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IEEE International Conference on Cloud Computing, CLOUD

作者： Vladimir Podolskiy Anshul Jindal Michael Gerndt Chair for Computer Architecture and Parallel Systems Technical University of Munich Garching (near Munich) Germany

The main feature of a cloud application is its scalability. Major IaaS cloud services providers (CSP) employ autoscaling on the level of virtual machines (VM). Other virtualization solutions (e.g. containers, pods) can also scale. An application scales in response to change in observed metrics, e.g. in CPU utilization. Occasionally, cloud applications exhibit the inability to meet the Quality of Service (QoS) requirements during the scaling caused by the reactivity of autoscaling solutions. This paper provides the results of the autoscaling performance evaluation for two-layered virtualization (VMs and Kubernetes pods) conducted in the public clouds of AWS, Microsoft and Google using the approach and the Autoscaling Performance Measurement Tool developed by the authors.

关键词： Cloud computing Quality of service Measurement Virtualization Google Tools Scalability

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Towards the Munich Quantum Software Stack: Enabling Efficient Access and Tool Support for Quantum computers 4

Towards the Munich Quantum Software Stack: Enabling Efficien...

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4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023

作者： Schulz, Martin Schulz, Laura Ruefenacht, Martin Wille, Robert Technical University of Munich Computer Architecture and Parallel Systems Munich Germany Leibniz Supercomputing Centre Qct Department Garching Germany Technical University of Munich Design Automation Munich Germany

ISBN: (纸本)9798350343236

As quantum computing systems mature and move from laboratories to production computing environments, corresponding software stacks are becoming key for their successful utilization. In particular, the expected use of quantum systems as HPC accelerators requires a deep integration with the existing and widely deployed HPC software stacks. Additionally, new requirements such as dynamic compilation and new challenges for tools and programming models must be considered. We tackle these challenges by developing the Munich Quantum Software Stack - a comprehensive initiative by the Munich Quantum Valley to offer a flexible, efficient, and user-oriented software environment. In this poster, we describe the core components and workflows, and how they will enable this transformation from quantum experiments to quantum accelerators. © 2023 IEEE.

关键词： Quantum optics

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Enhancing nanosatellite dependability through autonomous chip-level debug capabilities 29th

Enhancing nanosatellite dependability through autonomous chi...

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29th International Conference on architecture of Computing systems, ARCS 2016

作者： Fuchs, Christian M. Dafinger, Nikolaus Langer, Martin Trinitis, Carsten Chair of Space Systems Engineering Faculty of Aerospace Engineering Computer Engineering Laboratory Delft University of Technology Delft Netherlands Institute for Astronautics Chair for Computer Architecture and Organization Technical University Munich Garching Germany

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Leveraging Hybrid Classical-Quantum Methods for Efficient Load Rebalancing in HPC

Leveraging Hybrid Classical-Quantum Methods for Efficient Lo...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Zawalska, Justyna Chung, Minh Rycerz, Katarzyna Schulz, Laura Schulz, Martin Kranzlmuller, Dieter AGH University of Krakow Institute of Computer Science Krakow Poland CYFRONET AGH Academic Computer Center Krakow Poland Garching bei München Germany Chair for Computer Architecture and Parallel Systems Germany MNM-Team Germany

ISBN: (纸本)9798350355543

Load imbalance is a challenge for parallel applications in High Performance Computing (HPC). It is caused by processes having different execution times or load values, leading to idle or wait times at synchronization points, where faster processes must wait for the slowest process to catch up. To mitigate this issue, applications can employ load balancing (LB) strategies, which migrate load between processes to even out load. This is often referred to as the Load Rebalancing Problem (LRP). While many approaches solving the LRP exist, they can only be heuristics and hence further optimization potential exists. In our work, we turn to a novel approach by using hybrid classical-quantum approaches and present two versions of the constrained quadratic model for solving the LRP;the two differ in how they balance the number of qubits required with the types of applied constraints. We compare the quantum-based methods with classical methods using heuristic algorithms Greedy, Karmarkar-Karp, and ProactLB. We evaluate our approaches using imbalance ratio and speedup as metrics, as well as the number of migrated tasks to indicate overhead caused by migrations. Our results show that the quantum-based methods outperform the classic methods. For example, we need only 1/4 of the number of migrated tasks in a realistic use case compared with classical methods, particularly Greedy and KK, to balance the load. © 2024 IEEE.

