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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.
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.
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.
A Large Ion Collider Experiment (ALICE) is a high-energy physics experiment, designed to study heavy ion collisions at the European Organization for Nuclear Research (CERN)Large Hadron Collider (LHC). ALICE is built to study the fundamental properties of matter as it existed shortly after the big bang. This requires reading out millions of sensors with high frequency, enabling high statistics for physics analysis, resulting in a considerable computing demand concerning network throughput and processing power. With the ALICE Run 3 upgrade [14], requirements for a High Throughput Computing
(HTC) online processing cluster increased significantly, due to more than an order of magnitude more data than in Run 2, resulting in a processing input rate of up to 900 GB/s. Online (real-time) event reconstruction allows for the compression of the data stream to 130 GB/s, which is stored on disk for physics analysis.
This thesis presents the implementation of the ALICE Event Processing Node (EPN) compute farm, to cope with the Run 3 online computing challenges. Building a Data Centre tailored to ALICE requirements for the Run 3 and Run 4 EPN farm. Providing the operational conditions for a dynamic compute environment of a High Performance Computing (HPC) cluster, with significant load changes in a short time span, when starting or stopping a data-taking run. EPN servers provide the required computing resources for online reconstruction and data compression. The farm includes network connectivity towards First Level Processors (FLPs), requiring reliable throughput of 900 GB/s between FLPs and EPNs and connectivity from the internal InfiniBand network to the CERN Exabyte Object Storage (EOS) Ethernet network, with more than 100 GB/s.
The results of operating the EPN computing infrastructure during the first year of Run 3 LHC collisions are described in the context of the ALICE experiment. The EPN farm was delivering the expected performance for ALICE data-taking. Data Centre environmental conditions remained stable during the last more than two years, in particular during starting and stopping runs, which include significant changes in IT load. Several unforeseen external circumstances lead to increasing demands for the Online Offline System (O2). Higher data rates than anticipated required network performance to exceed the initial design specifications, for the throughput between FLPs and EPNs. In particular, the high throughput from an internal EPN InfiniBand network towards the storage Ethernet network was one of the challenges to overcome.
Modern experiments in heavy ion collisions operate with huge data rates that can not be fully stored on the currently available storage devices. Therefore the data flow should be reduced by selecting those collisions that potentially carry the information of the physics interest. The future CBM experiment will have no simple criteria for selecting such collisions and requires the full online reconstruction of the collision topology including reconstruction of short-lived particles.
In this work the KF Particle Finder package for online reconstruction and selection of short-lived particles is proposed and developed. It reconstructs more than 70 decays, covering signals from all the physics cases of the CBM experiment: strange particles, strange resonances, hypernuclei, low mass vector mesons, charmonium, and open-charm particles.
The package is based on the Kalman filter method providing a full set of the particle parameters together with their errors including position, momentum, mass, energy, lifetime, etc. It shows a high quality of the reconstructed particles, high efficiencies, and high signal to background ratios.
The KF Particle Finder is extremely fast for achieving the reconstruction speed of 1.5 ms per minimum-bias AuAu collision at 25 AGeV beam energy on single CPU core. It is fully vectorized and parallelized and shows a strong linear scalability on the many-core architectures of up to 80 cores. It also scales within the First Level Event Selection package on the many-core clusters up to 3200 cores.
The developed KF Particle Finder package is a universal platform for short- lived particle reconstruction, physics analysis and online selection.