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Driven by rapid technological advancements, the amount of data that is created, captured, communicated, and stored worldwide has grown exponentially over the past decades. Along with this development it has become critical for many disciplines of science and business to being able to gather and analyze large amounts of data. The sheer volume of the data often exceeds the capabilities of classical storage systems, with the result that current large-scale storage systems are highly distributed and are comprised of a high number of individual storage components. As with any other electronic device, the reliability of storage hardware is governed by certain probability distributions, which in turn are influenced by the physical processes utilized to store the information. The traditional way to deal with the inherent unreliability of combined storage systems is to replicate the data several times. Another popular approach to achieve failure tolerance is to calculate the block-wise parity in one or more dimensions. With better understanding of the different failure modes of storage components, it has become evident that sophisticated high-level error detection and correction techniques are indispensable for the ever-growing distributed systems. The utilization of powerful cyclic error-correcting codes, however, comes with a high computational penalty, since the required operations over finite fields do not map very well onto current commodity processors. This thesis introduces a versatile coding scheme with fully adjustable fault-tolerance that is tailored specifically to modern processor architectures. To reduce stress on the memory subsystem the conventional table-based algorithm for multiplication over finite fields has been replaced with a polynomial version. This arithmetically intense algorithm is better suited to the wide SIMD units of the currently available general purpose processors, but also displays significant benefits when used with modern many-core accelerator devices (for instance the popular general purpose graphics processing units). A CPU implementation using SSE and a GPU version using CUDA are presented. The performance of the multiplication depends on the distribution of the polynomial coefficients in the finite field elements. This property has been used to create suitable matrices that generate a linear systematic erasure-correcting code which shows a significantly increased multiplication performance for the relevant matrix elements. Several approaches to obtain the optimized generator matrices are elaborated and their implications are discussed. A Monte-Carlo-based construction method allows it to influence the specific shape of the generator matrices and thus to adapt them to special storage and archiving workloads. Extensive benchmarks on CPU and GPU demonstrate the superior performance and the future application scenarios of this novel erasure-resilient coding scheme.
The ALICE High-Level-Trigger (HLT) is a large scale computing farm designed and constructed for the purpose of the realtime reconstruction of particle interactions (events) inside the ALICE detector. The reconstruction of such events is based on the raw data produced in collisions inside the ALICE at the Large Hadron Collider. The online reconstruction in the HLT allows the triggering on certain event topologies and a significant data reduction by applying compression algorithms. Moreover, it enables a real-time verification of the quality of the data.
To receive the raw data from the various sub-detectors of ALICE, the HLT is equipped with 226 custom built FPGA-based PCI-X cards, the H-RORCs. The H-RORC interfaces the detector readout electronics to the nodes of the HLT farm. In addition to the transfer of raw data, 108 H-RORCs host 216 Fast-Cluster-Finder (FCF) processors for the Time-Projection-Chamber (TPC). The TPC is the main tracking detector of ALICE and contributes with up to 16 GB/s to over 90% of the overall data volume. The FCF processor implements the first of two steps in the data reconstruction of the TPC. It calculates the space points and their properties from charge clouds (clusters) created by charged particles traversing the TPCs gas volume. Those space points are not only the base for the tracking algorithm, but also allow for a Huffman-based data compression, which reduces the data volume by a factor of 4 to 6.
The FCF processor is designed to cope with any incoming data rate up to the maximum bandwidth of the incoming optical link (160 MB/s) without creating back-pressure to the detectors readout electronics. A performance comparison with the software implementation of the algorithm shows a speedup factor of about 20 compared with one AMD Opteron 6172 Core @ 2.1 GHz, the CPU type used in the HLT during the LHC Run1 campaign. Comparison with an Intel E5-2690 Core @ 3.0 GHz, the CPU type used by the HLT for the LHC Run2 campaign, results in a speedup factor of 8.5. In total numbers, the 216 FCF processors provide the computing performance of 4255 AMD Opteron cores or 2203 Intel cores of the previously mentioned type. The performance of the reconstruction with respect to the physics analysis is equivalent or better than the official ALICE Offline clusterizer. Therefore, ALICE data taking was switched in 2011 to FCF cluster recording and compression only, discarding the raw data from the TPC. Due to the capability to compress the clusters, the recorded data volume could be increased by a factor of 4 to 6.
