Establishing Applicability of SSDs to LHC Tier-2 Hardware Configuration

Solid State Disk technologies are increasingly replacing high-speed hard disks as the storage technology in high-random-I/O environments. There are several potentially I/O bound services within the typical LHC Tier-2 - in the back-end, with the trend towards many-core architectures continuing, worker nodes running many single-threaded jobs and storage nodes delivering many simultaneous files can both exhibit I/O limited efficiency. We estimate the effectiveness of affordable SSDs in the context of worker nodes, on a large Tier-2 production setup using both low level tools and real LHC I/O intensive data analysis jobs comparing and contrasting with high performance spinning disk based solutions. We consider the applicability of each solution in the context of its price/performance metrics, with an eye on the pragmatic issues facing Tier-2 provision and upgradesComment: 6 pages, 1 figure, 4 tables. Conference proceedings for CHEP201

Understanding and Optimizing Flash-based Key-value Systems in Data Centers

Flash-based key-value systems are widely deployed in today’s data centers for providing high-speed data processing services. These systems deploy flash-friendly data structures, such as slab and Log Structured Merge(LSM) tree, on flash-based Solid State Drives(SSDs) and provide efficient solutions in caching and storage scenarios. With the rapid evolution of data centers, there appear plenty of challenges and opportunities for future optimizations. In this dissertation, we focus on understanding and optimizing flash-based key-value systems from the perspective of workloads, software, and hardware as data centers evolve. We first propose an on-line compression scheme, called SlimCache, considering the unique characteristics of key-value workloads, to virtually enlarge the cache space, increase the hit ratio, and improve the cache performance. Furthermore, to appropriately configure increasingly complex modern key-value data systems, which can have more than 50 parameters with additional hardware and system settings, we quantitatively study and compare five multi-objective optimization methods for auto-tuning the performance of an LSM-tree based key-value store in terms of throughput, the 99th percentile tail latency, convergence time, real-time system throughput, and the iteration process, etc. Last but not least, we conduct an in-depth, comprehensive measurement work on flash-optimized key-value stores with recently emerging 3D XPoint SSDs. We reveal several unexpected bottlenecks in the current key-value store design and present three exemplary case studies to showcase the efficacy of removing these bottlenecks with simple methods on 3D XPoint SSDs. Our experimental results show that our proposed solutions significantly outperform traditional methods. Our study also contributes to providing system implications for auto-tuning the key-value system on flash-based SSDs and optimizing it on revolutionary 3D XPoint based SSDs

Application of deep learning methods in materials microscopy for the quality assessment of lithium-ion batteries and sintered NdFeB magnets

Die Qualitätskontrolle konzentriert sich auf die Erkennung von Produktfehlern und die Überwachung von Aktivitäten, um zu überprüfen, ob die Produkte den gewünschten Qualitätsstandard erfüllen. Viele Ansätze für die Qualitätskontrolle verwenden spezialisierte Bildverarbeitungssoftware, die auf manuell entwickelten Merkmalen basiert, die von Fachleuten entwickelt wurden, um Objekte zu erkennen und Bilder zu analysieren. Diese Modelle sind jedoch mühsam, kostspielig in der Entwicklung und schwer zu pflegen, während die erstellte Lösung oft spröde ist und für leicht unterschiedliche Anwendungsfälle erhebliche Anpassungen erfordert. Aus diesen Gründen wird die Qualitätskontrolle in der Industrie immer noch häufig manuell durchgeführt, was zeitaufwändig und fehleranfällig ist. Daher schlagen wir einen allgemeineren datengesteuerten Ansatz vor, der auf den jüngsten Fortschritten in der Computer-Vision-Technologie basiert und Faltungsneuronale Netze verwendet, um repräsentative Merkmale direkt aus den Daten zu lernen. Während herkömmliche Methoden handgefertigte Merkmale verwenden, um einzelne Objekte zu erkennen, lernen Deep-Learning-Ansätze verallgemeinerbare Merkmale direkt aus den Trainingsproben, um verschiedene Objekte zu erkennen. In dieser Dissertation werden Modelle und Techniken für die automatisierte Erkennung von Defekten in lichtmikroskopischen Bildern von materialografisch präparierten Schnitten entwickelt. Wir entwickeln Modelle zur Defekterkennung, die sich grob in überwachte und unüberwachte Deep-Learning-Techniken einteilen lassen. Insbesondere werden verschiedene überwachte Deep-Learning-Modelle zur Erkennung von Defekten in der Mikrostruktur von Lithium-Ionen-Batterien entwickelt, von binären Klassifizierungsmodellen, die auf einem Sliding-Window-Ansatz mit begrenzten Trainingsdaten basieren, bis hin zu komplexen Defekterkennungs- und Lokalisierungsmodellen, die auf ein- und zweistufigen Detektoren basieren. Unser endgültiges Modell kann mehrere Klassen von Defekten in großen Mikroskopiebildern mit hoher Genauigkeit und nahezu in Echtzeit erkennen und lokalisieren. Das erfolgreiche Trainieren von überwachten Deep-Learning-Modellen erfordert jedoch in der Regel eine ausreichend große Menge an markierten Trainingsbeispielen, die oft nicht ohne weiteres verfügbar sind und deren Beschaffung sehr kostspielig sein kann. Daher schlagen wir zwei Ansätze vor, die auf unbeaufsichtigtem Deep Learning zur Erkennung von Anomalien in der Mikrostruktur von gesinterten NdFeB-Magneten basieren, ohne dass markierte Trainingsdaten benötigt werden. Die Modelle sind in der Lage, Defekte zu erkennen, indem sie aus den Trainingsdaten indikative Merkmale von nur "normalen" Mikrostrukturmustern lernen. Wir zeigen experimentelle Ergebnisse der vorgeschlagenen Fehlererkennungssysteme, indem wir eine Qualitätsbewertung an kommerziellen Proben von Lithium-Ionen-Batterien und gesinterten NdFeB-Magneten durchführen

