70,813 research outputs found

    Main memory in HPC: do we need more, or could we live with less?

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    An important aspect of High-Performance Computing (HPC) system design is the choice of main memory capacity. This choice becomes increasingly important now that 3D-stacked memories are entering the market. Compared with conventional Dual In-line Memory Modules (DIMMs), 3D memory chiplets provide better performance and energy efficiency but lower memory capacities. Therefore, the adoption of 3D-stacked memories in the HPC domain depends on whether we can find use cases that require much less memory than is available now. This study analyzes the memory capacity requirements of important HPC benchmarks and applications. We find that the High-Performance Conjugate Gradients (HPCG) benchmark could be an important success story for 3D-stacked memories in HPC, but High-Performance Linpack (HPL) is likely to be constrained by 3D memory capacity. The study also emphasizes that the analysis of memory footprints of production HPC applications is complex and that it requires an understanding of application scalability and target category, i.e., whether the users target capability or capacity computing. The results show that most of the HPC applications under study have per-core memory footprints in the range of hundreds of megabytes, but we also detect applications and use cases that require gigabytes per core. Overall, the study identifies the HPC applications and use cases with memory footprints that could be provided by 3D-stacked memory chiplets, making a first step toward adoption of this novel technology in the HPC domain.This work was supported by the Collaboration Agreement between Samsung Electronics Co., Ltd. and BSC, Spanish Government through Severo Ochoa programme (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under ExaNoDe project (grant agreement No 671578). Darko Zivanovic holds the Severo Ochoa grant (SVP-2014-068501) of the Ministry of Economy and Competitiveness of Spain. The authors thank Harald Servat from BSC and Vladimir Marjanovi´c from High Performance Computing Center Stuttgart for their technical support.Postprint (published version

    The Effects of Different Footprint Sizes and Cloud Algorithms on the Top-Of-Atmosphere Radiative Flux Calculation from the Clouds and Earths Radiant Energy System (CERES) Instrument on Suomi National Polar-Orbiting Partnership (NPP)

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    Only one Clouds and Earths Radiant Energy System (CERES) instrument is onboard the Suomi National Polar-orbiting Partnership (NPP) and it has been placed in cross-track mode since launch; it is thus not possible to construct a set of angular distribution models (ADMs) specific for CERES on NPP. Edition 4 Aqua ADMs are used for flux inversions for NPP CERES measurements. However, the footprint size of NPP CERES is greater than that of Aqua CERES, as the altitude of the NPP orbit is higher than that of the Aqua orbit. Furthermore, cloud retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), which are the imagers sharing the spacecraft with NPP CERES and Aqua CERES, are also different. To quantify the flux uncertainties due to the footprint size difference between Aqua CERES and NPP CERES, and due to both the footprint size difference and cloud property difference, a simulation is designed using the MODIS pixel-level data, which are convolved with the Aqua CERES and NPP CERES point spread functions (PSFs) into their respective footprints. The simulation is designed to isolate the effects of footprint size and cloud property differences on flux uncertainty from calibration and orbital differences between NPP CERES and Aqua CERES. The footprint size difference between Aqua CERES and NPP CERES introduces instantaneous flux uncertainties in monthly gridded NPP CERES measurements of less than 4.0 W/sq. m for SW (shortwave) and less than 1.0 W/sq. m for both daytime and nighttime LW (longwave). The global monthly mean instantaneous SW flux from simulated NPP CERES has a low bias of 0.4 W/sq. m when compared to simulated Aqua CERES, and the root-mean-square (RMS) error is 2.2 W/sq. m between them; the biases of daytime and night- time LW flux are close to zero with RMS errors of 0.8 and 0.2 W/sq. m. These uncertainties are within the uncertainties of CERES ADMs. When both footprint size and cloud property (cloud fraction and optical depth) differences are considered, the uncertainties of monthly gridded NPP CERES SW flux can be up to 20 W/sq. m in the Arctic regions where cloud optical depth retrievals from VIIRS differ significantly from MODIS. The global monthly mean instantaneous SW flux from simulated NPP CERES has a high bias of 1.1 W/sq. m and the RMS error increases to 5.2 W/sq. m. LW flux shows less sensitivity to cloud property differences than SW flux, with uncertainties of about 2 W/sq. m in the monthly gridded LW flux, and the RMS errors of global monthly mean daytime and nighttime fluxes increase only slightly. These results highlight the importance of consistent cloud retrieval algorithms to maintain the accuracy and stability of the CERES climate data record

    The DIGMAP geo-temporal web gazetteer service

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    This paper presents the DIGMAP geo-temporal Web gazetteer service, a system providing access to names of places, historical periods, and associated geo-temporal information. Within the DIGMAP project, this gazetteer serves as the unified repository of geographic and temporal information, assisting in the recognition and disambiguation of geo-temporal expressions over text, as well as in resource searching and indexing. We describe the data integration methodology, the handling of temporal information and some of the applications that use the gazetteer. Initial evaluation results show that the proposed system can adequately support several tasks related to geo-temporal information extraction and retrieval