For the LHC Run3 campaign, starting in 2020, the FCF builds the foundation of the ALICE data taking and processing strategy. The raw data volume (before processing) of the upgraded TPC will exceed 3 TB/s. As a consequence, online processing of the raw data and compression of the results before it enters the online computing farms is an essential and crucial part of the computing model.
Within the scope of this thesis, the H-RORC card and the FCF processor were developed and built from scratch. It covers the conceptual design, the optimisation and implementation, as well as the verification. It is completed by performance benchmarks and experiences from real data taking.
High-energy physics experiments aim to deepen our understanding of the fundamental structure of matter and the governing forces. One of the most challenging aspects of the design of new experiments is data management and event selection. The search for increasingly rare and intricate physics events asks for high-statistics measurements and sophisticated event analysis. With progressively complex event signatures, traditional hardware-based trigger systems reach the limits of realizable latency and complexity. The Compressed Baryonic Matter experiment (CBM) employs a novel approach for data readout and event selection to address these challenges. Self-triggered, free-streaming detectors push all data to a central compute cluster, called First-level Event Selector (FLES), for software-based event analysis and selection. While this concept solves many issues present in classical architectures, it also sets new challenges for the design of the detector readout systems and online event selection.
This thesis presents an efficient solution to the data management challenges presented by self-triggered, free-streaming particle detectors. The FLES must receive asynchronously streamed data from a heterogeneous detector setup at rates of up to 1 TB/s. The real-time processing environment implies that all components have to deliver high performance and reliability to record as much valuable data as possible. The thesis introduces a time-based data model to partition the input streams into containers of fixed length in experiment time for efficient data management. These containers provide all necessary metadata to enable generic, detector-subsystem-agnostic data distribution across the entire cluster. An analysis shows that the introduced data overhead is well below 1 % for a wide range of system parameters.
Furthermore, a concept and the implementation of a detector data input interface for the CBM FLES, optimized for resource-efficient data transport, are presented. The central element of the architecture is an FPGA-based PCIe extension card for the FLES entry nodes. The hardware designs developed in the thesis enable interfacing with a diverse set of detector systems. A custom, high-throughput DMA design structures data in a way that enables low-overhead access and efficient software processing. The ability to share the host DMA buffers with other devices, such as an InfiniBand HCA, allows for true zero-copy data distribution between the cluster nodes. The discussed FLES input interface is fully implemented and has already proven its reliability in production operation in various physics experiments.
Das Projekt anan ist ein Werkzeug zur Fehlersuche in verteilten Hochleistungsrechnern. Die Neuheit des Beitrags besteht darin, dass die bekannten Methoden, die bereits erfolgreich zum Debuggen von Soft- und Hardware eingesetzt werden, auf Hochleistungs-Rechnen übertragen worden sind. Im Rahmen der vorliegenden Arbeit wurde ein Werkzeug namens anan implementiert, das bei der Fehlersuche hilft. Außerdem kann es als dynamischeres Monitoring eingesetzt werden. Beide Einsatzzwecke sind
getestet worden.
Das Werkzeug besteht aus zwei Teilen:
1. aus einem Teil namens anan, der interaktiv vom Nutzer bedient wird
2. und aus einem Teil namens anand, der automatisiert die verlangten Messwerte erhebt und nötigenfalls Befehle ausführt.
Der Teil anan führt Sensoren aus — kleine mustergesteuerte Algorithmen —, deren Ergebnisse per anan zusammengeführt werden. In erster Näherung lässt anan sich als Monitoring beschreiben, welches (1) schnell umkonfiguriert werden (2) komplexere Werte messen kann, die über Korrelationen einfacher Zeitreihen hinausgehen.