关键词： CQM HPC HPCQC integration Load Rebalancing Quantum Computing task migration

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FaST-GShare: Enabling Efficient Spatio-Temporal GPU Sharing in Serverless Computing for Deep Learning Inference

arXiv

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arXiv 2023年

作者： Gu, Jianfeng Zhu, Yichao Wang, Puxuan Chadha, Mohak Gerndt, Michael Computer Architecture & Parallel Systems Technical University of Munich Munich Germany

Serverless computing (FaaS) has been extensively utilized for deep learning (DL) inference due to the ease of deployment and pay-per-use benefits. However, existing FaaS platforms utilize GPUs in a coarse manner for DL inferences, without taking into account spatio-temporal resource multiplexing and isolation, which results in severe GPU under-utilization, high usage expenses, and SLO (Service Level Objectives) violation. There is an imperative need to enable an efficient and SLO-aware GPU-sharing mechanism in serverless computing to facilitate cost-effective DL inferences. In this paper, we propose FaST-GShare, an efficient FaaS-oriented Spatio-Temporal GPU Sharing architecture for deep learning inferences. In the architecture, we introduce the FaST-Manager to limit and isolate spatio-temporal resources for GPU multiplexing. In order to realize function performance, the automatic and flexible FaST-Profiler is proposed to profile function throughput under various resource allocations. Based on the profiling data and the isolation mechanism, we introduce the FaST-Scheduler with heuristic auto-scaling and efficient resource allocation to guarantee function SLOs. Meanwhile, FaST-Scheduler schedules function with efficient GPU node selection to maximize GPU usage. Furthermore, model sharing is exploited to mitigate memory contention. Our prototype implementation on the OpenFaaS platform and experiments on MLPerf-based benchmark prove that FaST-GShare can ensure resource isolation and function SLOs. Compared to the time sharing mechanism, FaST-GShare can improve throughput by 3.15x, GPU utilization by 1.34x, and SM (Streaming Multiprocessor) occupancy by 3.13x on average. © 2023, CC BY-NC-ND.

关键词： Graphics processing unit

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Towards Autoscaling with Guarantees on Kubernetes Clusters

Towards Autoscaling with Guarantees on Kubernetes Clusters

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Autonomic Computing and Self-Organizing systems Companion (ACSOS-C), IEEE International Conference on

作者： Stephen Burroughs Helge Dickel Martin van Zijl Vladimir Podolskiy Michael Gerndt Robi Malik Panos Patros ORKA Cloud and Adaptive Systems Lab University of Waikato New Zealand Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany

Autoscaling is used by cloud providers, microser-vices, and edge computing applications to respond to dynamic load fluctuations. A critical direction of research has focused on providing guarantees under uncertainty that the auto scaling system will work as intended-both at design-time and more importantly, at runtime. In this work, we evaluate the efficacy of three complementary methods: A) deterministic finite automata, B) probabilistic process algebra and C) Proportional-Integral-Derivative (PID) control. We experimentally evaluate their efficacy in modelling and verify autoscaling on clusters managed by Kubernetes. Our results suggest that deterministic modelling can provide theoretically optimal guarantees for small deployments; probabilistic algebras are able to capture stochastic behaviours, but benefit from deterministic templates; and control theory benefits by providing a black-box approach for modelling, verification and control.

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iCheck: Leveraging RDMA and Malleability for Application-Level Checkpointing in HPC systems

iCheck: Leveraging RDMA and Malleability for Application-Lev...

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International Conference on parallel and Distributed systems (ICPADS)

作者： Jophin John Isaac David Núñez Araya Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Germany

The estimate that the mean time between failures will be in minutes in exascale supercomputers should be alarming for application developers. The inherent system’s complexity, millions of components, and susceptibility to failures make checkpointing more relevant than ever. Since most high performance scientific applications contain an in-house checkpoint restart mechanism, their performance can be impacted by the contention of parallel file system resources. A shift in checkpointing strategies is needed to thwart this behavior. With iCheck, we present a novel checkpointing framework that supports malleable multilevel application-level checkpointing. We employ an RDMA enabled configurable multi-agent-based checkpoint transfer mechanism where minimal application resources are utilized for checkpointing. The high-level API of iCheck facilitates easy integration and malleability. We have added the iCheck library into the Is1 mardyn application providing performance improvement up to five thousand times over the in-house checkpointing mechanism. LULESH, Jacobi 2D heat simulation, and a synthetic application were also used for extensive analysis.

