Superconductivity in a Molecular Metal Cluster Compound

Compelling evidence for band-type conductivity and even bulk superconductivity below $T\_{\text{c}}\approx 8$ K has been found in $^{69,71}$Ga-NMR experiments in crystalline ordered, giant Ga$\_{84}$ cluster-compounds. This material appears to represent the first realization of a theoretical model proposed by Friedel in 1992 for superconductivity in ordered arrays of weakly coupled, identical metal nanoparticles.Comment: 5 pages, 4 figure

Provisioning of data locality for HEP analysis workflows

The heavily increasing amount of data produced by current experiments in high energy particle physics challenge both end users and providers of computing resources. The boosted data rates and the complexity of analyses require huge datasets being processed in short turnaround cycles. Usually, data storages and computing farms are deployed by different providers, which leads to data delocalization and a strong influence of the interconnection transfer rates. The CMS collaboration at KIT has developed a prototype enabling data locality for HEP analysis processing via two concepts. A coordinated and distributed caching approach that reduce the limiting factor of data transfers by joining local high performance devices with large background storages were tested. Thereby, a throughput optimization was reached by selecting and allocating critical data within user work-flows. A highly performant setup using these caching solutions enables fast processing of throughput dependent analysis workflows

Benchmark of a cubieboard cluster

We built a cluster of ARM-based Cubieboards2 which has a SATA interface to connect a harddrive. This cluster was set up as a storage system using Ceph and as a compute cluster for high energy physics analyses. To study the performance in these applications, we ran two benchmarks on this cluster. We also checked the energy efficiency of the cluster using the preseted benchmarks. Performance and energy efficency of our cluster were compared with a network-attached storage (NAS), and with a desktop PC

Boosting Performance of Data-intensive Analysis Workflows with Distributed Coordinated Caching

Data-intensive end-user analyses in high energy physics require high data throughput to reach short turnaround cycles. This leads to enormous challenges for storage and network infrastructure, especially when facing the tremendously increasing amount of data to be processed during High-Luminosity LHC runs. Including opportunistic resources with volatile storage systems into the traditional HEP computing facilities makes this situation more complex. Bringing data close to the computing units is a promising approach to solve throughput limitations and improve the overall performance. We focus on coordinated distributed caching by coordinating workows to the most suitable hosts in terms of cached files. This allows optimizing overall processing efficiency of data-intensive workows and efficiently use limited cache volume by reducing replication of data on distributed caches. We developed a NaviX coordination service at KIT that realizes coordinated distributed caching using XRootD cache proxy server infrastructure and HTCondor batch system. In this paper, we present the experience gained in operating coordinated distributed caches on cloud and HPC resources. Furthermore, we show benchmarks of a dedicated high throughput cluster, the Throughput-Optimized Analysis-System (TOpAS), which is based on the above-mentioned concept

Inhibition of protein crystallization by evolutionary negative design

In this perspective we address the question: why are proteins seemingly so hard to crystallize? We suggest that this is because of evolutionary negative design, i.e. proteins have evolved not to crystallize, because crystallization, as with any type of protein aggregation, compromises the viability of the cell. There is much evidence in the literature that supports this hypothesis, including the effect of mutations on the crystallizability of a protein, the correlations found in the properties of crystal contacts in bioinformatics databases, and the positive use of protein crystallization by bacteria and viruses.Comment: 5 page