Blockchains in public administration : a RADIUS on blockchain framework for public administration
(2023)
The emergence of blockchain technology has generated a great deal of attention, as reflected in numerous scientific and journalistic articles. However, the implementation of blockchain for public administrations in Germany has encountered a setback owing to unsuccessful initiatives. Initial enthusiasm was followed by disillusionment. Nevertheless, technology continues to evolve. This paper examines whether the use of a blockchain can still optimize the processes of public administrations. Not only the failed projects are analysed, but also more current applications of the technology and their potential relevance for the administration, especially in the state of Hesse.
To answer if blockchains are promising to administrations, a Design Science Research (DSR) research approach is chosen. The DSR method is a research-based approach that aims to create new and innovative solutions to real-world problems through the development and evaluation of artefacts such as models, methods, or prototypes. For this work, the implementation of a framework to realize an Authentication, Authorization, and Accounting (AAA) system on the blockchain was identified as profitable. The framework aims to implement the aforementioned AAA tasks using a blockchain. The Remote Authentication Dial-In User Service (RADIUS) protocol has been identified as a potential protocol of the AAA system. The goal is to create a way to implement the system either entirely on a blockchain or as a hybrid system. Various blockchain technologies will be considered. Suitable for development, the framework AAA-me is named.
The development of AAA-me has shown that the desired framework for implementing RADIUS on the blockchain is possible in various degrees of implementation. Previous work mostly relied on full development. Additionally, it has been shown that AAA-me can be used to perform hybrid integration at different implementation levels. This makes AAA-me stand out from the few hybrid previous approaches. Furthermore, AAA-me was investigated in different laboratory environments. This was to determine the expected resilience against Single Point of Failure (SPOF). The results of the lab investigation indicated that a RADIUS system on top of a blockchain can provide benefits in terms of security and performance. In the lab environment, times were measured within which a series of authorization requests were processed. In addition, it was illustrated how a RADIUS system implemented using blockchain can protect itself against Man-in-the-Middle (MITM) attacks.
Finally, in collaboration with the Hessian Central Office for Data Processing (German: Hessische Zentrale für Datenverarbeitung) (HZD), another test lab demonstrated how a RADIUS system on the blockchain can integrate with the existing IT systems of the German state of Hesse. Based on these findings, this work reevaluated the applicability of blockchain technology for public administration processes.
The work has thus shown that the use of a blockchain can still be purposeful. However, it has also been shown that an implementation can bring many problems with it. The small number of blockchain developers and engineers also poses the risk of finding people to develop and maintain a system. In addition, one faces the problem of determining an architecture now that will be applied to many projects in the future. However, each project can, in turn, have an impact on the choice of architecture. Once one has solved this problem and a blockchain infrastructure is available, it can be established quickly and be more SPOF resistant, for example, for Public Key Infrastructure (PKI) systems.
AAA-me was only applied in lab and test environments. As a result, no real data ran over its own infrastructure. This allowed the necessary flexibility for development. However, system-related properties could appear in real situations that are not detectable here in this way. Furthermore, the initial stage of AAA-me’s development is still in its infancy. Many manual adjustments need to be made in order for this to integrate with an existing RADIUS system. Also, no system security effort in and of itself has been carried out in the lab environments. Thus, vulnerabilities can quickly open up on web servers due to misconfigurations and missing updates. For the above reasons, productive use should be discouraged unless major developments are carried out.
The relevant field of interest in High Energy Physics experiments is shifting to searching and studying extremely rare particles and phenomena. The search for rare probes requires an increase in the number of available statistics by increasing the particle interaction rate. The structure of the events also becomes more complicated, the multiplicity of particles in each event increases, and a pileup appears. Due to technical limitations, such data flow becomes impossible to store fully on available storage devices. The solution to the problem is the correct triggering of events and real-time data processing.
In this work, the issue of accelerating and improving the algorithms for reconstruction of the charged particles' trajectories based on the Cellular Automaton in the STAR experiment is considered to implement them for track reconstruction in real-time within the High-Level Trigger. This is an important step in the preparation of the CBM experiment as part of the FAIR Phase-0 program. The study of online data processing methods in real conditions at similar interaction energies allows us to study this process and determine the possible weaknesses of the approach.