关键词： Checkpointing Jacobian matrices Heating systems Fault tolerance File systems Fault tolerant systems Supercomputers

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Real-Time Capability of DLR’S Beamforming Synthetic Aperture Radar Processing architecture

Real-Time Capability of DLR’S Beamforming Synthetic Apertur...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Maron Schlemon Martin Schulz Rolf Scheiber Marc Jäger Joel Amao Oliva German Aerospace Center (DLR) Microwaves and Radar Institute Germany Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

Synthetic Aperture Radar (SAR) enables the generation of realistic and high-resolution 2D or 3D representations of landscapes. Typically, radar instruments are deployed in specially equipped, low-flying aircraft that capture a significant amount of raw data, necessitating image reconstruction processing. However, the aircraft’s limited onboard processing capabilities (power, size, weight, cooling, and communication bandwidth to ground stations) and the need to generate multiple SAR products, such as slant-range and geo-coded images during a single flight, require efficient onboard processing and transmission to the ground station. This paper outlines the processing architecture of the digital beamforming SAR (DBFSAR) employed by the German Aerospace Center (DLR) and the specific measures implemented to enable onboard processing. We elucidate the essential software optimizations and their integration into the SAR onboard routines, facilitating (near) real-time capability under certain conditions. Furthermore, we share the insights gained from our work and discuss their applicability to other processing scenarios with limited resource availability.

关键词： Three-dimensional displays Array signal processing Instruments computer architecture Radar imaging Real-time systems Software

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Real-Time Capability of Dlr's Beamforming Synthetic Aperture Radar Processing architecture

Real-Time Capability of Dlr's Beamforming Synthetic Aperture...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

ISBN: (数字)9798350320107

ISBN: (纸本)9798350331745

Synthetic Aperture Radar (SAR) enables the generation of realistic and high-resolution 2D or 3D representations of landscapes. Typically, radar instruments are deployed in specially equipped, low-flying aircraft that capture a significant amount of raw data, necessitating image reconstruction processing. However, the aircraft's limited onboard processing capabilities (power, size, weight, cooling, and communication bandwidth to ground stations) and the need to generate multiple SAR products, such as slant-range and geo-coded images during a single flight, require efficient onboard processing and transmission to the ground station. This paper outlines the processing architecture of the digital beamforming SAR (DBFSAR) employed by the German Aerospace Center (DLR) and the specific measures implemented to enable onboard processing. We elucidate the essential software optimizations and their integration into the SAR onboard routines, facilitating (near) real-time capability under certain conditions. Furthermore, we share the insights gained from our work and discuss their applicability to other processing scenarios with limited resource availability.

关键词： Three-dimensional displays Array signal processing Instruments computer architecture Radar imaging Real-time systems Software

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Scalable Infrastructure and Workflow for Anomaly Detection in an Automotive Industry

Scalable Infrastructure and Workflow for Anomaly Detection i...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Anshul Jindal Michael Gerndt Mario Bauch Hachim Haddouti Chair of Computer Architecture and Parallel Systems Technical University of Munich Garching (near Munich) Germany OTD Platform and Services Munich Germany

ISBN: (数字)9781728142104

ISBN: (纸本)9781728142111

Anomalies are unexpected instances which significantly deviate from the normal patterns formed by the majority of a dataset. The more an observation deviate from the normal pattern, the more likely it is an anomaly. The continuous increase in the number of car models and configuration possibilities has led to continuous increase in the complexity of logistics supply chain and production. Consequently, it has become difficult to manage the whole IT Landscape, a small anomaly/failure somewhere in the system could lead to a huge loss of money. Therefore, to identify and ultimately resolve quickly a problem in such a system is highly important. This paper addresses the challenge of identifying anomalies in a scalable way. The new data collected suffers from the problem of lack of labels for training. This challenge is addressed in the developed solution by using multiple unsupervised algorithms and reporting those observation as anomalies which are commonly reported as anomalies by all the algorithms. The developed solution also tackles the problem of data heterogeneity and big size by using Spark underneath for scalable data processing. Scalability test results demonstrate the reduction in training time of 100 transactions by 80% when using 10 cores instead of using 1 core. The results of the study have also pointed out that increasing the number of cores does not necessarily means reduction in the overall execution time, there are other factors like communications between the cores, non-spark related processing tasks, etc which can also influence the execution time.

关键词： Anomaly detection Time series analysis Industries Production Data models Support vector machines Automotive engineering

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