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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.
Urn models are simple examples for random growth processes that involve various competing types. In the study of these schemes, one is generally interested in the impact of the specific form of interaction on the allocation of elements to the types. Depending on their reciprocal action, effects of cancellation and self-reinforcement become apparent in the long run of the system. For some urn models, the influencing is of a smoothing nature and the asymptotic allocation to the types is close to being a result of independent and identically distributed growth events. On the contrary, for others, almost sure random tendencies or logarithmically periodic terms emerge in the second growth order. The present thesis is devoted to the derivation of central limit theorems in the latter case. For urns of this kind, we use a "non-classical" normalisation to derive asymptotic joint normality of the types. This normalisation takes random tendencies and phases into account and consequently involves random centering and, also, possibly random scaling.
Biological ageing is a degenerative and irreversible process, ultimately leading to death of the organism. The process is complex and under the control of genetic, environmental and stochastic traits. Although many theories have been established during the last decades, none of these are able to fully describe the complex mechanisms, which lead to ageing. Generally, biological processes and environmental factors lead to molecular damage and an accumulation of impaired cellular components. In contrast, counteracting surveillance systems are effective, including repair, remodelling and degradation of damaged or impaired components, respectively. Nevertheless, at some point these systems are no longer effective, either because the increasing amount of molecular damages can not longer be removed efficiently or because the repairing and removing mechanisms themselves become affected by impairing effects. The organism finally declines and dies. To investigate and to understand these counteracting mechanisms and the complex interplay of decline and maintenance, holistic and systems biological investigations are required. Hence, the processes which lead to ageing in the fungal model organism Podospora anserina, had been analysed using different advanced bioinformatics methods. In contrast to many other ageing models, P. anserina exhibits a short lifespan, a less biochemical complexity and it provides a good accessibility for genetic manipulations.
To achieve a general overview on the different biochemical processes, which are affected during ageing in P. anserina, an initial comprehensive investigation was applied, which aimed to reveal genes significantly regulated and expressed in an age-dependent manner. This investigation was based on an age-dependent transcriptome analysis. Sophisticated and comprehensive analyses revealed different age-related pathways and indicated that especially autophagy may play a crucial role during ageing. For example, it was found that the expression of autophagy-associated genes increases in the course of ageing.
Subsequently, to investigate and to characterise the autophagy pathway, its associated single components and their interactions, Path2PPI, a new bioinformatics approach, was developed. Path2PPI enables the prediction of protein-protein interaction networks of particular pathways by means of a homology comparison approach and was applied to construct the protein-protein interaction network of autophagy in P. anserina.
The predicted network was extended by experimental data, comprising the transcriptome data as well as newly generated protein-protein interaction data achieved from a yeast two-hybrid analysis. Using different mathematical and statistical methods the topological properties of the constructed network had been compared with those of randomly generated networks to approve its biological significance. In addition, based on this topological and functional analysis, the most important proteins were determined and functional modules were identified, which correspond to the different sub-pathways of autophagy. Due to the integrated transcriptome data the autophagy network could be linked to the ageing process. For example, different proteins had been identified, which genes are continuously up- or down-regulated during ageing and it was shown for the first time that autophagy-associated genes are significantly often co-expressed during ageing.
The presented biological network provides a systems biological view on autophagy and enables further studies, which aim to analyse the relationship of autophagy and ageing. Furthermore, it allows the investigation of potential methods for intervention into the ageing process and to extend the healthy lifespan of P. anserina as well as of other eukaryotic organisms, in particular humans.
For the class of balanced, irreducible Pólya urn schemes with two colours, say black and white, limit theorems for the number of black balls after n steps are known. Depending on the ratio of the eigenvalues of the replacement matrix, two regimes of limit laws occur: almost sure convergence to a non-degenerate random variable whose distribution depends on the initial composition of the urn and that is known to be not normally distributed and weak convergence to the normal distribution. In this thesis, upper bounds on the rates of convergence in both the non-normal limit case and the normal limit case are given.
Recently, Aumüller and Dietzfelbinger proposed a version of a dual-pivot Quicksort, called "Count", which is optimal among dual-pivot versions with respect to the average number of key comparisons required. In this master's thesis we provide further probabilistic analysis of "Count". We derive an exact formula for the average number of swaps needed by "Count" as well as an asymptotic formula for the variance of the number of swaps and a limit law. Also for the number of key comparisons the asymptotic variance and a limit law are identified. We also consider both complexity measures jointly and find their asymptotic correlation.