Two versions of the Cellular Automaton based track reconstruction are discussed, which are used, depending on the detecting systems' features. HFT~CA Track Finder, similar to the tracking algorithm of the CBM experiment, has been accelerated by several hundred times, using both algorithm optimization and data-level parallelism. TPC~CA Track Finder has been upgraded to improve the reconstruction quality while maintaining high calculation speed. The algorithm was tuned to work with the new iTPC geometry and provided an additional module for very low momentum track reconstruction.
The improved track reconstruction algorithm for the TPC detector in the STAR experiment was included in the HLT reconstruction chain and successfully tested in the express production for the online real data analysis. This made it possible to obtain important physical results during the experiment runtime without the full offline data processing. The tracker is also being prepared for integration into a standard offline data processing chain, after which it will become the basic track search algorithm in the STAR experiment.
A new era in experimental nuclear physics has begun with the start-up of the Large Hadron Collider at CERN and its dedicated heavy-ion detector system ALICE. Measuring the highest energy density ever produced in nucleus-nucleus collisions, the detector has been designed to study the properties of the created hot and dense medium, assumed to be a Quark-Gluon Plasma.
Comprised of 18 high granularity sub-detectors, ALICE delivers data from a few million electronic channels of proton-proton and heavy-ion collisions.
The produced data volume can reach up to 26 GByte/s for central Pb–Pb
collisions at design luminosity of L = 1027 cm−2 s−1 , challenging not only the data storage, but also the physics analysis. A High-Level Trigger (HLT) has been built and commissioned to reduce that amount of data to a storable value prior to archiving with the means of data filtering and compression without the loss of physics information. Implemented as a large high performance compute cluster, the HLT is able to perform a full reconstruction of all events at the time of data-taking, which allows to trigger, based on the information of a complete event. Rare physics probes, with high transverse momentum, can be identified and selected to enhance the overall physics reach of the experiment.
The commissioning of the HLT is at the center of this thesis. Being deeply embedded in the ALICE data path and, therefore, interfacing all other ALICE subsystems, this commissioning imposed not only a major challenge, but also a massive coordination effort, which was completed with the first proton-proton collisions reconstructed by the HLT. Furthermore, this thesis is completed with the study and implementation of on-line high transverse momentum triggers.
Conceptual design of an ALICE Tier-2 centre integrated into a multi-purpose computing facility
(2012)
This thesis discusses the issues and challenges associated with the design and operation of a data analysis facility for a high-energy physics experiment at a multi-purpose computing centre. At the spotlight is a Tier-2 centre of the distributed computing model of the ALICE experiment at the Large Hadron Collider at CERN in Geneva, Switzerland. The design steps, examined in the thesis, include analysis and optimization of the I/O access patterns of the user workload, integration of the storage resources, and development of the techniques for effective system administration and operation of the facility in a shared computing environment. A number of I/O access performance issues on multiple levels of the I/O subsystem, introduced by utilization of hard disks for data storage, have been addressed by the means of exhaustive benchmarking and thorough analysis of the I/O of the user applications in the ALICE software framework. Defining the set of requirements to the storage system, describing the potential performance bottlenecks and single points of failure and examining possible ways to avoid them allows one to develop guidelines for selecting the way how to integrate the storage resources. The solution, how to preserve a specific software stack for the experiment in a shared environment, is presented along with its effects on the user workload performance. The proposal for a flexible model to deploy and operate the ALICE Tier-2 infrastructure and applications in a virtual environment through adoption of the cloud computing technology and the 'Infrastructure as Code' concept completes the thesis. Scientific software applications can be efficiently computed in a virtual environment, and there is an urgent need to adapt the infrastructure for effective usage of cloud resources.
