Improving Performance of Iterative Methods by Lossy Checkponting

Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale iterative methods are running with a large number of ranks in parallel, they have to checkpoint the dynamic variables periodically in case of unavoidable fail-stop errors, requiring fast I/O systems and large storage space. To this end, significantly reducing the checkpointing overhead is critical to improving the overall performance of iterative methods. Our contribution is fourfold. (1) We propose a novel lossy checkpointing scheme that can significantly improve the checkpointing performance of iterative methods by leveraging lossy compressors. (2) We formulate a lossy checkpointing performance model and derive theoretically an upper bound for the extra number of iterations caused by the distortion of data in lossy checkpoints, in order to guarantee the performance improvement under the lossy checkpointing scheme. (3) We analyze the impact of lossy checkpointing (i.e., extra number of iterations caused by lossy checkpointing files) for multiple types of iterative methods. (4)We evaluate the lossy checkpointing scheme with optimal checkpointing intervals on a high-performance computing environment with 2,048 cores, using a well-known scientific computation package PETSc and a state-of-the-art checkpoint/restart toolkit. Experiments show that our optimized lossy checkpointing scheme can significantly reduce the fault tolerance overhead for iterative methods by 23%~70% compared with traditional checkpointing and 20%~58% compared with lossless-compressed checkpointing, in the presence of system failures.Comment: 14 pages, 10 figures, HPDC'1

The Jurisdiction of the D.C. Circuit

The U.S. Court of Appeals for the D.C. Circuit is unique among federal courts, well known for an unusual caseload that is disproportionally weighted toward administrative law. What explains that unusual caseload? This Article explores that question. We identify several factors that “push” some types of cases away from the Circuit and several factors that “pull” other cases to it. We give particular focus to the jurisdictional provisions of federal statutes, which reveal congressional intent about the types of actions over which the D.C. Circuit should have special jurisdiction. Through a comprehensive examination of the U.S. Code, we identify several trends. First, the Congress is more likely to give the D.C. Circuit exclusive jurisdiction over the review of administrative rulemaking than over the review of agency decisions imposing a penalty. Second, the Congress is more likely to give the D.C. Circuit exclusive jurisdiction over the review of independent agency actions than over the review of executive agency actions. Finally, the Congress tends to grant the D.C. Circuit exclusive jurisdiction over matters that are likely to have a national effect. In sum, we explore what makes this court unique, from its history to its modern docket and jurisdiction

Intragenic recombination between pseudogenes as a source of new disease specificity at a simple resistance locus

BACKGROUND: Pooling of multi-site MRI data is often necessary when a large cohort is desired. However, different scanning platforms can introduce systematic differences which confound true effects of interest. One may reduce multi-site bias by calibrating pivotal scanning parameters, or include them as covariates to improve the data integrity. NEW METHOD: In the present study we use a source-based morphometry (SBM) model to explore scanning effects in multi-site sMRI studies and develop a data-driven correction. Specifically, independent components are extracted from the data and investigated for associations with scanning parameters to assess the influence. The identified scanning-related components can be eliminated from the original data for correction. RESULTS: A small set of SBM components captured most of the variance associated with the scanning differences. In a dataset of 1460 healthy subjects, pronounced and independent scanning effects were observed in brainstem and thalamus, associated with magnetic field strength-inversion time and RF-receiving coil. A second study with 110 schizophrenia patients and 124 healthy controls demonstrated that scanning effects can be effectively corrected with the SBM approach. COMPARISON WITH EXISTING METHOD(S): Both SBM and GLM correction appeared to effectively eliminate the scanning effects. Meanwhile, the SBM-corrected data yielded a more significant patient versus control group difference and less questionable findings. CONCLUSIONS: It is important to calibrate scanning settings and completely examine individual parameters for the control of confounding effects in multi-site sMRI studies. Both GLM and SBM correction can reduce scanning effects, though SBM's data-driven nature provides additional flexibility and is better able to handle collinear effects

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures

The regulatory and transcriptional landscape associated with carbon utilization in a filamentous fungus.

Filamentous fungi, such as Neurospora crassa, are very efficient in deconstructing plant biomass by the secretion of an arsenal of plant cell wall-degrading enzymes, by remodeling metabolism to accommodate production of secreted enzymes, and by enabling transport and intracellular utilization of plant biomass components. Although a number of enzymes and transcriptional regulators involved in plant biomass utilization have been identified, how filamentous fungi sense and integrate nutritional information encoded in the plant cell wall into a regulatory hierarchy for optimal utilization of complex carbon sources is not understood. Here, we performed transcriptional profiling of N. crassa on 40 different carbon sources, including plant biomass, to provide data on how fungi sense simple to complex carbohydrates. From these data, we identified regulatory factors in N. crassa and characterized one (PDR-2) associated with pectin utilization and one with pectin/hemicellulose utilization (ARA-1). Using in vitro DNA affinity purification sequencing (DAP-seq), we identified direct targets of transcription factors involved in regulating genes encoding plant cell wall-degrading enzymes. In particular, our data clarified the role of the transcription factor VIB-1 in the regulation of genes encoding plant cell wall-degrading enzymes and nutrient scavenging and revealed a major role of the carbon catabolite repressor CRE-1 in regulating the expression of major facilitator transporter genes. These data contribute to a more complete understanding of cross talk between transcription factors and their target genes, which are involved in regulating nutrient sensing and plant biomass utilization on a global level