Petascale turbulence simulation using a highly parallel fast multipole method on GPUs

This paper reports large-scale direct numerical simulations of homogeneous-isotropic fluid turbulence, achieving sustained performance of 1.08 petaflop/s on gpu hardware using single precision. The simulations use a vortex particle method to solve the Navier-Stokes equations, with a highly parallel fast multipole method (FMM) as numerical engine, and match the current record in mesh size for this application, a cube of 4096^3 computational points solved with a spectral method. The standard numerical approach used in this field is the pseudo-spectral method, relying on the FFT algorithm as numerical engine. The particle-based simulations presented in this paper quantitatively match the kinetic energy spectrum obtained with a pseudo-spectral method, using a trusted code. In terms of parallel performance, weak scaling results show the fmm-based vortex method achieving 74% parallel efficiency on 4096 processes (one gpu per mpi process, 3 gpus per node of the TSUBAME-2.0 system). The FFT-based spectral method is able to achieve just 14% parallel efficiency on the same number of mpi processes (using only cpu cores), due to the all-to-all communication pattern of the FFT algorithm. The calculation time for one time step was 108 seconds for the vortex method and 154 seconds for the spectral method, under these conditions. Computing with 69 billion particles, this work exceeds by an order of magnitude the largest vortex method calculations to date

Pioneering a new approach to sustainable concrete in Western Australia: Geopolymer concrete from fly-ash with recycled aggregates

This study investigates the suitability of a closed-loop approach to the management of useful waste derived materials such as recycled aggregates from construction waste and fly-ash to manufacture recycled aggregate geopolymer concrete. This investigation was carried out in two parts. Part 1 considered the particle size distribution, water absorption and particle densities for both recycled coarse and fine aggregates. These materials were used to create recycled aggregate concrete specimens used for part 2 – compressive strength and slump test for both conventional Portland cement concrete and fly-ash based geopolymer concrete. The results demonstrate that the higher water absorption values obtained for the recycled aggregates can be attributed to the decreases in particle size fraction and particle density. Overall, the use of recycled aggregates in geopolymer concrete had better workability than in conventional concrete. The use of manufactured results in a very dry mix for both the conventional and geopolymer concrete mixes. The use of recycled sand results in a mix that had better workability than a mix with natural sand for types of concrete. The technical, sustainability and economic implications of these findings are further discussed. Overall, the manufacturing of recycled aggregate geopolymer concrete is a two-fold solution, addressing both the problem of natural resource depletion and the large carbon footprint linked to cement manufacturing. It was determined that integrating FGRAC into the Western Australian concrete market requires specific focus on low value, fit-for-purpose pre-cast applications. For higher value applications of concrete, pre-treatment of the source materials is suggested to improve their attributes

Jet Momentum Resolution for the CMS Experiment and Distributed Data Caching Strategies

Accurately measured jets are mandatory for precision measurements of the Standard Model of particle physics as well as for searches for new physics. The increased instantaneous luminosity and center-of-mass energy at LHC Run 2 pose challenges for pileup mitigation and the measurement of jet characteristics. This thesis concentrates on using Z + jets events to calibrate the energy scale of jets recorded by the CMS detector in 2018. Furthermore, it proposes a new procedure for determining the jet momentum resolution using Z + jets events. This procedure is expected to allow cross-checking complementary measurement approaches and increasing the accuracy of the jet momentum resolution at the CMS experiment. Data-intensive end-user analyses in High Energy Physics such as the presented calibration of jets put enormous challenges on the computing infrastructure since requiring high data throughput. Besides the particle physics analysis, this thesis also focuses on accelerating data processing within a distributed computing infrastructure via a coordinated distributed caching approach. Coordinated placement of critical data within distributed caches and matching workflows to the most suitable host in terms of cached data allows for optimizing processing efficiency. Improving the processing of data-intensive workflows aims at shortening turnaround cycles and thus deriving physics results, e.g. the jet calibration results, faster

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments