !

very good on AMD Opteron not so good on Intel Itanium & Xeon

RAxML-OMP Speedup

Things I can cover Things I can partially cover Things I cannot cover

Things I can cover Novel Divide-and-Conquer algorithms

Cooperations: Usman Roshan at NJIT, Olaf Bininda-Emonds at TUM, Le Sy Vinh at HHUD

Hybrid parallelisation of RAxML Cooperation: Michael Ott at TUM

OpenMP parallelisation of PHYML Cooperation: Michael Ott at TUM

Parallelisation of RAxML on GPUs Cooperation: Pedro Trancoso at UC, Michael

Ott at TUM Grid-enabled RAxML

Cooperation: Angelos Bilas at ICS

Things I can partially cover RAxML code maintenance Phylogenetic on-line Benchmark Phylogeny contest Implementation of Protein

substitution models in RAxML Force European Tree-of-Life

project Candidate SPEC-Benchmark ? RAxML Web-Interface Simultaneous multiple alignment

and tree building

Things I cannot cover Phylogenetic Networks Novel Consesus-Tools Novel Mathematical Models Visualization tools Issues of quality assessment Performance of RAxML-OMP on

SMT/HT architectures

PART II: Areas of Common Interest What I am looking for What you might be looking for

What I am looking for: Compute-intensive sequential

Bioinformatics applications Everything that runs between 1 and 10.000

days Compute-intensive NP-complete

optimization problems Design & experimental evaluation of new

heuristics Manpower Biologists at IMBB that want to use RAxML

What I am looking for: Compute-intensive sequential

Bioinformatics applications Everything that runs between 1 and 10.000

days Compute-intensive NP-complete

optimization problems Design & experimental evaluation of new

heuristics Manpower Biologists at IMBB that want to use RAxML

With the current infrastructure at ICS we could compute trees for up to 5000 sequences

What you might be looking for:

Support with phylogenetic tree building programs

Parallel and distributed implementation of your applications

A benchmark application to test CPU architectures RAxML has some interesting properties

Collaborate on some interesting aspects of phylogenetics I cannot fully cover

Conclusion Presentation of past, current &

future work on RAxML Issues for collaboration in

phylogenetics Issues for collaboration in other

domains Code & papers available at:

www.ics.forth.gr/~stamatak

RESEARCH STATEMENT: High Performance Computing Bioinformatics Alexandros Stamatakis ICS-FORTH...

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