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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Nikitas M. Sgouros Department of Digital Systems University of Piraeus Piraeus Greece

The rise of deep learning methods has ignited interest in efficient hardware and software systems for tensor-based computing. A question worth investigating is whether other areas in computing can benefit as well from the power and increasing availability of such systems. To this end, we study the integration of tensor-based techniques in agent-based simulators. In particular, we describe methods for the representation as vectors of agent and edge attributes in agent networks and the compilation of rules governing agent behavior and their edge-based interactions into functions that can process and compute such attribute vectors in parallel. We describe a proof of concept implementation of such an idea in Politika, our Elixir-based web framework for concurrent, agent-based simulation. We discuss various simulation scenarios for our implementation and provide directions for future optimizations and integration pathways with concurrent simulation environments.

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BestNeighbor: efficient evaluation of kNN queries on large time series databases

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KNOWLEDGE AND INFORMATION systems 2021年第2期63卷 349-378页

作者： Levchenko, Oleksandra Kolev, Boyan Yagoubi, Djamel-Edine Akbarinia, Reza Masseglia, Florent Palpanas, Themis Shasha, Dennis Valduriez, Patrick Univ Montpellier Inria CNRS LIRMM Montpellier France Univ Paris Paris France NYU Dept Comp Sci New York NY USA

This paper presents parallel solutions (developed based on two state-of-the-art algorithms iSAX and sketch) for evaluating k nearest neighbor queries on large databases of time series, compares them based on various measures of quality and time performance, and offers a tool that uses the characteristics of application data to determine which algorithm to choose for that application and how to set the parameters for that algorithm. Specifically, our experiments show that: (i) iSAX and its derivatives perform best in both time and quality when the time series can be characterized by a few low-frequency Fourier Coefficients, a regime where the iSAX pruning approach works well. (ii) iSAX performs significantly less well when high-frequency Fourier Coefficients have much of the energy of the time series. (iii) A random projection approach based on sketches by contrast is more or less independent of the frequency power spectrum. The experiments show the close relationship between pruning ratio and time for exact iSAX as well as between pruning ratio and the quality of approximate iSAX. Our toolkit analyzes typical time series of an application (i) to determine optimal segment sizes for iSAX and (ii) when to use parallel Sketches instead of iSAX. Our algorithms have been implemented using Spark, evaluated over a cluster of nodes, and have been applied to both real and synthetic data. The results apply to any databases of numerical sequences, whether or not they relate to time.

关键词： Time series parallel indexing distributed querying Fourier coefficients Frequency analysis Filtering

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Informed Prefetching in I/O Bounded distributed Deep Learning

Informed Prefetching in I/O Bounded Distributed Deep Learnin...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Ruan, Xiaojun Chen, Haiquan Calif State Univ East Bay Dept Comp Sci Hayward CA 94542 USA Calif State Univ Sacramento Dept Comp Sci Sacramento CA 95819 USA

ISBN: (纸本)9781665435772

Deep learning research has been growing rapidly in the past decade for the significant performance improvement on GPUs. While the computing capability of current GPUs is tremendous, data pre-processing/loading becomes a potential bottleneck that incurs major training latency and adds overhead in both CPU and memory, especially when datasets are too large to fit in memory. When datasets are stripped on distributed file systems, access to a remote storage node may deteriorate I/O performance significantly due to network I/O latency in cloud. Moreover, some deep learning workloads may be assigned to remote GPU servers in Edge Computing which results in even higher network I/O ***, it is desirable to provide efficient parallel and distributed prefetching solution which is able to reduce the I/O cost of data pre-processing before feeding the data into GPUs for training on distributed storage systems of Cloud or Edge. Although the current deep learning frameworks like PyTorch or TensorFlow offer multiprocessing data loading functionalities, their approaches come at the price of high computing resource usage and memory usage. In this paper, we presented a novel thread-level Informed Prefetching Data Loader framework, IPDL, in support of efficient data prefetching from remote storage nodes in distributed deep learning environments and possibly in Edge Computing. Compared to its counterparts in PyTorch, IPDL is able to provide accelerated I/O performance for data loading while consuming lower computing resource and memory space at the same time. Extensive experiments on both an individual server and a cluster computing system have shown the superiority of IPDL over the latest implementation of PyTorch.

关键词： Prefetching Deep Learning Neural Network Virtual distributed File System

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Download time analysis for distributed storage systems with node failures

Download time analysis for distributed storage systems with ...