    ICESat/GLAS Data as a Measurement Tool for Peatland Topography and Peat Swamp Forest Biomass in Kalimantan, Indonesia

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    Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. We assessed the applicability of quality filtered ICESat/GLAS (a spaceborne LiDAR system) data to measure peatland topography as a proxy for peat volume and to estimate peat swamp forest Above Ground Biomass (AGB) in a thoroughly investigated study site in Central Kalimantan, Indonesia. Mean Shuttle Radar Topography Mission (SRTM) elevation was correlated to the corresponding ICESat/GLAS elevation. The best results were obtained from the waveform centroid (R2 = 0.92; n = 4,186). ICESat/GLAS terrain elevation was correlated to three 3D peatland elevation models derived from SRTM data (R2 = 0.90; overall difference = −1.0 m, ±3.2 m; n = 4,045). Based on the correlation of in situ peat swamp forest AGB and airborne LiDAR data (R2 = 0.75, n = 36) an ICESat/GLAS AGB prediction model was developed (R2 = 0.61, n = 35). These results demonstrate that ICESat/GLAS data can be used to measure peat topography and to collect large numbers of forest biomass samples in remote and highly inaccessible peatland forests

    Wellington : a novel method for the accurate identification of digital genomic footprints from DNase-seq data

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    The expression of eukaryotic genes is regulated by cis-regulatory elements such as promoters and enhancers, which bind sequence-specific DNA-binding proteins. One of the great challenges in the gene regulation field is to characterise these elements. This involves the identification of transcription factor (TF) binding sites within regulatory elements that are occupied in a defined regulatory context. Digestion with DNase and the subsequent analysis of regions protected from cleavage (DNase footprinting) has for many years been used to identify specific binding sites occupied by TFs at individual cis-elements with high resolution. This methodology has recently been adapted for high-throughput sequencing (DNase-seq). In this study, we describe an imbalance in the DNA strand-specific alignment information of DNase-seq data surrounding protein–DNA interactions that allows accurate prediction of occupied TF binding sites. Our study introduces a novel algorithm, Wellington, which considers the imbalance in this strand-specific information to efficiently identify DNA footprints. This algorithm significantly enhances specificity by reducing the proportion of false positives and requires significantly fewer predictions than previously reported methods to recapitulate an equal amount of ChIP-seq data. We also provide an open-source software package, pyDNase, which implements the Wellington algorithm to interface with DNase-seq data and expedite analyses

    Why carbon footprinting (and carbon labelling) only tells half the story

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    The UK is a world leader in the use of carbon footprints. The introduction of PAS2050 has legitimised carbon footprinting and manufacturers and retailers have responded by estimating carbon footprints for selected products. In industrial production, where the relationship between inputs and outputs is constant and the process is tightly controlled, carbon footprints tend to be reproducible. However, agricultural production is different, being influenced by biological, geological and climatic variation. Thus, although the use of a single value to represent the carbon burden of a food product is appealing, in practice it can be misleading. This paper discusses the variability associated with carbon footprints of agricultural products and considers the value of carbon labelling. We suggest that carbon footprinting is a useful approach that will assist in the transition to a low carbon society but that current approaches to carbon labelling may not help consumers understand the carbon burden of agricultural products

    Outline of the Finnish system of certified carbon footprints of food products

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    The basic structure of a system called Certified Footprints of Products (CFP system) is outlined in this discussion paper. The CFP system could produce strict and reliable data needed for generating product-oriented carbon footprints in Finland. Central parts of the CFP system are a national CFP programme, product category rules (PCRs), a chain or actor-wise monitoring plan, validation of the monitoring plan, and reporting and verification of data, and an ICT-system to support data sharing. The system is designed around activity-based monitoring data, and every actor would be responsible for data on its own activities. Linkages to existing environmental management systems are taken into account. The CFP system is still just a theoretical structure. It needs further development prior to full-scale introduction. For the food sector, a new architecture for data acquisition and quality assurance, development of existing mechanisms and consolidation of them in the CFP system are needed. Additional research is needed regarding emissions from agricultural production

    Creating Low-carbon Communities: Evaluating the Role of Individual Agency and Systemic Inequality in San Jose, CA

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    Following a scholarly need to test compelling community level sociodemographic representations of environmental behaviors and outcomes, a sequential mixed method approach was used to evaluate the connections of human agency and systemic inequalities with carbon footprints. Statistical analyses of the 2016 SDG San Jose Dashboard data of city blocks and 2009 - 2013 ACS survey data were supplemented with interviews with eight climate action-oriented community engagement professionals in the South Bay. Boundary limiting socioeconomic conditions for systemic inequalities and human agency, dimensions of Gidden’s Structuration model, were specified. Partially supporting structural inequality theories, socioeconomic resources, primarily, and to a lesser extent dominant race concentration, were associated with larger carbon footprints, particularly when wealth was concentrated. Both human (time driven alone) and demographic (senior and mid-aged blocks) agencies were also in part at play in shrinking or even enlarging carbon footprints, in wealthier communities. These findings not only contributed to the literature on climate action, but also highlighted the need for targeted interventions in communities of different socioeconomic standing
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