The 2020 campaign in Europe

In 2020, the beet campaign in many European countries began later and was shorter than usual. Sugar production was of 14.50 mn t in 2020/21 was 1.76 mn t or 10.9% lower than last year. The paper describes the passing of the campaign in Germany, Denmark, Sweden, Finland, France, the Netherlands, Austria and Poland in respect to climate, beet area, yields, production, campaign length and investments.

Characterizing network paths in and out of the clouds

Commercial Cloud computing is becoming mainstream, with funding agencies moving beyond prototyping and starting to fund production campaigns, too. An important aspect of any scientific computing production campaign is data movement, both incoming and outgoing. And while the performance and cost of VMs is relatively well understood, the network performance and cost is not. This paper provides a characterization of networking in various regions of Amazon Web Services, Microsoft Azure and Google Cloud Platform, both between Cloud resources and major DTNs in the Pacific Research Platform, including OSG data federation caches in the network backbone, and inside the clouds themselves. The paper contains both a qualitative analysis of the results as well as latency and peak throughput measurements. It also includes an analysis of the costs involved with Cloud-based networking.

Physics Data Production on HPC: Experience to be efficiently running at scale

The Solenoidal Tracker at RHIC (STAR) is a multi-national supported experiment located at the Brookhaven National Lab and is currently the only remaining running experiment at RHIC. The raw physics data captured from the detector is on the order of tens of PBytes per data acquisition campaign, making STAR fit well within the definition of a big data science experiment. The production of the data has typically run using a High Throughput Computing (HTC) approach either done on a local farm or via Grid computing resources. Especially, all embedding simulations (complex workflow mixing real and simulated events) have been run on standard Linux resources at NERSC’s Parallel Distributed Systems Facility (PDSF). However, as per April 2019 PDSF has been retired and High Performance Computing (HPC) resources such as the Cray XC-40 Supercomputer known as “Cori” have become available for STAR’s data production as well as embedding. STAR has been the very first experiment to show feasibility of running a sustainable data production campaign on this computing resource. In this contribution, we hope to share with the community the best practices for using such resource efficiently. The use of Docker containers with Shifter is the standard approach to run on HPC at NERSC – this approach encapsulates the environment in which a standard STAR workflow runs. From the deployment of a tailored Scientific Linux environment (with the set of libraries and special configurations required for STAR to run) to the deployment of third-party software and the STAR specific software stack, we’ve learned it has become impractical to rely on a set of containers comprising each specific software release. To this extent, a solution based on the CernVM File System (CVMFS) for the deployment of software and services has been deployed but it doesn’t stop there. One needs to make careful scalability considerations when using a resource like Cori, such as avoiding metadata lookups, scalability of distributed filesystems, and real limitations of containerized environments on HPC. Additionally, CVMFS clients are not compatible on Cori nodes and one needs to rely on an indirect NFS mount scheme using custom services known as DVS servers designed to forward data to worker nodes. In our contribution, we will discuss our strategies from the past and our current solution based on CVMFS. The second focus of our presentation will be to discuss strategies to find the most efficient use of database Shifter containers serving our data production (a near “database as a service” approach) and the best methods to test and scale your workflow efficiently.

Mass Production Test of Solar Cells and Modules Made of 100% UMG Silicon. 20.76% Record Efficiency

For more than 15 years FerroAtlantica (now Ferroglobe) has been developing a method of silicon purification to obtain Upgraded Metallurgical Grade Silicon (UMG-Si) for PV solar application without blending. After many improvements and optimizations, the final process has clearly demonstrated its validity in terms of quality and costs. In this paper the authors present new results stemming from a first mass-production campaign and a detailed description of the purification process that results in the tested UMG-Si. The subsequent steps in the value chain for the wafer, cell and module manufacturing are also described. Two independent companies, among the Tier-1 solar cells producers, were selected for the industrial test, each using a different solar cell technology: Al-BSF and black silicon + PERC. Cells and modules were manufactured in conventional production lines and their performances compared to those obtained with standard polysilicon wafers produced in the same lines and periods. Thus, for Al-BSF technology, the average efficiency of solar cells obtained with UMG-Si was (18.4 ± 0.4)% compared to 18.49% obtained with polysilicon-made wafers. In the case of black silicon + PERC, the average efficiency obtained with UMG-Si was (20.1 ± 0.6)%, compared to 20.41% for polysilicon multicrystalline wafers.

PRODUÇÃO LEGISLATIVA, GASTOS DE CAMPANHA E DESEMPENHO ELEITORAL: EVIDÊNCIAS A PARTIR DE UM ESTUDO DE CASO / LEGISLATIVE PRODUCTION, CAMPAIGN EXPENDITURE AND ELECTORAL PERFORMANCE: EVIDENCE FROM A CASE STUDY

Quais fatores explicam o desempenho eleitoral? Este artigo analisa o impacto da produção legislativa e dos gastos de campanha sobre a quantidade de votos recebido pelos vereadores do Recife nas eleições municipais de 2016. Testa-se duas hipóteses: 1) quanto maior a produção legislativa maior o número de votos e 2) quanto maior o gasto de campanha, maior a quantidade de votos. O desenho de pesquisa examina um banco de dados original elaborado a partir de informações secundárias coletadas nos sites da Câmara do Recife e do Tribunal Regional Eleitoral de Pernambuco. Os resultados indicam que, ao se considerar todos os casos (n=37), o modelo de regressão apresenta um ajuste de 0,456. No entanto, após a exclusão de um outlier, temos um r²=0,081, o que significa que as variáveis não explicam satisfatoriamente o desempenho eleitoral. Em termos substantivos, os resultados indicam que a performance eleitoral dos vereadores não é afetada pela produção legislativa formal nem pelo gasto de campanha.