arXiv subjects

Florina M. Ciorba

Publications and source records attributed to Florina M. Ciorba.

43 records · Page 3Linked to original sources

arXiv2018

SiL: An Approach for Adjusting Applications to Heterogeneous Systems Under Perturbations

Scientific applications consist of large and computationally-intensive loops. Dynamic loop scheduling (DLS) techniques are used to load balance the execution of such applications. Load imbalance can be caused by variations in loop iteration execution times due to problem, algorithmic, or systemic characteristics (also, perturbations). The following question motivates this work: "Given an application, a high-performance computing (HPC) system, and both their characteristics and interplay, which DLS technique will achieve improved performance under unpredictable perturbations?" Existing work only considers perturbations caused by variations in the HPC system delivered computational speeds. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. Simulator in the loop (SiL) is introduced, herein, as a new control-theoretic inspired approach to dynamically select DLS techniques that improve the performance of applications on heterogeneous HPC systems under perturbations. The present work examines the performance of six applications on a heterogeneous system under all above system perturbations. The SiL proof of concept is evaluated using simulation. The performance results confirm the initial hypothesis that no single DLS technique can deliver best performance in all scenarios, while the SiL-based DLS selection delivered improved application performance in most experiments.

cs.DC↗

arXiv2018

Performance Reproduction and Prediction of Selected Dynamic Loop Scheduling Experiments

Scientific applications are complex, large, and often exhibit irregular and stochastic behavior. The use of efficient loop scheduling techniques in computationally-intensive applications is crucial for improving their performance on high-performance computing (HPC) platforms. A number of dynamic loop scheduling (DLS) techniques have been proposed between the late 1980s and early 2000s, and efficiently used in scientific applications. In most cases, the computing systems on which they have been tested and validated are no longer available. This work is concerned with the minimization of the sources of uncertainty in the implementation of DLS techniques to avoid unnecessary influences on the performance of scientific applications. Therefore, it is important to ensure that the DLS techniques employed in scientific applications today adhere to their original design goals and specifications. The goal of this work is to attain and increase the trust in the implementation of DLS techniques in present studies. To achieve this goal, the performance of a selection of scheduling experiments from the 1992 original work that introduced factoring is reproduced and predicted via both, simulative and native experimentation. The experiments show that the simulation reproduces the performance achieved on the past computing platform and accurately predicts the performance achieved on the present computing platform. The performance reproduction and prediction confirm that the present implementation of the DLS techniques considered both, in simulation and natively, adheres to their original description. The results confirm the hypothesis that reproducing experiments of identical scheduling scenarios on past and modern hardware leads to an entirely different behavior from expected.

cs.DC↗

arXiv2018

Assessing Data Usefulness for Failure Analysis in Anonymized System Logs

System logs are a valuable source of information for the analysis and understanding of systems behavior for the purpose of improving their performance. Such logs contain various types of information, including sensitive information. Information deemed sensitive can either directly be extracted from system log entries by correlation of several log entries, or can be inferred from the combination of the (non-sensitive) information contained within system logs with other logs and/or additional datasets. The analysis of system logs containing sensitive information compromises data privacy. Therefore, various anonymization techniques, such as generalization and suppression have been employed, over the years, by data and computing centers to protect the privacy of their users, their data, and the system as a whole. Privacy-preserving data resulting from anonymization via generalization and suppression may lead to significantly decreased data usefulness, thus, hindering the intended analysis for understanding the system behavior. Maintaining a balance between data usefulness and privacy preservation, therefore, remains an open and important challenge. Irreversible encoding of system logs using collision-resistant hashing algorithms, such as SHAKE-128, is a novel approach previously introduced by the authors to mitigate data privacy concerns. The present work describes a study of the applicability of the encoding approach from earlier work on the system logs of a production high performance computing system. Moreover, a metric is introduced to assess the data usefulness of the anonymized system logs to detect and identify the failures encountered in the system.

cs.DC↗

arXiv2018

Experimental Verification and Analysis of Dynamic Loop Scheduling in Scientific Applications

Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. Dynamic loop scheduling (DLS) techniques improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, simulative experiments are more appropriate for studying the performance of applications. This motivates the question of How realistic are the simulations of executions of scientific applications using DLS on HPC platforms? In the present work, a methodology is devised to answer this question. It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC plat- forms, both via native and simulative experiments. The evaluation and analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the approach used to extract the computational effort of the application (FLOP- or time-based), the choice of application model representation into simulation (data or task parallel), and the available HPC subsystem models in the simulator (multi-core CPUs, memory hierarchy, and network topology). The minimum and the maximum percent errors achieved between the native and the simulative experiments are 0.95% and 8.03%, respectively.

cs.DC↗

arXiv2017

Accretion outbursts in massive star formation

Using the HPC ressources of the state of Baden-Württemberg, we modelled for the first time the luminous burst from a young massive star by accretion of material from its close environment. We found that the surroundings of young massive stars are shaped as a clumpy disk whose fragments provoke outbursts once they fall onto the protostar and concluded that similar strong luminous events observed in high-mass star forming regions may be a signature of the presence of such disks.

astro-ph.SR↗

arXiv2017

Towards Adaptive Resilience in High Performance Computing

Failure rates in high performance computers rapidly increase due to the growth in system size and complexity. Hence, failures became the norm rather than the exception. Different approaches on high performance computing (HPC) systems have been introduced, to prevent failures (e. g., redundancy) or at least minimize their impacts (e. g., checkpoint and restart). In most cases, when these approaches are employed to increase the resilience of certain parts of a system, energy consumption rapidly increases, or performance significantly degrades. To address this challenge, we propose on-demand resilience as an approach to achieve adaptive resilience in HPC systems. In this work, the HPC system is considered in its entirety and resilience mechanisms such as checkpointing, isolation, and migration, are activated on-demand. Using the proposed approach, the unavoidable increase in total energy consumption and system performance degradation is decreased compared to the typical checkpoint/restart and redundant resilience mechanisms. Our work aims to mitigate a large number of failures occurring at various layers in the system, to prevent their propagation, and to minimize their impact, all of this in an energy-saving manner. In the case of failures that are estimated to occur but cannot be mitigated using the proposed on-demand resilience approach, the system administrators will be notified in view of performing further investigations into the causes of these failures and their impacts.

cs.DC↗

arXiv2017

Anonymization of System Logs for Privacy and Storage Benefits

System logs constitute valuable information for analysis and diagnosis of system behavior. The size of parallel computing systems and the number of their components steadily increase. The volume of generated logs by the system is in proportion to this increase. Hence, long-term collection and storage of system logs is challenging. The analysis of system logs requires advanced text processing techniques. For very large volumes of logs, the analysis is highly time-consuming and requires a high level of expertise. For many parallel computing centers, outsourcing the analysis of system logs to third parties is the only affordable option. The existence of sensitive data within system log entries obstructs, however, the transmission of system logs to third parties. Moreover, the analytical tools for processing system logs and the solutions provided by such tools are highly system specific. Achieving a more general solution is only possible through the access and analysis system of logs of multiple computing systems. The privacy concerns impede, however, the sharing of system logs across institutions as well as in the public domain. This work proposes a new method for the anonymization of the information within system logs that employs de-identification and encoding to provide sharable system logs, with the highest possible data quality and of reduced size. The results presented in this work indicate that apart from eliminating the sensitive data within system logs and converting them into shareable data, the proposed anonymization method provides 25% performance improvement in post-processing of the anonymized system logs, and more than 50% reduction in their required storage space.

cs.DC↗