Parallel hierarchical global illumination

Solving the global illumination problem is equivalent to determining the intensity of every wavelength of light in all directions at every point in a given scene. The complexity of the problem has led researchers to use approximation methods for solving the problem on serial computers. Rather than using an approximation method, such as backward ray tracing or radiosity, we have chosen to solve the Rendering Equation by direct simulation of light transport from the light sources. This paper presents an algorithm that solves the Rendering Equation to any desired accuracy, and can be run in parallel on distributed memory or shared memory computer systems with excellent scaling properties. It appears superior in both speed and physical correctness to recent published methods involving bidirectional ray tracing or hybrid treatments of diffuse and specular surfaces. Like progressive radiosity methods, it dynamically refines the geometry decomposition where required, but does so without the excessive storage requirements for ray histories. The algorithm, called Photon, produces a scene which converges to the global illumination solution. This amounts to a huge task for a 1997-vintage serial computer, but using the power of a parallel supercomputer significantly reduces the time required to generate a solution. Currently, Photon can be run on most parallel environments from a shared memory multiprocessor to a parallel supercomputer, as well as on clusters of heterogeneous workstations

Analysis of long branch extraction and long branch shortening.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Long branch attraction (LBA) is a problem that afflicts both the parsimony and maximum likelihood phylogenetic analysis techniques. Research has shown that parsimony is particularly vulnerable to inferring the wrong tree in Felsenstein topologies. The long branch extraction method is a procedure to detect a data set suffering from this problem so that Maximum Likelihood could be used instead of Maximum Parsimony. RESULTS: The long branch extraction method has been well cited and used by many authors in their analysis but no strong validation has been performed as to its accuracy. We performed such an analysis by an extensive search of the branch length search space under two topologies of six taxa, a Felsenstein-like topology and Farris-like topology. We also examine a long branch shortening method. CONCLUSIONS: The long branch extraction method seems to mask the majority of the search space rendering it ineffective as a detection method of LBA. A proposed alternative, the long branch shortening method, is also ineffective in predicting long branch attraction for all tree topologies

Whole Genome Phylogenetic Tree Reconstruction Using Colored de Bruijn Graphs

We present kleuren, a novel assembly-free method to reconstruct phylogenetic trees using the Colored de Bruijn Graph. kleuren works by constructing the Colored de Bruijn Graph and then traversing it, finding bubble structures in the graph that provide phylogenetic signal. The bubbles are then aligned and concatenated to form a supermatrix, from which a phylogenetic tree is inferred. We introduce the algorithms that kleuren uses to accomplish this task, and show its performance on reconstructing the phylogenetic tree of 12 Drosophila species. kleuren reconstructed the established phylogenetic tree accurately, and is a viable tool for phylogenetic tree reconstruction using whole genome sequences. Software package available at: https://github.com/Colelyman/kleurenComment: 6 pages, 3 figures, accepted at BIBE 2017. Minor modifications to the text due to reviewer feedback and fixed typo

Phylogenetic search through partial tree mixing.

BACKGROUND: Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques. RESULTS: When compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at http://dna.cs.byu.edu/psoda CONCLUSIONS: The use of Partial Tree Mixing in a partition based tree space allows the algorithm to quickly converge on near optimal tree regions. These regions can then be searched in a methodical way to determine the overall optimal phylogenetic solution

The Performance Impact of Advance Reservation Meta-scheduling

Abstract As supercomputing resources become more available, users will require resources man-aged by several local schedulers. For example, a user may request 100 processors, a telescope, network bandwidth and a graphics display in order to perform an experiment. In order to gain access to all of these resources (some of which may be in dierent geographical and administrative domains), current systems require meta-jobs like this to run during locked down periods when the resources are only available for meta job use. It is more convenient and eÆcient if the user is able to make a reservation at the soonest time when all of these resources are available. Low utiliza-tion during lock down periods can also be eliminated when meta-jobs are interleaved with existing local usage. System administrators are reluctant to allow reservations external to locked down pe-riods because of the impact reservations may have on utilization and the Quality of Service that the center is able to provide to its normal users. This research quanties the impact of advance reservations on supercomputing center metrics. It also outlines the algorithms that must be used to schedule meta-jobs. The Maui scheduler is used to examine metascheduling using trace les from existing supercomputing centers. These results indicate that advance reservations can improve the response time of supercomputing centers for meta-jobs, while not signicantly impacting overall sys-tem performance. The appropriate balance between meta-jobs and local jobs is also specied using experimental results