The future heavy-ion experiment CBM (FAIR/GSI, Darmstadt, Germany) will focus on the measurements of very rare probes, which require the experiment to operate under extreme interaction rates of up to 10 MHz. Due to high multiplicity of charged particles in heavy-ion collisions, this will lead to the data rates of up to 1 TB/s. In order to meet the modern achievable archival rate, this data ow has to be reduced online by more than two orders of magnitude.
The rare observables are featured with complicated trigger signatures and require full event topology reconstruction to be performed online. The huge data rates together with the absence of simple hardware triggers make traditional latency limited trigger architectures typical for conventional experiments inapplicable for the case of CBM. Instead, CBM will employ a novel data acquisition concept with autonomous, self-triggered front-end electronics.
While in conventional experiments with event-by-event processing the association of detector hits with corresponding physical event is known a priori, it is not true for the CBM experiment, where the reconstruction algorithms should be modified in order to process non-event-associated data. At the highest interaction rates the time difference between hits belonging to the same collision will be larger than the average time difference between two consecutive collisions. Thus, events will overlap in time. Due to a possible overlap of events one needs to analyze time-slices rather than isolated events.
The time-stamped data will be shipped and collected into a readout buffer in a form of a time-slice of a certain length. The time-slice data will be delivered to a large computer farm, where the archival decision will be obtained after performing online reconstruction. In this case association of hit information with physical events must be performed in software and requires full online event reconstruction not only in space, but also in time, so-called 4-dimensional (4D) track reconstruction.
Within the scope of this work the 4D track finder algorithm for online reconstruction has been developed. The 4D CA track finder is able to reproduce performance and speed of the traditional event-based algorithm. The 4D CA track finder is both vectorized (using SIMD instructions) and parallelized (between CPU cores). The algorithm shows strong scalability on many-core systems. The speed-up factor of 10.1 has been achieved on a CPU with 10 hyper-threaded physical cores.
The 4D CA track finder algorithm is ready for the time-slice-based reconstruction in the CBM experiment.
Modern mobile devices offer a great variety of data that can be recorded. This broad range of information offers the possibility to tailor applications more to the needs of a user. Several context information can be collected, like e.g. information about position or movement. Besides integrated sensors, a broad range of additional sensors are available which can be connected to a mobile device. These additional sensors offer for example the possibility to measure physiological signals of a user.The human body offers a broad range of different signals. These signals have been used in several examples to conclude on the state of a user. The different signals allow to get a deeper insight into emotional or mental state of a user. Electrodermal activity gives feedback about the current arousal level of a user. Heart rate and heart rate variability can give an estimation about valence and mental load of a user. Several models exist to conclude from information like valence and arousal on different emotional states. Russell defined a two dimensional model, using valence and arousal to define affective states. Yerkes and Dodson developed a curve that expresses the relationship between arousal and performance of a user. Different examples exist, that use physiological signals to determine the user state for tailoring and adapting of applications. At the time of this work most of these examples did not address the usage of physiological signals for user state estimation in mobile applications and in mobile scenarios. Mobile scenarios lead to several challenges that need to be addressed. Influencing factors on physiological signals, like e.g. movement have to be controlled. Furthermore a user might be interrupted and influenced by environmental aspects. The combination of physiological data and context information might improve the interpretation of user state in mobile scenarios. In this work, we present a model that addresses the challenges of usage in mobile scenarios to offer an estimation of user state to mobile applications. To address a broad range of mobile applications, affective and cognitive state are provided as output. As input heart rate and electrodermal activity are used, as well as context information about movement and performance. Electrodermal activity is measured by a simple sensor that can be worn as a wristband. Heart rate is measured by a chest strap as used in sports. The input channels are transformed to affective and cognitive state based on a fuzzy rule based approach. With help of fuzzy logic, uncertainty can be expressed and the data continuously being processed. At the start, input channels are fuzzified by defined functions. After a that, a first fuzzy rule set transforms the input signals into values for valence, arousal and mental load. In a second step, these values and context information are transformed with another fuzzy rule set to values for affective and cognitive state. Affective state is based on the model of Russell, where valence and arousal are used to determine different emotional states. The output of the model are eight different affective states (alarmed, excited, happy, relaxed, tired, bored, sad and frustrated), which can have a high, medium, low or very low value as output. Cognitive state is determined based on mental load and context information about performance and movement. The output value can be very high, high, medium or low. The model was implemented as background service for Android devices. Different applications have been used for evaluation of the model. The model has been integrated in a multiplayer space shooter game, called ”Zone of Impulse”, which mainly benefits from the affective state. Cognitive state is more addressed in applications like a simple vocable trainer, which adapts difficulty based on user state. A study to evaluate different aspects of the model has been conducted. The study was designed to investigate the suitability of the model for mobile scenarios. The game ”zone of impulse” and the vocable trainer have been investigated in different configurations. Versions with integrated model have been compared to version of the applications without model, as well as versions of the model without context information. In total 41 participants took part in the study. A part of the participants had to do the tasks of the study in a mobile scenario, walking around several streets. The remaining participants had to do the tasks in a controlled environment in a sitting position. Different aspects were collected with ratings and questionnaires. Overall, participants rated that they did not feel impaired by the sensors they had to wear. The results showed, that the combination of physiological data and context information had an advantage against versions without context information in part of the ratings. A comparison between versions with and without model showed, that the subjective mental load ratings were significantly better for the version with model. Subjective ratings for aspects like fun, overstrain and support were mixed. When comparing the application versions in indoor and outdoor scenarios, no significant difference could be found, which leads to the assumption that there is no loss of interpretation quality in outdoor scenarios. The results also showed that the model seems to be robust enough to compensate the loss of an input channel, as there was no significant difference between application versions with full integrated model and versions with one channel lost. With the model developed in this work, context information and physiological data were combined to improve user state estimation. Furthermore pitfalls of user state estimation in mobile scenarios are overcome with this combination. However, the model has only been evaluated with a limited amount of applications and situations that mobile scenarios offer.
