Susan M. Mniszewski

A categorization approach to automated ontological function annotation.

Automated function prediction (AFP) methods increasingly use knowledge discovery algorithms to map sequence, structure, literature, and/or pathway information about proteins whose functions are unknown into functional ontologies, typically (a portion of) the Gene Ontology (GO). While there are a growing number of methods within this paradigm, the general problem of assessing the accuracy of such prediction algorithms has not been seriously addressed. We present first an application for function prediction from protein sequences using the POSet Ontology Categorizer (POSOC) to produce new annotations by analyzing collections of GO nodes derived from annotations of protein BLAST neighborhoods. We then also present hierarchical precision and hierarchical recall as new evaluation metrics for assessing the accuracy of any predictions in hierarchical ontologies, and discuss results on a test set of protein sequences. We show that our method provides substantially improved hierarchical precision (measure of predictions made that are correct) when applied to the nearest BLAST neighbors of target proteins, as compared with simply imputing that neighborhoods annotations to the target. Moreover, when our method is applied to a broader BLAST neighborhood, hierarchical precision is enhanced even further. In all cases, such increased hierarchical precision performance is purchased at a modest expense of hierarchical recall (measure of all annotations that get predicted at all).

Efficient coupling of parallel applications using PAWS

PAWS (Parallel Application WorkSpace) is a software infrastructure for use in connecting separate parallel applications within a component-like model. A central PAWS Controller coordinates the linking of serial or parallel applications across a network to allow them to share parallel data structures such as multidimensional arrays. Applications use the PAWS API to indicate which data structures are to be shared and at what points the data is ready to be sent or received. PAWS implements a general parallel data descriptor and automatically carries out parallel layout remapping when necessary. Connections can be dynamically established and dropped, and can use multiple data transfer pathways between applications. PAWS uses the NEXUS communication library and is independent of the applications parallel communication mechanism.

PAWS: collective interactions and data transfers

The authors discuss problems and solutions pertaining to the interaction of components representing parallel applications. We introduce the notion of a collective port which is an extension of the Common Component Architecture (CCA) ports and allows collective components representing parallel applications to interact as one entity. We further describe a class of translation components, which translate between the distributed data format used by one parallel implementation to that used by another. A well known example of such components is the MxN component which translates between data distributed on M processors to data distributed on N processors. We describe its implementation in Parallel Application Work Space (PAWS), as well as the data structures PAWS uses to support it. We also present a mechanism allowing the framework to invoke this component on the programmers behalf whenever such translation is necessary, freeing the programmer from treating collective component interactions as a special case. In doing that, we introduce framework-based, user-defined distributed type casts. Finally, we discuss our initial experiments in building optimized complex translation components out of atomic functionalities.

Protein annotation as term categorization in the gene ontology using word proximity networks

BackgroundWe participated in the BioCreAtIvE Task 2, which addressed the annotation of proteins into the Gene Ontology (GO) based on the text of a given document and the selection of evidence text from the document justifying that annotation. We approached the task utilizing several combinations of two distinct methods: an unsupervised algorithm for expanding words associated with GO nodes, and an annotation methodology which treats annotation as categorization of terms from a proteins document neighborhood into the GO.ResultsThe evaluation results indicate that the method for expanding words associated with GO nodes is quite powerful; we were able to successfully select appropriate evidence text for a given annotation in 38% of Task 2.1 queries by building on this method. The term categorization methodology achieved a precision of 16% for annotation within the correct extended family in Task 2.2, though we show through subsequent analysis that this can be improved with a different parameter setting. Our architecture proved not to be very successful on the evidence text component of the task, in the configuration used to generate the submitted results.ConclusionThe initial results show promise for both of the methods we explored, and we are planning to integrate the methods more closely to achieve better results overall.

Revisiting wavelet compression for large-scale climate data using JPEG 2000 and ensuring data precision

We revisit wavelet compression by using a standards-based method to reduce large-scale data sizes for production scientific computing. Many of the bottlenecks in visualization and analysis come from limited bandwidth in data movement, from storage to networks. The majority of the processing time for visualization and analysis is spent reading or writing large-scale data or moving data from a remote site in a distance scenario. Using wavelet compression in JPEG 2000, we provide a mechanism to vary data transfer time versus data quality, so that a domain expert can improve data transfer time while quantifying compression effects on their data. By using a standards-based method, we are able to provide scientists with the state-of-the-art wavelet compression from the signal processing and data compression community, suitable for use in a production computing environment. To quantify compression effects, we focus on measuring bit rate versus maximum error as a quality metric to provide precision guarantees for scientific analysis on remotely compressed POP (Parallel Ocean Program) data.