Inferring gene regulatory networks from asynchronous microarray data with AIRnet

Background Modern approaches to treating genetic disorders, cancers and even epidemics rely on a detailed understanding of the underlying gene signaling network. Previous work has used time series microarray data to infer gene signaling networks given a large number of accurate time series samples. Microarray data available for many biological experiments is limited to a small number of arrays with little or no time series guarantees. When several samples are averaged to examine differences in mean value between a diseased and normal state, information from individual samples that could indicate a gene relationship can be lost. Results Asynchronous Inference of Regulatory Networks (AIRnet) provides gene signaling network inference using more practical assumptions about the microarray data. By learning correlation patterns for the changes in microarray values from all pairs of samples, accurate network reconstructions can be performed with data that is normally available in microarray experiments. Conclusions By focussing on the changes between microarray samples, instead of absolute values, increased information can be gleaned from expression data

Early environmental conditions shape personality types in a jumping spider

Individuals of many species across the animal kingdom are found to be less plastic than expected, even in behavioral traits. The existence of consistent behavioral differences between individuals, termed personality differences, is puzzling, since plastic behavior is considered ideal to enable animals to adaptively respond to changes in environmental conditions. In order to elucidate which mechanisms are important for the evolution of personality differences, it is crucial to understand which aspects of the environment are important for the development of personality differences. Here, we tested whether physical or social aspects of the environment during development influence individual differentiation (mean level of behavior) using the jumping spider Marpissa muscosa. Furthermore, we assessed whether those behaviors were repeatable, i.e. whether personalities existed. We applied a split-brood design and raised spider siblings in three different environments: a deprived environment with no enrichment, a socially and a physically enriched environment. We focused on exploratory behavior and repeatedly assessed individual behavior in a novel environment and a novel object test. Results show that the environment during development influenced spiders’ exploratory tendencies: spiders raised in enriched environments tended to be more exploratory. Most investigated behaviors were repeatable (i.e. personalities existed) across all individuals tested, whereas only few behaviors were also repeatable across individuals that had experienced the same environmental condition. Taken together, our results indicate that external stimuli can influence the development of one aspect of personality, the inter-individual variation (mean level of behavior), in a jumping spider. We also found family by environment interactions on behavioral traits potentially suggesting genetic variation in developmental plasticity

On the use of cartographic projections in visualizing phylo-genetic tree space

Phylogenetic analysis is becoming an increasingly important tool for biological research. Applications include epidemiological studies, drug development, and evolutionary analysis. Phylogenetic search is a known NP-Hard problem. The size of the data sets which can be analyzed is limited by the exponential growth in the number of trees that must be considered as the problem size increases. A better understanding of the problem space could lead to better methods, which in turn could lead to the feasible analysis of more data sets. We present a definition of phylogenetic tree space and a visualization of this space that shows significant exploitable structure. This structure can be used to develop search methods capable of handling much larger data sets

Accelerated large-scale multiple sequence alignment

<p>Abstract</p> <p>Background</p> <p>Multiple sequence alignment (MSA) is a fundamental analysis method used in bioinformatics and many comparative genomic applications. Prior MSA acceleration attempts with reconfigurable computing have only addressed the first stage of progressive alignment and consequently exhibit performance limitations according to Amdahl's Law. This work is the first known to accelerate the third stage of progressive alignment on reconfigurable hardware.</p> <p>Results</p> <p>We reduce subgroups of aligned sequences into discrete profiles before they are pairwise aligned on the accelerator. Using an FPGA accelerator, an overall speedup of up to 150 has been demonstrated on a large data set when compared to a 2.4 GHz Core2 processor.</p> <p>Conclusions</p> <p>Our parallel algorithm and architecture accelerates large-scale MSA with reconfigurable computing and allows researchers to solve the larger problems that confront biologists today. Program source is available from <url>http://dna.cs.byu.edu/msa/</url>.</p