Data-parallel programming is more important than ever since serial performance is stagnating. All mainstream computing architectures have been and are still enhancing their support for general purpose computing with explicitly data-parallel execution. For CPUs, data-parallel execution is implemented via SIMD instructions and registers. GPU hardware works very similar allowing very efficient parallel processing of wide data streams with a common instruction stream.
These advances in parallel hardware have not been accompanied by the necessary advances in established programming languages. Developers have thus not been enabled to explicitly state the data-parallelism inherent in their algorithms. Some approaches of GPU and CPU vendors have introduced new programming languages, language extensions, or dialects enabling explicit data-parallel programming. However, it is arguable whether the programming models introduced by these approaches deliver the best solution. In addition, some of these approaches have shortcomings from a hardware-specific focus of the language design. There are several programming problems for which the aforementioned language approaches are not expressive and flexible enough.
This thesis presents a solution tailored to the C++ programming language. The concepts and interfaces are presented specifically for C++ but as abstract as possible facilitating adoption by other programming languages as well. The approach builds upon the observation that C++ is very expressive in terms of types. Types communicate intention and semantics to developers as well as compilers. It allows developers to clearly state their intentions and allows compilers to optimize via explicitly defined semantics of the type system.
Since data-parallelism affects data structures and algorithms, it is not sufficient to enhance the language's expressivity in only one area. The definition of types whose operators express data-parallel execution automatically enhances the possibilities for building data structures. This thesis therefore defines low-level, but fully portable, arithmetic and mask types required to build a flexible and portable abstraction for data-parallel programming. On top of these, it presents higher-level abstractions such as fixed-width vectors and masks, abstractions for interfacing with containers of scalar types, and an approach for automated vectorization of structured types.
The Vc library is an implementation of these types. I developed the Vc library for researching data-parallel types and as a solution for explicitly data-parallel programming. This thesis discusses a few example applications using the Vc library showing the real-world relevance of the library. The Vc types enable parallelization of search algorithms and data structures in a way unique to this solution. It shows the importance of using the type system for expressing data-parallelism. Vc has also become an important building block in the high energy physics community. Their reliance on Vc shows that the library and its interfaces were developed to production quality.
In the first part of the thesis, we show that the payment flow of a linear tax on trading gains from a security with a semimartingale price process can be constructed for all càglàd and adapted trading strategies. It is characterized as the unique continuous extension of the tax payments for elementary strategies w.r.t. the convergence "uniformly in probability". In this framework, we prove that under quite mild assumptions dividend payoffs have almost surely a negative effect on investor’s after-tax wealth if the riskless interest rate is always positive. In addition, we give an example for tax-efficient strategies for which the tax payment flow can be computed explicitly.
In the second part of the thesis, we investigate the impact of capital gains taxes on optimal investment decisions in a quite simple model. Namely, we consider a risk neutral investor who owns one risky stock from which she assumes that it has a lower expected return than the riskless bank account and determine the optimal stopping time at which she sells the stock to invest the proceeds in the bank account up to the maturity date. In the case of linear taxes and a positive riskless interest rate, the problem is nontrivial because at the selling time the investor has to realize book profits which triggers tax payments. We derive a boundary that is continuous and increasing in time, and decreasing in the volatility of the stock such that the investor sells the stock at the first time its price is smaller or equal to this boundary.