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arXiv 2021年

作者： Jindal, Anshul Staab, Paul Cardoso, Jorge Gerndt, Michael Podolskiy, Vladimir Chair of Computer Architecture and Parallel Systems Technical University of Munich Garching Germany Huawei Munich Research Center Huawei Technologies Munich Germany

A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details. This paper addresses this challenge of online detection of memory leaks in cloud-based infrastructure without having any internal application knowledge by introducing a novel machine learning based algorithm Precog. This algorithm solely uses one metric i.e the system’s memory utilization on which the application is deployed for the detection of a memory leak. The developed algorithm’s accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by our industry partner Huawei Munich Research Center and it was found that the proposed algorithm achieves the accuracy score of 85% with less than half a second prediction time per virtual machine. © 2021, CC BY.

关键词： Machine learning

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Windsurfing with APPA: Automating Computational Fluid Dynamics Simulations of Wind Flow using Cloud Computing

Windsurfing with APPA: Automating Computational Fluid Dynami...

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Euromicro Conference on parallel, Distributed and Network-Based Processing

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

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Computational fluid dynamics (CFD) can serve as a complementary approach to conventional wind tunnel testing to assess the wind flow around tall buildings. Being a clear High Performance Computing (HPC) task, CFD simulations conventionally run on supercomputers and compute clusters using specialized software such as OpenFOAM. The limited availability and high maintenance costs of supercomputers and clusters force small and medium companies to search for the cost-efficient infrastructure to conduct their simulations with the appropriate performance. The on-demand offer of compute capacity by cloud service providers are well suited this task. However, engineers and researchers require extensive expertise and experience in working with cloud computing in order to benefit from running CFD simulations on a *** contribution of the paper to the outlined problem is two-fold: 1) a unique Automated parallel Processing Application (APPA) tool that hides the cloud management details from the wind engineer and provides an intuitive user interface; 2) the estimation of the optimal number of cores (vCPUs) for virtual machine instances provided by AWS and Google Cloud based on average run time and total cost metrics for a given number of cells of a CFD-simulation. n1-highcpu-96 Google Cloud VM met both goals: low cost and low runtime per timestep. For the number of vCPUs below 16, the c4.8xlarge AWS VM type has the least runtime per timestep in all the cases. Google Cloud instances with high vCPUs are recommended to run the simulations if budget is a big concern.

关键词： Cloud computing Computational modeling Google Containers Buildings Tools Numerical models

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

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

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Quantum Computing and Engineering (QCE), IEEE International Conference on

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

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.

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An Accurate and Hardware-Efficient Dual Spike Detector for Implantable Neural Interfaces

An Accurate and Hardware-Efficient Dual Spike Detector for I...

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2022 IEEE Biomedical Circuits and systems Conference, BioCAS 2022

作者： Guo, Xiaorang Shaeri, MohammadAli Shoaran, Mahsa Institute of Electrical and Micro Engineering Center for Neuroprosthetics Epfl Geneva1202 Switzerland Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden01069 Germany Chair of Computer Architecture and Parallel Systems Technische Universität München Garching85748 Germany

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

Spike detection plays a central role in neural data processing and brain-machine interfaces (BMIs). A challenge for future-generation implantable BMIs is to build a spike detector that features both low hardware cost and high performance. In this work, we propose a novel hardware-efficient and high-performance spike detector for implantable BMIs. The proposed design is based on a dual-detector architecture with adaptive threshold estimation. The dual-detector comprises two separate TEO-based detectors that distinguish a spike occurrence based on its discriminating features in both high and low noise scenarios. We evaluated the proposed spike detection algorithm on the Wave Clus dataset. It achieved an average detection accuracy of 98.9%, and over 95% in high-noise scenarios, ensuring the reliability of our method. When realized in hardware with a sampling rate of 16kHz and 7-bits resolution, the detection accuracy is 97.4%. Designed in 65nm TSMC process, a 256-channel detector based on this architecture occupies only 682μm2/Channel and consumes 0.07μW/Channel, improving over the state-of-the-art spike detectors by 39.7% in power consumption and 78.8% in area, while maintaining a high accuracy. © 2022 IEEE.