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IEEE international symposium on Information Theory (ISIT)

作者： Hellemans, Tim Yardi, Arti Bodas, Tejas Univ Antwerp Antwerp Belgium IIIT Bangalore Bangalore Karnataka India IIT Dharwad Dharwad Karnataka India

ISBN: (纸本)9781538682098

We consider a distributed storage system which stores several hot (popular) and cold (less popular) data files across multiple nodes or servers. Hot files are stored using repetition codes while cold files are stored using erasure codes. The nodes are prone to failure and hence at any given time, we assume that only a fraction of the nodes are available. Using a cavity process based mean field framework, we analyze the download time for users accessing hot or cold data in the presence of failed nodes. Our work also illustrates the impact of the choice of the storage code on the download time performance of users in the system.

关键词： Analytical models distributed databases Numerical models Servers Information theory

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Q-CASA Invited Speakers Quantum-Centric Supercomputing Strategies for Neuroscience problems: Challenges and Progress

Q-CASA Invited Speakers Quantum-Centric Supercomputing Strat...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Robert Loredo Fahad Saeed IBM Quantum ambassador Miami FL USA Florida International University (FIU) Miami FL USA

For decades high-performance computing systems have allowed researchers, scientists and engineers to solve complex data- and compute-intensive problems. These parallel and distributed computing systems provided researchers to develop state of the art solutions in areas such as machine learning, health, and life science. While successful for a large number of problems, these systems face limitations when handling intractable problems which require either an enormous number of resources (i.e. number of bits, or power) or time to solve a problem (i.e. factoring integers). In this work we describe how quantum computers can enhance these systems by incorporating quantum computational principles to create a quantum-centric supercomputer. Recent developments include quantum middleware, error mitigation, and error suppression techniques to curtail these issues in near-term quantum devices and enable researchers to create quantum useful applications without having to wait for full fault-tolerant quantum computers. We also give an overview of dynamic circuits, circuit knitting, and various error suppression techniques recently developed to help minimize noise and execute quantum circuits on multiple quantum systems. Our preliminary work shows recent challenges and advancements in quantum-centric supercomputing when trying to solve neuroscience problems. We focus on functional MRI (fMRI) and diffusion MRI problems related to mental health and the potential areas where quantum computers can alleviate some of these challenges. We also present preliminary solutions to connectomics, tractography, and brain network analysis that can be used to help researchers identify neurodegenerative diseases or injuries.

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HetDAPAC: distributed Attribute-Based Private Access Control with Heterogeneous Attributes

HetDAPAC: Distributed Attribute-Based Private Access Control...

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IEEE international symposium on Information Theory

作者： Shreya Meel Sennur Ulukus Department of Electrical and Computer Engineering University of Maryland College Park MD

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Verifying user attributes to provide fine-grained access control to databases is fundamental to an attribute-based authentication system. In such systems, either a single (central) authority verifies all attributes, or multiple independent authorities verify individual attributes distributedly to allow a user to access records stored on the servers. While a central setup is more communication cost efficient, it causes privacy breach of all user attributes to a central authority. Recently, Jafarpisheh et al. studied an information theoretic formulation of the distributed multi-authority setup with $N$ non-colluding authorities, $N$ attributes and $K$ possible values for each attribute, called an $(N, K)$ distributed attribute-based private access control (DAPAC) system, where each server learns only one attribute value that it verifies, and remains oblivious to the remaining $N-1$ attributes. We show that off-loading a subset of attributes to a central server for verification improves the achievable rate from $\fracdistributed{2K}$ in Jafarpisheh et al. to $\fracdistributed{K+1}$ in this paper, thus almost doubling the rate for relatively large $K$ , while sacrificing the privacy of a few possibly non-sensitive attributes.

关键词： Access control Costs databases Authentication Privacy breach Servers Information theory

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Nonblocking execution in GraphBLAS

Nonblocking execution in GraphBLAS

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Aristeidis Mastoras Sotiris Anagnostidis Albert– Jan N. Yzelman Computing Systems Laboratory Zurich Research Center Huawei Technologies Switzerland Data Analytics Laboratory Department of Computer Science ETH Zurich Switzerland

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

GraphBLAS is a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are executed either in blocking or in non-blocking mode. Although there exist multiple implementations of GraphBLAS for efficient blocking execution on both shared-and distributed-memory systems, none of these implementations supports full nonblocking execution to improve data locality. In this paper, we present a preliminary evaluation for two algorithms, Pagerank and Conjugate Gradient, that confirms the importance of nonblocking execution, by showing promising speedups over the corresponding blocking execution.

关键词： Analytical models distributed processing Heuristic algorithms Instruction sets distributed databases Manuals Linear algebra

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Serialization-Oriented Data Layout for distributed and Real-Time Agent-Based Simulation

Serialization-Oriented Data Layout for Distributed and Real-...

