Event-Based Transverse Momentum Resummation

We have developed a framework for automated transverse momentum resummation for arbitrary electroweak final states based on reweighting tree-level events. It is fully differential in the kinematics of the electroweak final states, which facilitates a straightforward analysis of arbitrary observables in the small transverse momentum region. We have implemented the resummation at next-to-next-to-leading logarithmic accuracy and match to next-to-leading fixed-order results using the event generator MadGraph5_aMC@NLO. Results for $Z$ and $W$ boson production with leptonic decay as well as $WZ$ production are presented. We compare to experimental measurements for the transverse momentum and the angular observable $\phi^*$.Comment: 28 pages, 13 figures. v2: journal versio

Validation of hardware events for successful performance pattern identification in High Performance Computing

Hardware performance monitoring (HPM) is a crucial ingredient of performance analysis tools. While there are interfaces like LIKWID, PAPI or the kernel interface perf\_event which provide HPM access with some additional features, many higher level tools combine event counts with results retrieved from other sources like function call traces to derive (semi-)automatic performance advice. However, although HPM is available for x86 systems since the early 90s, only a small subset of the HPM features is used in practice. Performance patterns provide a more comprehensive approach, enabling the identification of various performance-limiting effects. Patterns address issues like bandwidth saturation, load imbalance, non-local data access in ccNUMA systems, or false sharing of cache lines. This work defines HPM event sets that are best suited to identify a selection of performance patterns on the Intel Haswell processor. We validate the chosen event sets for accuracy in order to arrive at a reliable pattern detection mechanism and point out shortcomings that cannot be easily circumvented due to bugs or limitations in the hardware

LIKWID Monitoring Stack: A flexible framework enabling job specific performance monitoring for the masses

System monitoring is an established tool to measure the utilization and health of HPC systems. Usually system monitoring infrastructures make no connection to job information and do not utilize hardware performance monitoring (HPM) data. To increase the efficient use of HPC systems automatic and continuous performance monitoring of jobs is an essential component. It can help to identify pathological cases, provides instant performance feedback to the users, offers initial data to judge on the optimization potential of applications and helps to build a statistical foundation about application specific system usage. The LIKWID monitoring stack is a modular framework build on top of the LIKWID tools library. It aims on enabling job specific performance monitoring using HPM data, system metrics and application-level data for small to medium sized commodity clusters. Moreover, it is designed to integrate in existing monitoring infrastructures to speed up the change from pure system monitoring to job-aware monitoring.Comment: 4 pages, 4 figures. Accepted for HPCMASPA 2017, the Workshop on Monitoring and Analysis for High Performance Computing Systems Plus Applications, held in conjunction with IEEE Cluster 2017, Honolulu, HI, September 5, 201

COMET: Gateway to Commercial Space

The COMmercial Experiment Transporter (COMET), scheduled to begin its first mission in September of 1992, is expected to be the baseline that is used to measure the capabilities of the next generation of commercial To Space and Back systems. This paper provides an overview of the COMET system with emphasis on its operational capabilities. The design of the Service Module (spacecraft bus) will be highlighted and its modular design and flexible architecture will be discussed