Robert S. Germain

The free energy landscape for β hairpin folding in explicit water

The folding free energy landscape of the C-terminal β hairpin of protein G has been explored in this study with explicit solvent under periodic boundary condition and oplsaa force field. A highly parallel replica exchange method that combines molecular dynamics trajectories with a temperature exchange Monte Carlo process is used for sampling with the help of a new efficient algorithm P3ME/RESPA. The simulation results show that the hydrophobic core and the β strand hydrogen bond form at roughly the same time. The free energy landscape with respect to various reaction coordinates is found to be rugged at low temperatures and becomes a smooth funnel-like landscape at about 360 K. In contrast to some very recent studies, no significant helical content has been found in our simulation at all temperatures studied. The β hairpin population and hydrogen-bond probability are in reasonable agreement with the experiment at biological temperature, but both decay more slowly than the experiment with temperature.

Fingerprint matching using transformation parameter clustering

Fingerprint matching is used in many noncriminal identification applications. Flash, a similarity-searching algorithm akin to geometric hashing, proves suitable for one-to-many matching of fingerprints on large-scale databases.

Large Scale Systems

This chapter describes some of the fundamental issues related to biometric identification (one-to-many matches) on a large scale as well as some details of a particular implementation of a scalable fingerprint matcher. The decomposition of system error rates into expressions in terms of component error rates and the extrapolation of identification accuracy from verification accuracy are explained. Details of the Flash fingerprint matcher are presented including assumptions about allowable transformations connecting instances of model fingerprints and the structure of the invariant index used to form tentative model correspondences. Finally, the pose clustering stage used to filter tentative correspondences and accumulate evidence is described. Results using this preliminary implementation on a database containing 100,000 models are presented and interpreted in the context pf the above-mentioned extrapolation framework described above.

Blue Matter, an application framework for molecular simulation on blue gene

In this paper we describe the context, architecture, and challenges of Blue Matter, the application framework being developed in conjunction with the science effort within IBMs Blue Gene project. The study of the mechanisms behind protein folding and related topics can require long time simulations on systems with a wide range of sizes and the application supporting these studies must map efficiently onto a large range of parallel partition sizes to optimize scientific throughput for a particular study. The design goals for the Blue Matter architecture include separating the complexities of the parallel implementation on a particular machine from those of the scientific simulation as well as minimizing system environmental dependencies so that running an application within a low overhead kernel with minimal services is possible. We describe some of the parallel decompositions currently being explored that target the first member of the Blue Gene family, BG/L, and present simple performance models for these decompositions that we are using to prioritize our development work. Preliminary results indicate that the high-performance networks on BG/L will allow us to use FFT-based techniques for periodic electrostatics with reasonable speedups on 512-1024 node count partitions even for systems with as few as 5000 atoms.

Scalable framework for 3D FFTs on the Blue Gene/L supercomputer: implementation and early performance measurements

This paper presents results on a communications-intensive kernel, the three-dimensional fast Fourier transform (3D FFT), running on the 2,048-node Blue Gene®/L (BG/L) prototype. Two implementations of the volumetric FFT algorithm were characterized, one built on the Message Passing Interface library and another built on an active packet Application Program Interface supported by the hardware bring-up environment, the BG/L advanced diagnostics environment. Preliminary performance experiments on the BG/L prototype indicate that both of our implementations scale well up to 1,024 nodes for 3D FFTs of size 128 × 128 × 128. The performance of the volumetric FFT is also compared with that of the Fastest Fourier Transform in the West (FFTW) library. In general, the volumetric FFT outperforms a port of the FFTW Version 2.1.5 library on large-node-count partitions.

Blue matter: approaching the limits of concurrency for classical molecular dynamics

This paper describes a novel spatial-force decomposition for N-body simulations for which we observe O(sqrt(p)) communication scaling. This has enabled Blue Matter to approach the effective limits of concurrency for molecular dynamics using particle-mesh (FFT-based) methods for handling electrostatic interactions. Using this decomposition, Blue Matter running on Blue Gene/L has achieved simulation rates in excess of 1000 time steps per second and demonstrated significant speed-ups to O(1) atom per node. Blue Matter employs a communicating sequential process (CSP) style model with application communication state machines compiled to hardware interfaces. The scalability achieved has enabled methodologically rigorous biomolecular simulations on biologically interesting systems, such as membrane-bound proteins, whose time scales dwarf previous work on those systems. Major scaling improvements require exploration of alternative algorithms for treating the long range electrostatics

Blue matter: strong scaling of molecular dynamics on blue gene/l

This paper presents strong scaling performance data for the Blue Matter molecular dynamics framework using a novel n-body spatial decomposition and a collective communications technique implemented on both MPI and low level hardware interfaces. Using Blue Matter on Blue Gene/L, we have measured scalability through 16,384 nodes with measured time per time-step of under 2.3 milliseconds for a 43,222 atom protein/lipid system. This is equivalent to a simulation rate of over 76 nanoseconds per day and represents an unprecedented time-to-solution for biomolecular simulation as well as continued speed-up to fewer than three atoms per node. On a smaller, solvated lipid system with 13,758 atoms, we have achieved continued speedups through fewer than one atom per node and less than 2 milliseconds/time-step. On a 92,224 atom system, we have achieved floating point performance of over 1.8 TeraFlops/second on 16,384 nodes. Strong scaling of fixed-size classical molecular dynamics of biological systems to large numbers of nodes is necessary to extend the simulation time to the scale required to make contact with experimental data and derive biologically relevant insights.

Performance measurements of the 3D FFT on the blue gene/l supercomputer

This paper presents performance characteristics of a communications-intensive kernel, the complex data 3D FFT, running on the Blue Gene/L architecture. Two implementations of the volumetric FFT algorithm were characterized, one built on the MPI library using an optimized collective all-to-all operation [2] and another built on a low-level System Programming Interface (SPI) of the Blue Gene/L Advanced Diagnostics Environment (BG/L ADE) [17]. We compare the current results to those obtained using a reference MPI implementation (MPICH2 ported to BG/L with unoptimized collectives) and to a port of version 2.1.5 the FFTW library [14]. Performance experiments on the Blue Gene/L prototype indicate that both of our implementations scale well and the current MPI-based implementation shows a speedup of 730 on 2048 nodes for 3D FFTs of size 128 × 128 × 128. Moreover, the volumetric FFT outperforms FFTW port by a factor 8 for a 128× 128× 128 complex FFT on 2048 nodes.

A Volumetric FFT for BlueGene/L

BlueGene/L is a massively parallel supercomputer organized as a three-dimensional torus of compute nodes. A fundamental challenge in harnessing the new computational capabilities of BlueGene/L is the design and implementation of numerical algorithms that scale effectively on thousands of nodes. A computational kernel of particular importance is the Fast Fourier Transform (FFT) of three-dimensional data. In this paper, we present the approach we are taking in BlueGene/L to produce a scalable FFT implementation. We rely on a volume decomposition of the data to take advantage of the toroidal communication topology. We present experimental results using an MPI-based implementation of our algorithm, in order to test the basic tenets behind our decomposition and to allow experimentation on existing platforms. Our preliminary results indicate that our algorithm scales well on as many as 512 nodes for three-dimensional FFTs of size 128 × 128 × 128.

Early performance data on the blue matter molecular simulation framework

Blue Matter is the application framework being developed in conjunction with the scientific portion of the IBM Blue Gene® project. We describe the parallel decomposition currently being used to target the Blue Gene/L machine and discuss the application-based trace tools used to analyze the performance of the application. We also present the results of early performance studies, including a comparison of the performance of the Ewald and the particle-particle particle-mesh (P3ME) methods, compare the measured performance of some key collective operations with the limitations imposed by the hardware, and discuss some future directions for research.