关键词： Data handling

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Comparison of Atom Detection Algorithms for Neutral Atom Quantum Computing

Comparison of Atom Detection Algorithms for Neutral Atom Qua...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Jonas Winklmann Andrea Alberti Martin Schulz Chair of Computer Architecture and Parallel Systems Technical University of Munich Munich Germany Quantum Many-Body Systems Division Max Planck Institute of Quantum Optics Garching Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In neutral atom quantum computers, readout and preparation of the atomic qubits are usually based on fluorescence imaging and subsequent analysis of the acquired image. For each atom site, the brightness or some comparable metric is estimated and used to predict the presence or absence of an atom. Across different setups, we can see a vast number of different approaches used to analyze these images. Often, the choice of detection algorithm is either not mentioned at all or it is not justified. We investigate several different algorithms and compare their performance in terms of both precision and execution run time. To do so, we rely on a set of synthetic images across different simulated exposure times with known occupancy states, which we generated using a previously validated imaging simulation. Since the use of simulation provides us with the ground truth of atom site occupancy, we can easily state precise error rates and variances of the reconstructed property. However, knowing the relative performance of these algorithms is not sufficient to justify their use, since better ones can exist that were not compared. To investigate this possibility, we calculated the Cramer-Rao bound in order to establish an upper limit that even a perfect estimator cannot outperform. As the metric of choice, we used the number of photonelectrons that can be contributed to a specific atom site. Every estimator that reconstructs a different property can simply be scaled accordingly. Since the bound depends on the occupancy of neighboring sites, we provide the best and worst cases, as well as a half filled one, which should represent an averaged bound best. Our comparison shows that of our tested algorithms, a global nonlinear least-squares solver that uses the optical system's point spread function (PSF) to return a global bias and each sites' number of photoelectrons performed the best, on average crossing the worst-case bound for longer exposure times. Its main drawback is its huge

关键词： Measurement computers Runtime Deconvolution Error analysis Qubit Quantum mechanics Prediction algorithms Detection algorithms Image reconstruction

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gFaaS: Enabling Generic Functions in Serverless Computing

gFaaS: Enabling Generic Functions in Serverless Computing

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Mohak Chadha Paul Wieland Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

With the advent of AWS Lambda in 2014, Serverless Computing, particularly Function-as-a-Service (FaaS), has witnessed growing popularity across various application domains. FaaS enables an application to be decomposed into fine-grained functions that are executed on a FaaS platform. It offers several advantages such as no infrastructure management, a pay-per-use billing policy, and on-demand fine-grained autoscaling. However, despite its advantages, developers today encounter various challenges while adopting FaaS solutions that reduce productivity. These include FaaS platform lock-in, support for diverse function deployment parameters, and diverse interfaces for interacting with FaaS platforms. To address these challenges, we present gFaaS, a novel framework that facilitates the holistic development and management of functions across diverse FaaS platforms. Our framework enables the development of generic functions in multiple programming languages that can be seamlessly deployed across different platforms without modifications. Results from our experiments demonstrate that gFaaS functions perform similarly to native platform-specific functions across various scenarios. A video demonstrating the functioning of gFaaS is available from https://***/STbb6ykJFf0.

关键词： Productivity computer languages Serverless computing Software

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Comparison of Atom Detection Algorithms for Neutral Atom Quantum Computing

arXiv

引用

arXiv 2024年

作者： Winklmann, Jonas Alberti, Andrea Schulz, Martin Chair of Computer Architecture and Parallel Systems Technical University of Munich Munich Germany Quantum Many-Body Systems Division Max Planck Institute of Quantum Optics Garching Germany

In neutral atom quantum computers, readout and preparation of the atomic qubits are usually based on fluorescence imaging and subsequent analysis of the acquired image. For each atom site, the brightness or some comparable metric is estimated and used to predict the presence or absence of an atom. Across different setups, we can see a vast number of different approaches used to analyze these images. Often, the choice of detection algorithm is either not mentioned at all or it is not justified. We investigate several different algorithms and compare their performance in terms of both precision and execution run time. To do so, we rely on a set of synthetic images across different simulated exposure times with known occupancy states, which we generated using a previously validated imaging simulation. Since the use of simulation provides us with the ground truth of atom site occupancy, we can easily state precise error rates and variances of the reconstructed property. However, knowing the relative performance of these algorithms is not sufficient to justify their use, since better ones can exist that were not compared. To investigate this possibility, we calculated the Cramér-Rao bound in order to establish an upper limit that even a perfect estimator cannot outperform. As the metric of choice, we used the number of photonelectrons that can be contributed to a specific atom site. Every estimator that reconstructs a different property can simply be scaled accordingly. Since the bound depends on the occupancy of neighboring sites, we provide the best and worst cases, as well as a half filled one, which should represent an averaged bound best. Our comparison shows that of our tested algorithms, a global non-linear least-squares solver that uses the optical system’s point spread function (PSF) to return a global bias and each sites’ number of photoelectrons performed the best, on average crossing the worst-case bound for longer exposure times. Its main drawback is its hug