Biogeochemical hotspots in Forested Landscapes: The Role of Vernal Pools in Denitrification and Organic Matter

Quantifying spatial and temporal heterogeneity in ecosystem processes presents a challenge for conserving ecosystem function across landscapes. In particular, many ecosystems contain small features that play larger roles in ecosystem processes than their size would indicate; thus, they may represent ‘‘hotspots’’ of activity relative to their surroundings. Biogeochemical hotspots are characterized as small features within a landscape that show comparatively high chemical reaction rates. In northeastern forests in North America, vernal pools are abundant, small features that typically fill in spring with snow melt and precipitation and dry by the end of summer. Ephemeral flooding alters soil moisture and the depth of the soil’s oxic/anoxic boundary, which may affect biogeochemical processes. We studied the effects of vernal pools on leaf-litter decomposition rates, soil enzyme activity, and denitrification in vernal pools to assess whether they function as biogeochemical hotspots. Our results indicate that seasonal inundation enhanced leaf-litter decomposition, denitrification, and enzyme activity in vernal pools relative to adjacent forest sites. Leaves in seasonally flooded areas decomposed faster than leaves in terra firme forest sites. Flooding also influenced the C, N, and P stoichiometry of decomposing leaf litter and explained the variance in microbial extracellular enzyme activity for phosphatase, β-D- glucosidase, and β-N-acetylglucosaminidase. Additionally, denitrification rates were enhanced by seasonal flooding across all of the study pools. Collectively, these data suggest that vernal pool eco- systems may function as hotspots of leaf-litter decomposition and denitrification and play a significant role in decomposition and nutrient dynamics relative to their size

Joint Elastic Side-Scattering Lidar and Raman Lidar Measurements of Aerosol Optical Properties in South East Colorado

We describe an experiment, located in south-east Colorado, USA, that measured aerosol optical depth profiles using two Lidar techniques. Two independent detectors measured scattered light from a vertical UV laser beam. One detector, located at the laser site, measured light via the inelastic Raman backscattering process. This is a common method used in atmospheric science for measuring aerosol optical depth profiles. The other detector, located approximately 40km distant, viewed the laser beam from the side. This detector featured a 3.5m2 mirror and measured elastically scattered light in a bistatic Lidar configuration following the method used at the Pierre Auger cosmic ray observatory. The goal of this experiment was to assess and improve methods to measure atmospheric clarity, specifically aerosol optical depth profiles, for cosmic ray UV fluorescence detectors that use the atmosphere as a giant calorimeter. The experiment collected data from September 2010 to July 2011 under varying conditions of aerosol loading. We describe the instruments and techniques and compare the aerosol optical depth profiles measured by the Raman and bistatic Lidar detectors.Comment: 34 pages, 16 figure

How much effort is required to accurately describe the complex ecology of a rodent‐borne viral disease?

We use data collected on 18, 1-ha live trapping grids monitored from 1994 through 2005 and on five of those grids through 2013 in the mesic northwestern United States to illustrate the complexity of the deer mouse (Peromyscus maniculatus)/Sin Nombre virus (SNV) host-pathogen system. Important factors necessary to understand zoonotic disease ecology include those associated with distribution and population dynamics of reservoir species as well as infection dynamics. Results are based on more than 851,000 trap nights, 16,608 individual deer mice and 10,572 collected blood samples. Deer mice were distributed throughout every habitat we sampled and were present during every sampling period in all habitats except high altitude habitats over 1900 m. Abundance varied greatly among locations with peak numbers occurring mostly during fall. However, peak rodent abundance occurred during fall, winter and spring during various years on three grids trapped 12 months/yr. Prevalence of antibodies to SNV averaged 3.9% to 22.1% but no grids had mice with antibodies during every month. The maximum period without antibody-positive mice ranged from 1 to 52 months, or even more at high altitude grids where deer mice were not always present. Months without antibody-positive mice were more prevalent during fall than spring. Population fluctuations were not synchronous over broad geographic areas and antibody prevalences were not well spatially consistent, differing greatly over short distances. We observed an apparently negative, but nonstatistically significant relationship between average antibody prevalence and average deer mouse population abundance and a statistically significant positive relationship between the average number of antibody positive mice and average population abundance. We present data from which potential researchers can estimate the effort required to adequately describe the ecology of a rodentborne viral system. We address different factors affecting population dynamics and hantavirus antibody prevalence and discuss the path to understanding a complex rodent-borne disease system as well as the obstacles in that path.Fil: Douglass, Richard J.. University of Montana; Estados UnidosFil: Vadell, María Victoria. Universidad Nacional de San Martín. Instituto de Investigaciones e Ingeniería Ambiental. Laboratorio de Ecología de Enfermedades Transmitidas por Vectores; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecología, Genética y Evolución; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin