A method is provided for monitoring broadcast audio con tent. According to the method, a broadcast datastream is received, and audio identifying information is generated for audio content from the broadcast datastream. It is deter mined whether the audio identifying information generated for the broadcast audio content matches audio identifying information in an audio content database. In one preferred embodiment, the audio identifying information is an audio feature Signature that is based on audio content. Also pro Vided is a System for monitoring broadcast audio content.

Storage class memory is receiving increasing attention for use in HPC systems for the acceleration of intensive IO operations. We report a particular instance using SLC FLASH memory integrated with an IBM BlueGene/Q supercomputer at scale (Blue Gene Active Storage, BGAS). We describe two principle modes of operation of the non-volatile memory: 1) block device; 2) direct storage access (DSA). The block device layer, built on the DSA layer, provides compatibility with IO layers common to existing HPC IO systems (POSIX, MPIO, HDF5) and is expected to provide high performance in bandwidth critical use cases. The novel DSA strategy enables a low-overhead, byte addressable, asynchronous, kernel by-pass access method for very high user space IOPs in multithreaded application environments. Here, we expose DSA through HDF5 using a custom file driver. Benchmark results for the different modes are presented and scale-out to full system size showcases the capabilities of this technology.

We present the Active Storage Fabrics (ASF) model for storage embedded parallel processing as a way to address petascale data intensive challenges. ASF is aimed at emerging scalable system-on-a-chip, storage class memory architectures, but may be realized in prototype form on current parallel systems. ASF can be used to transparently accelerate host workloads by close integration at the middleware data/storage boundary or directly by data intensive applications. We provide an overview of the major components involved in accelerating a parallel file system and a relational database management system, describe some early results, and outline our current research directions.

We have performed molecular dynamics simulations for a total duration of more than 10 µs (with most molecular trajectories being 1 µs in duration) to study, the effect of a single mutation on hen lysozyme protein stability and denaturing, using an IBM Blue Gene/L ™ supercomputer. One goal of this study was to assess the use of certain force fields to reproduce experimental results of protein unfolding using thermal denaturing techniques. A second and more important goal was to gain microscopic insights into the mechanism of protein misfolding using both thermal and chemical denaturing techniques. We found that the thermal denaturing results were robust and reproducible with various force fields. The chemical denaturing results explained why the single amino-acid mutation on residue Trp62 causes the disruption of long-range interactions in the tertiary structure. Simulation results revealed that the Trp62 residue was the key to a cooperative long-range interaction within the wild-type protein. Specifically, Trp62 acts as a bridge between two neighboring basic residues through a π-type H-bond or π-cation interaction to form an Arg-Trp-Arg "sandwich-like" structure. Our findings support the general conclusions of the experiment and provide an interesting molecular depiction of the disruption of the long-range interactions.

N-body simulations present some of the most interesting challenges in the area of massively parallel computing, especially when the object is to improve the time to solution for a fixed-size problem. The Blue Matter molecular simulation framework was developed specifically to address these challenges, to explore programming models for massively parallel machine architectures in a concrete context, and to support the scientific goals of the IBM Blue Gene® Project. This paper reviews the key issues involved in achieving ultrastrong scaling of methodologically correct biomolecular simulations, particularly the treatment of the long-range electrostatic forces present in simulations of proteins in water and membranes. Blue Matter computes these forces using the particle-particle particle-mesh Ewald (P3ME) method, which breaks the problem up into two pieces, one that requires the use of three-dimensional fast Fourier transforms with global data dependencies and another that involves computing interactions between pairs of particles within a cutoff distance. We summarize our exploration of the parallel decompositions used to compute these finite-ranged interactions, describe some of the implementation details involved in these decompositions, and present the evolution of strong-scaling performance achieved over the course of this exploration, along with evidence for the quality of simulation achieved.

