In the field of nanoparticle material science, X-ray scattering techniques are widely used for characterization of macromolecules and particle systems (ordered, partially-ordered or custom) based on their structural properties at the micro- and nano-scales. Numerous applications utilize these, including design and fabrication of energy-relevant nanodevices such as photovoltaic and energy storage devices. Due to its size, analysis of raw data obtained through present ultra-fast light beamlines and X-ray scattering detectors has been a primary bottleneck in such characterization processes. To address this hurdle, we are developing high-performance parallel algorithms and codes for analysis of X-ray scattering data for several of the scattering methods, such as the Small Angle X-ray Scattering (SAXS), which we talk about in this paper. As an inverse modeling problem, structural fitting of the raw data obtained through SAXS experiments is a method used for extracting meaningful information on the structural properties of materials. Such fitting processes involve a large number of variable parameters and, hence, require a large amount of computational power. In this paper, we focus on this problem and present a high-performance and scalable parallel solution based on the Reverse Monte Carlo simulation algorithm, on highly-parallel systems such as clusters of multicore CPUs and graphics processors. We have implemented and optimized our algorithm on generic multi-core CPUs as well as the Nvidia GPU architectures with C++ and CUDA. We also present detailed performance results and computational analysis of our code.

Although present X-ray scattering techniques can provide tremendous information on the nano-structural properties of materials that are valuable in the design and fabrication of energy-relevant nano-devices, a primary challenge remains in the analyses of such data. In this paper we describe a high-performance, flexible, and scalable Grazing Incidence Small Angle X-ray Scattering simulation algorithm and codes that we have developed on multi-core/CPU and many-core/GPU clusters. We discuss in detail our implementation, optimization and performance on these platforms. Our results show speedups of ~125x on a Fermi-GPU and ~20x on a Cray-XE6 24-core node, compared to a sequential CPU code, with near linear scaling on multi-node clusters. To our knowledge, this is the first GISAXS simulation code that is flexible to compute scattered light intensities in all spatial directions allowing full reconstruction of GISAXS patterns for any complex structures and with highresolutions while reducing simulation times from months to minutes.

Direct computation of all pairwise distances or interactions is a fundamental problem that arises in many application areas including particle or atomistic simulations, fluid dynamics, computational electromagnetics, materials science, genomics and systems biology, and clustering and data mining. In this paper, we present methods for performing such pairwise computations efficiently in parallel on Cell processors. This problem is particularly challenging on the Cell processor due to the small sized Local Stores of the Synergistic Processing Elements, the main computational cores of the processor. We present techniques for different variants of this problem including those with large number of entities or when the dimensionality of the information per entity is large. We demonstrate our methods in the context of multiple applications drawn from fluid dynamics, materials science and systems biology, and present detailed experimental results. Our software library is an open source and can be readily used by application scientists to accelerate pairwise computations using Cell accelerators.

Constructing genome-wide gene regulatory networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, none of them is parallel, and they do not scale to the whole genome level or incorporate the largest data sets, particularly with rigorous statistical techniques. In this paper, we present a parallel method integrating mutual information, data processing inequality, and statistical testing to detect significant dependencies between genes, and efficiently exploit parallelism inherent in such computations. We present a new method to carry out permutation testing for assessing statistical significance of interactions, while reducing its computational complexity by a factor of Θ(n2), where n is the number of genes. Using both synthetic and known regulatory networks, we show that our method produces networks of quality similar to ARACNe, a widely used mutual-information-based method. We further explore the use of accelerators for gene network construction by presenting a parallelization on a cluster of IBM Cell blades. We exploit parallelization across multiple Cells, multiple cores within each Cell, and vector units within the cores to develop a high-performance implementation that effectively addresses the scaling problem. We report the first inference of a plant whole genome network by constructing a 15,222 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in 30 minutes on a 2,048-CPU IBM Blue Gene/L, and in 2 hours and 25 minutes on a 8-node Cell blade cluster.

Constructing genome-wide gene regulatory networks from a large number of gene expression profile measurements is an important problem in systems biology. While several techniques have been developed, none of them is parallel, and they lack the capability to scale to the whole-genome level or incorporate the largest data sets, particularly with rigorous statistical testing. To address this problem, we recently developed a mutual information theory based parallel method for gene network reconstruction. In this paper, we extend this work to a cluster of Cell processors. We use parallelization across multiple Cells, multiple cores within each Cell, and vector units within the cores to develop a high performance implementation that effectively addresses the scaling problem. We present experimental results comparing the Cell implementation with a standard uniprocessor implementation and an implementation on a conventional supercomputer. Finally, we report the construction of a large 15,203 gene network of the plant Arabidopsis thaliana from 2,996 microarray experiments on a 8-node Cell blade cluster in 2 hours and 24 minutes.

Genomic alignments, as a means to uncover evolutionary relationships among organisms, are a fundamental tool in computational biology. There is considerable recent interest in using the Cell Broadband Engine, a heterogeneous multicore chip that provides high performance, for biological applications. However, work in genomic alignments so far has been limited to computing optimal alignment scores using quadratic space for the basic global/local alignment problem. In this paper, we present a comprehensive study of developing alignment algorithms on the Cell, exploiting its thread and data level parallelism features. First, we develop a parallel implementation on the Cell that computes optimal alignments and adopts Hirschberg's linear space technique. The former is essential, as merely computing optimal alignment scores is not useful, while the latter is needed to permit alignments of longer sequences. We then present Cell implementations of two advanced alignment techniques-spliced alignments and syntenic alignments. Spliced alignments are useful in aligning mRNA sequences with corresponding genomic sequences to uncover the gene structure. Syntenic alignments are used to discover conserved exons and other sequences between long genomic sequences from different organisms. We present experimental results for these three types of alignments on 16 Synergistic Processing Elements of the IBM QS20 dual-Cell blade system.

Sequence alignment and its many variants are a fundamental tool in computational biology. There is considerable recent interest in using the cell broadband engine, a heterogenous multi-core chip that provides high performance, for biological applications. However, work so far has been limited to computing optimal alignment scores using quadratic space under the basic global/local alignment algorithm. In this paper, we present a comprehensive study of developing sequence alignment algorithms on the Cell exploiting its thread and data level parallelism features. First, we develop a cell implementation that computes optimal alignments and adopts Hirschberg's linear space technique. The former is essential as merely computing optimal alignment scores is not useful while the latter is needed to permit alignments of longer sequences. We then present cell implementations of two advanced alignment techniques - spliced alignments and syntenic alignments. In a spliced alignment, consecutive non-overlapping portions of a sequence align with ordered non-overlapping, but non-consecutive portions of another sequence. Spliced alignments are useful in aligning mRNA sequences with corresponding genomic sequences to uncover gene structure. Syntenic alignments are used to discover conserved exons and other sequences between long genomic sequences from different organisms. We present experimental results for these three types of alignments on the Cell BE and report speedups of about 4 on six SPUs on the Playstation 3, when compared to the respective best serial algorithms on the Cell BE and the Pentium 4 processor.

Multiagent systems have emerged as an active sub field of artificial intelligence. Machine learning techniques have played a significant role by handling the inherent complexity of such systems. Robotic Soccer is a typical multiagent system, wherein the challenge is to develop and hone the skills of the agents that take part in the game. For an in-depth and sophisticated understanding of the game, soccer-playing agents must possess the capability to learn and acquire low-level skills. These skills can later be put together and used to emulate the expertise of experienced players. This paper describes the use of reinforcement learning, a machine learning technique, to acquire the base level skills of intercepting a moving ball. Results of simulation runs using the Robocup Soccer server have also been presented.