Quarks and gluons are the building blocks of all hadronic matter, like protons and neutrons. Their interaction is described by Quantum Chromodynamics (QCD), a theory under test by large scale experiments like the Large Hadron Collider (LHC) at CERN and in the future at the Facility for Antiproton and Ion Research (FAIR) at GSI. However, perturbative methods can only be applied to QCD for high energies. Studies from first principles are possible via a discretization onto an Euclidean space-time grid. This discretization of QCD is called Lattice QCD (LQCD) and is the only ab-initio option outside of the high-energy regime. LQCD is extremely compute and memory intensive. In particular, it is by definition always bandwidth limited. Thus—despite the complexity of LQCD applications—it led to the development of several specialized compute platforms and influenced the development of others. However, in recent years General-Purpose computation on Graphics Processing Units (GPGPU) came up as a new means for parallel computing. Contrary to machines traditionally used for LQCD, graphics processing units (GPUs) are a massmarket product. This promises advantages in both the pace at which higher-performing hardware becomes available and its price. CL2QCD is an OpenCL based implementation of LQCD using Wilson fermions that was developed within this thesis. It operates on GPUs by all major vendors as well as on central processing units (CPUs). On the AMD Radeon HD 7970 it provides the fastest double-precision D= kernel for a single GPU, achieving 120GFLOPS. D=—the most compute intensive kernel in LQCD simulations—is commonly used to compare LQCD platforms. This performance is enabled by an in-depth analysis of optimization techniques for bandwidth-limited codes on GPUs. Further, analysis of the communication between GPU and CPU, as well as between multiple GPUs, enables high-performance Krylov space solvers and linear scaling to multiple GPUs within a single system. LQCD calculations require a sampling of the phase space. The hybrid Monte Carlo (HMC) algorithm performs this. For this task, a single AMD Radeon HD 7970 GPU provides four times the performance of two AMD Opteron 6220 running an optimized reference code. The same advantage is achieved in terms of energy-efficiency. In terms of normalized total cost of acquisition (TCA), GPU-based clusters match conventional large-scale LQCD systems. Contrary to those, however, they can be scaled up from a single node. Examples of large GPU-based systems are LOEWE-CSC and SANAM. On both, CL2QCD has already been used in production for LQCD studies.
We live in age of data ubiquity. Even the most conservative estimates predict exponential growth in produced, transmitted and stored data. Big data is used to power business analytics as well as to foster scientific discoveries. In many cases, explosion of produced data exceeds capabilities of digital storage systems. Scientific high-performance computing environments cope with this problem by utilizing large, distributed, storage systems. These complex systems can only provide a high degree of reliability and durability by means of data redundancy. The most straight-forward way of doing that is by replicating the data over different physical devices. However, more elaborate approaches, such as erasure coding, can provide similar data protection while utilizing less storage. Recently, software-defined reliability methods began to replace traditional, hardware- based, solutions. Complicated failure modes of storage system components also warrant checksums to guaranty long-term data integrity. To cope with ever increasing data volumes, flexible and efficient software implementation of error correction codes is of great importance. This thesis introduces a method for realizing a flexible Reed-Solomon erasure code using the “Just-In-Time” compilation technique. By exploiting intrinsic arithmetic redundancy in the algorithm, and by relying on modern optimizing compilers, we obtain a throughput-efficient erasure code implementation. Additionally, exploitation of data parallelism is achieved effortlessly by instructing the compiler to produce SIMD code for desired execution platform. We show results of codes implemented using SSE and AVX2 SIMD instruction sets for x86, and NEON instruction set for ARM platforms. Next, we introduce a framework for efficient vectorized RAID-Z redundancy operations of ZFS file system. Traditional, table-based Galois field multiplication algorithms are replaced with custom SSE and AVX2 parallel methods, providing significantly faster and more efficient parity operations. The implementation of this framework was made publicly available as a part of ZFS on Linux project, since version 0.7. Finally, we propose a new erasure scheme for use with existing, high performance, parallel filesystems. Described reliability middleware (ECCFS) allows definition of flexible, file-based, reliability policies, adapting to customized user needs. By utilizing the block erasure code, the ECCFS achieves optimal storage, computation, and network resource utilization, while providing a high level of reliability. The distributed nature of the middleware allows greater scalability and more efficient utilization of storage and network resources, in order to improve availability of the system.