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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Zhuoxiao Meng Mingyue Gao Stefano Bortoli Christoph Sommer Alois Knoll Department of Informatics Technical University of Munich Germany Intelligent Cloud Technologies Laboratory Huawei Munich Research Center Germany Faculty of Computer Science TU Dresden Germany

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

Data transfer efficiency is a frequent bottleneck of distributed (co-)simulations and X-in-the-loop systems. One of the key reasons, particularly in Agent-Based Simulation (ABS), is related to the low serialization performance caused by non-optimal data layout in memory. To address these challenges, this paper explores the potentials of a Serialization-oriented Data Layout (SoDaLa) approach for ABS building on Data Oriented Design (DOD) principles. In this work we also introduce ABS_M, a model-based abstraction of memory access in ABS. Using this model, we evaluate the impact of SoDaLa. This is done also to promote the adoption of SoDaLa and to ease the assessment of data layout strategies prior to full implementation in an existing codebases. The results indicate that our proposed approach enhances serialization efficiency and highlights the trade-offs between serialization efficiency and simulation performance across different model specifics and hardware conditions.

关键词： Layout Memory management distributed databases Data transfer Real-time systems Robustness Hardware Data models US Department of Defense Context modeling

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THE ARIA-S1-GUNW: THE ARIA SENTINEL-1 GEOCODED UNWRAPPED PHASE PRODUCT FOR OPEN INSAR SCIENCE AND DISASTER RESPONSE

THE ARIA-S1-GUNW: THE ARIA SENTINEL-1 GEOCODED UNWRAPPED PHA...

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IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Bekaert, David Arena, Nicholas Bato, M. Grace Buzzanga, Brett Govorcin, Marin Havazli, Emre Hogenson, Kirk Hua, Hook Johnston, Andrew Karim, Mohammed Kennedy, Joseph H. Lu, Zhong Marshak, Charlie Z. Meyer, Franz Owen, Susan Sangha, Simran Short, Gregory Wang, Jiahui Zinke, Robert CALTECH Jet Prop Lab Pasadena CA 91109 USA NASA Distributed Active Arch Ctr Alaska Satellite Facil Fairbanks AK 99775 USA Univ Alaska Fairbanks Geophys Inst Fairbanks AK 99775 USA Southern Methodist Univ Dept Earth Sci Dallas TX 75205 USA

ISBN: (纸本)9798350320107

NASA has committed to open-source science that enables Earth observation data transparency, inclusivity, accessibility, and reproducibility - all fundamental to the pace and quality of scientific progress. We have embraced this vision by producing standard InSAR science products that are freely available to the public through NASA Data Active Archive Centers (DAACs) and are generated using state-of-the-art open-source and openly-developed methods. The Advanced Rapid Image Analysis (ARIA) project's Sentinel-1 Geocoded Unwrapped Phase product (ARIA-S1-GUNW) is a 90 meter InSAR product that spans major, land-based fault systems, the US Coasts, and active volcanic regions through the complete Sentinel-1 record. The products enable the measurement of centimeter-scale surface displacement with applications across the solid earth, hydrology, and sea-level disciplines. The ARIA-S1-GUNW also enables rapid response mapping of surface motion after earthquakes, landslides, and subsidence. The ARIA-S1-GUNW products are freely available through the Alaska Satellite Facility (ASF) DAAC. In the last year, we have successfully grown the archive to over 1.1 million products, a 6 fold increase, through NASA ACCESS by improving our processing workflow and leveraging HyP3, an AWS-based cloud processing environment. We are continuing to partner with researchers to generate more products over relevant areas of scientific interest. All the processing software and cloud infrastructure are open-source to ensure reproducibility and enable other scientists to modify, improve upon, and scale their own cloud workflows for related InSAR analyses. We have, in parallel, developed and supported open-source, well-documented tools to further streamline time-series analysis from the ARIA-S1-GUNW into deformation analysis workflows.

关键词： InSAR Open Science Surface Deformation Disaster Response

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GPU-LSolve: An Efficient GPU-Based Laplacian Solver for Million-Scale Graphs

GPU-LSolve: An Efficient GPU-Based Laplacian Solver for Mill...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Sumiaya Dabeer Amitabha Bagchi Rahul Narain Department of Computer Science Indian Institute of Technology Delhi New Delhi India

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Breaking from the general run of Laplacian solvers that depend on algebraic primitives, we present the first GPU implementation of a message-passing-based solver. Our solver called GPU-LSolve, implements a randomized algorithm that simulates a queueing network where some nodes act as sources that generate messages and one node acts as a sink that removes messages from the network. The steady state of this network provides a solution for the Laplacian system of equations. We show how the simplicity of the primitives of this algorithm can be leveraged in a G PU setting to provide an efficient implementation that can solve Laplacian systems on million-scale graphs. Our solver takes advantage of GPU parallelism through sorting and key-value reduction. We have provided an extensive experimental evaluation on real data sets against several recently developed solvers. It is shown from the results that the presented solver does not suffer much in terms of memory footprint and execution time.

关键词： distributed processing Laplace equations Conferences Graphics processing units parallel processing Mathematical models Steady-state

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