This paper describes a parallel strategy to extend the scalability of a small 3D FFT on thousands of Blue Gene/L processors. The approach is to execute the intermediate phases of the 3D FFT on smaller processor subsets. Performance measurements of the standalone 3D FFT on two communication protocols, MPI and BG/L ADE are presented. While the performance of the 3D-FFT with MPI-based and BG/L ADE-based implementations exhibited qualitatively similar behavior, the BG/L ADE-based version has lower communication cost than the MPI based version for small message sizes. Measurements also show that the proposed approach is effective in improving Particle-Mesh-based N-body simulation performance significantly at the limits of scalability.

This paper describes some of the issues involved with scaling biomolecular simulations onto massively parallel machines drawing on the Blue Matter application team's experiences with Blue Gene/L. Our experiences in scaling biomolecular simulation to one atom/node on BG/L should be relevant to scaling biomolecular simulations onto larger peta-scale platforms because the path to increased performance is through the exploitation of increased concurrency so that even larger systems will have to operate in the extreme strong scaling regime. Petascale platforms also present challenges with regard to the correctness of biomolecular simulations since longer time-scale simulations are more likely to encounter significant energy drift. Total energy drift data for a microsecond-scale simulation is presented along with the measured scalability of various components of a molecular dynamics time-step.

Biomolecular simulations enabled by massively parallel supercomputers such as BlueGene/L promise to bridge the gap between the currently accessible simulation time scale and the experimental time scale for many important protein folding processes. In this study, molecular dynamics simulations were carried out for both the wild-type and the mutant hen lysozyme (TRP62GLY) to study the single mutation effect on lysozyme stability and misfolding. Our thermal denaturing simulations at 400-500 K with both the OPLSAA and the CHARMM force fields show that the mutant structure is indeed much less stable than the wild-type, which is consistent with the recent urea denaturing experiment (Dobson et al. Science 2002, 295, 1719-1722; Nature 2003, 424, 783-788). Detailed results also reveal that the single mutation TRP62GLY first induces the loss of native contacts in the beta-domain region of the lysozyme protein at high temperatures, and then the unfolding process spreads into the alpha-domain region through Helix C. Even though the OPLSAA force field in general shows a more stable protein structure than does the CHARMM force field at high temperatures, the two force fields examined here display qualitatively similar results for the misfolding process, indicating that the thermal denaturing of the single mutation is robust and reproducible with various modern force fields.

Abstract Although we receive a relatively static view of molecular structure from spectroscopic tools such as x-ray crystallography and nuclear magnetic resonance (NMR), the reality is that molecules are in constant motion at biological temperatures. Intermolecular motions, such as the binding or unbinding of an antibody–antigen complex, have an important role in biological processes. In addition, biomolecules are always flexing, bending, and stretching in ways that affect their function. For example, many proteins display allosteric behavior in which the binding of a ligand to some site on the protein causes the protein to change its shape. This can result in the active site of an enzyme becoming operational. The folding and unfolding of a protein are more extreme examples of intramolecular motions that have a profound impact on biological function. One way to understand the motions of biological molecules is by using a computer to simulate those motions explicitly. The computational techniques used to model intra- and intermolecular motions are known as molecular simulations. Molecular simulations are a set of computational methods that allow the modeling of the motions of molecules. Molecular motions are coupled to the environment—other biomolecules, cofactors, counterions, and water. A typical molecular simulation involves simulating the motions of all of the atoms of a protein or nucleic acid along with all of the surrounding water molecules.

The replica exchange method is a popular approach for studying the folding thermodynamics of small to modest size proteins in explicit solvent, since it is easily parallelized. However, replica exchange can become computationally expensive for large-scale studies, due to the number of replicas needed as well as interprocess or communication requirements both between and within replicas. In this paper, we discuss an implementation of replica exchange molecular dynamics on Blue Gene/L for performing large scale simulation studies of systems of biological interest. The algorithm is tuned with an awareness of the physical network topology and hardware performance features of the Blue Gene/L architecture. Performance measurements for replica exchange using the blue matter molecular dynamics application are presented on Blue Gene/L hardware with up to 256 replicas simulated on 8,192 compute nodes. Both scalability and performance are achieved with this implementation.

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.