A default hierarchy for pronouncing English

The authors study the principles governing the power and efficiency of the default hierarchy, a system of knowledge acquisition and representation. The default hierarchy trains automatically, yet yields a set of rules which can be easily assessed and analyzed. Rules are organized in a hierarchical structure containing general (default) and specific rules. In training the hierarchy, general rules are learned before specific rules. In using the hierarchy, specific rules are accessed first, with default rules used when no specific rules apply. The main results concern the properties of the default hierarchy architecture, as revealed by its application to English pronunciation. Evaluating the hierarchy as a pronouncer of English, the authors find that its rules capture several key features of English spelling. The default hierarchy pronounces English better than the neural network NETtalk, and almost as well as expert-devised systems. >

Computing the Density Matrix in Electronic Structure Theory on Graphics Processing Units.

The self-consistent solution of a Schrödinger-like equation for the density matrix is a critical and computationally demanding step in quantum-based models of interatomic bonding. This step was tackled historically via the diagonalization of the Hamiltonian. We have investigated the performance and accuracy of the second-order spectral projection (SP2) algorithm for the computation of the density matrix via a recursive expansion of the Fermi operator in a series of generalized matrix-matrix multiplications. We demonstrate that owing to its simplicity, the SP2 algorithm [Niklasson, A. M. N. Phys. Rev. B2002, 66, 155115] is exceptionally well suited to implementation on graphics processing units (GPUs). The performance in double and single precision arithmetic of a hybrid GPU/central processing unit (CPU) and full GPU implementation of the SP2 algorithm exceed those of a CPU-only implementation of the SP2 algorithm and traditional matrix diagonalization when the dimensions of the matrices exceed about 2000 × 2000. Padding schemes for arrays allocated in the GPU memory that optimize the performance of the CUBLAS implementations of the level 3 BLAS DGEMM and SGEMM subroutines for generalized matrix-matrix multiplications are described in detail. The analysis of the relative performance of the hybrid CPU/GPU and full GPU implementations indicate that the transfer of arrays between the GPU and CPU constitutes only a small fraction of the total computation time. The errors measured in the self-consistent density matrices computed using the SP2 algorithm are generally smaller than those measured in matrices computed via diagonalization. Furthermore, the errors in the density matrices computed using the SP2 algorithm do not exhibit any dependence of system size, whereas the errors increase linearly with the number of orbitals when diagonalization is employed.

Designing systems for large-scale, discrete-event simulations: Experiences with the FastTrans parallel microsimulator

We describe the various aspects involved in building FastTrans, a scalable, parallel microsimulator for transportation networks that can simulate and route tens of millions of vehicles on real-world road networks in a fraction of real time. Vehicular trips are generated using agent-based simulations that provide realistic, daily activity schedules for a synthetic population of millions of intelligent agents. We use parallel discrete-event simulation techniques and distributed-memory algorithms to scale these simulations to over one thousand compute nodes. We present various optimizations for speeding up simulation execution times, including (i) a set of routing algorithms such as variations of Dijkstras shortest path algorithm and heuristic-based A⋆ search, and (ii) a number of different partitioning schemes for load balancing, including geographic partitioning (that assigns simulation entities that are geographically close by to the same processor) and scattering (that assigns geographically close by entities to different processors). Our main findings include: (i) A⋆ significantly outperforms other routing algorithms while computing near-optimal paths; (ii) surprisingly, scattering outperforms more sophisticated partitioning schemes by achieving near-perfect load-balancing. With optimized routing and partitioning, FastTrans is able to simulate a full 24 hour work-day in New York — involving over one million road links and approximately 25 million vehicular trips — in less than one hour of wall-clock time on a 512-node cluster.

Graph-based linear scaling electronic structure theory

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

TADSim: Discrete Event-Based Performance Prediction for Temperature-Accelerated Dynamics

Next-generation high-performance computing will require more scalable and flexible performance prediction tools to evaluate software--hardware co-design choices relevant to scientific applications and hardware architectures. We present a new class of tools called application simulators—parameterized fast-running proxies of large-scale scientific applications using parallel discrete event simulation. Parameterized choices for the algorithmic method and hardware options provide a rich space for design exploration and allow us to quickly find well-performing software--hardware combinations. We demonstrate our approach with a TADSim simulator that models the temperature-accelerated dynamics (TAD) method, an algorithmically complex and parameter-rich member of the accelerated molecular dynamics (AMD) family of molecular dynamics methods. The essence of the TAD application is captured without the computational expense and resource usage of the full code. We accomplish this by identifying the time-intensive elements, quantifying algorithm steps in terms of those elements, abstracting them out, and replacing them by the passage of time. We use TADSim to quickly characterize the runtime performance and algorithmic behavior for the otherwise long-running simulation code. We extend TADSim to model algorithm extensions, such as speculative spawning of the compute-bound stages, and predict performance improvements without having to implement such a method. Validation against the actual TAD code shows close agreement for the evolution of an example physical system, a silver surface. Focused parameter scans have allowed us to study algorithm parameter choices over far more scenarios than would be possible with the actual simulation. This has led to interesting performance-related insights and suggested extensions.