关键词： Quantum computers

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Realistic Neutral Atom Image Simulation

Realistic Neutral Atom Image Simulation

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Jonas Winklmann Dimitrios Tsevas Martin Schulz Chair of Computer Architecture and Parallel Systems Technical University of Munich Munich Germany Quantum Many-Body Systems Division Max Planck Institute of Quantum Optics Garching Germany

Neutral atom quantum computers require accurate single atom detection for the preparation and readout of their qubits. This is usually done using fluorescence imaging. The occupancy of an atom site in these images is often somewhat ambiguous due to the stochastic nature of the imaging process. Further, the lack of ground truth makes it difficult to rate the accuracy of reconstruction algorithms. We introduce a bottom-up simulator that is capable of generating sample images of neutral atom experiments from a description of the actual state in the simulated system. Possible use cases include the creation of exemplary images for demonstration purposes, fast training iterations for deconvolution algorithms, and generation of labeled data for machine-learning-based atom detection approaches. The implementation is available through our GitHub as a C library or wrapped Python package. We show the modeled effects and implementation of the simulations at different stages of the imaging process. Not all real-world phenomena can be reproduced perfectly. The main discrepancies are that the simulator allows for only one characterization of optical aberrations across the whole image, supports only discrete atom locations, and does not model all effects of complementary metal-oxide-semiconductor (CMOS) cameras perfectly. Nevertheless, our experiments show that the generated images closely match real-world pictures to the point that they are practically indistinguishable and can be used as labeled data for training the next generation of detection algorithms.

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FedLess: Secure and Scalable Federated Learning Using Serverless Computing

FedLess: Secure and Scalable Federated Learning Using Server...

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IEEE International Conference on Big Data

作者： Andreas Grafberger Mohak Chadha Anshul Jindal Jianfeng Gu Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (Near Munich) Germany

ISBN: (纸本)9781665445993

The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacy-sensitive domains like healthcare. Towards this, a new learning paradigm called Federated Learning (FL) has been proposed that brings the potential of DL to these domains while addressing privacy and data ownership issues. FL enables clients to learn a shared ML model while keeping the data local. However, conventional FL systems face challenges such as scalability, complex infrastructure management, and wasted compute and incurred costs due to idle clients. These challenges of FL systems closely align with the core problems that serverless computing and Function-as-a-Service (FaaS) platforms aim to solve. These include rapid scalability, no infrastructure management, automatic scaling to zero for idle clients, and a pay-per-use billing model. To this end, we present a novel system and framework for serverless FL, called FedLess. Our system supports multiple commercial and self-hosted FaaS providers and can be deployed in the cloud, on-premise in institutional data centers, and on edge devices. To the best of our knowledge, we are the first to enable FL across a large fabric of heterogeneous FaaS providers while providing important features like security and Differential Privacy. We demonstrate with comprehensive experiments that the successful training of DNNs for different tasks across up to 200 client functions and more is easily possible using our system. Furthermore, we demonstrate the practical viability of our methodology by comparing it against a traditional FL system and show that it can be cheaper and more resource-efficient.

关键词： Training Scalability Computational modeling Serverless computing Training data FAA Big Data

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Extending SLURM for Dynamic Resource-Aware Adaptive Batch Scheduling

Extending SLURM for Dynamic Resource-Aware Adaptive Batch Sc...

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International Conference on High Performance Computing

作者： Mohak Chadha Jophin John Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany

ISBN: (纸本)9781665422925;9781665446501

With the growing constraints on power budget and increasing hardware failure rates, the operation of future exascale systems faces several challenges. Towards this, resource awareness and adaptivity by enabling malleable jobs has been actively researched in the HPC community. Malleable jobs can change their computing resources at runtime and can significantly improve HPC system performance. However, due to the rigid nature of popular parallel programming paradigms such as MPI and lack of support for dynamic resource management in batch systems, malleable jobs have been largely unrealized. In this paper, we extend the SLURM batch system to support the execution and batch scheduling of malleable jobs. The malleable applications are written using a new adaptive parallel paradigm called Invasive MPI which extends the MPI standard to support resource-adaptivity at runtime. We propose two malleable job scheduling strategies to support performance-aware and power-aware dynamic reconfiguration decisions at runtime. We implement the strategies in SLURM and evaluate them on a production HPC system. Results for our performance-aware scheduling strategy show improvements in makespan, average system utilization, average response, and waiting times as compared to other scheduling strategies. Moreover, we demonstrate dynamic power corridor management using our power-aware strategy.

关键词： Runtime Processor scheduling parallel programming System performance High performance computing Production Dynamic scheduling

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