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FLEXLOG: A Shared Log for Stateful Serverless Computing 23

FLEXLOG: A Shared Log for Stateful Serverless Computing

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32nd international symposium on High-Performance parallel and distributed Computing (HPDC) part of the ACM Federated Computing Research Conference (FCRC)

作者： Giantsidi, Dimitra Giortamis, Emmanouil Tornow, Nathaniel Dinu, Florin Bhatotia, Pramod Univ Edinburgh Edinburgh Midlothian Scotland Tech Univ Munich Munich Germany Huawei Munich Res Ctr Munich Germany

ISBN: (纸本)9798400701559

Stateful serverless applications need to persist their state and data. The existing approach is to store the data in general purpose storage systems. However, these approaches are not designed to meet the demands of serverless applications in terms of consistency, fault tolerance and performance. We present FlexLog, a storage system, specifically a distributed sharedlog, distinctively designedtomeet the requirements of stateful serverless computing while mitigating the relevant system bottlenecks. FlexLog's data layer leverages the state-of-the-art persistent memory (PM) to offer low latency I/O and improve performance. To match the performance, FlexLog's ordering layer employs a scalable design, namely a tree-structure set of sequencer nodes. Importantly, this design provides serverless applications with the flexibility to implement different consistency guarantees and to seamlessly support multi-tenancy configurations. We implement FlexLog from the ground up on a real hardware testbed and we also prove the correctness of our protocols. In particular, we evaluate FlexLog on a cluster of 6 machines with 800 GB Intel Optane DC PM over a 10 Gbps interconnect. Our evaluation shows that FlexLog scales to millions of operations per second while maintaining minimal latency. Our comparison with the state-of-the-art shared log for serverless, Boki, shows that we achieve 10x better throughput in the storage layer and 2x-4x lower latency in the ordering layer, while also providing flexibility to support different consistency properties and multi-tenancy.

关键词： Serverless Computing Shared distributed Log Persistent Memory

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HPPython: Extending Python with HPspmd for Data parallel Programming 2

HPPython: Extending Python with HPspmd for Data Parallel Pro...

引用

2nd international symposium on Computing and Artificial Intelligence, ISCAI 2023

作者： Nava, Judah Josiah Lee, Jaden Jinu Lee, Hanku Computer Science and Information Systems Department University Moorhead MN United States

ISBN: (纸本)9798400708954

In light of previous endeavors and trends in the realm of parallel programming, HPPython emerges as an essential superset that enhances the accessibility of parallel programming for developers, facilitating scalability across multiple nodes. Despite Python's popularity as a programming language in scientific and engineering applications and its native support for executing various processes, HPPython brings substantial simplification to the development of parallel programs and empowers program distribution across heterogeneous clusters consisting of multiple physical computers. HPPython leverages the MPI standard for its underlying communication, thereby harnessing the benefits of the SPMD model. Additionally, HPPython introduces novel syntax and constructs, such as parallel loops and distributed lists, while endeavoring to retain the natural essence of the original language. This paper delves into the distinct components of HPPython and elucidates their integration, establishing HPPython as a viable solution for parallel programming in today's data-driven world. © 2023 ACM.

关键词： parallel programming

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parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics 36

Parallel Approximations of the Tukey g-and-h Likelihoods and...

引用

36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Mondal, Sagnik Abdulah, Sameh Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia King Abdullah Univ Sci & Technol Stat Program Thuwal Saudi Arabia King Abdullah Univ Sci & Technol Extreme Comp Res Ctr Thuwal Saudi Arabia

ISBN: (纸本)9781665481069

Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Mat ' ern correlation function. This computation of the log-likelihood requires O( n2) storage and O( n3) operations, which can be a huge task considering that the number of geographical locations, n, now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey g-and-h (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different lowrank approximations. We compress the aforementioned positivedefinite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical OffDiagonal Low-Rank (HODLR) matrix approximatio

关键词： Maximum likelihood estimation Symmetric matrices Runtime Computational modeling Linear algebra Tail Predictive models

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DTS: A Simulator to Estimate the Training Time of distributed Deep Neural Networks 30

DTS: A Simulator to Estimate the Training Time of Distribute...

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30th international symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Robinson, Wilfredo J. M. Esposito, Flavio Zuluaga, Maria A. St Louis Univ Dept Comp Sci St Louis MO 63103 USA Eurecom Data Sci Dept Biot France

ISBN: (纸本)9781665455800

Deep Neural Networks (DNNs) process big datasets achieving high accuracy on incredibly complex tasks. However, this progress has led to a scalability impasse, as DNNs require massive amounts of processing power and local memory to be trained, making them impossible or impractical to be used on a single device. This situation has led to the design of distributed training architectures, where the DNN and the training data can be split among multiple processors. How to choose the appropriate distributed training architecture, however, remains an open question. To help bring insights into this debate, in this work we design a distributed Training Simulator (DTS) that estimates the training time of a DNN in a distributed architecture through a mathematical model of the distributed architecture and resource-allocation heuristics. We illustrate the power of the proposed DTS through the implementation of five different distributed architectures, Pipeline Learning, Federated Learning, Split Learning, parallel Split Learning, and Federated Split Learning, and we validate the accuracy of the training estimates using three different datasets of varying complexity and two different DNNs. Finally, we present a trade-off analysis to demonstrate the coherence of DTS estimates for diverse high-performance computing scenarios by comparing these estimates with the behaviors of a real computer cluster.

关键词： big data distributed computing deep learning simulator

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MDLoader: A Hybrid Model-driven Data Loader for distributed Deep Neural Networks Training

MDLoader: A Hybrid Model-driven Data Loader for Distributed ...

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

作者： Jonghyun Bae Jong Youl Choi Massimiliano Lupo Pasini Kshitij Mehta Khaled Z. Ibrahim Lawrence Berkeley National Laboratory USA Oak Ridge National Laboratory USA

In this work, we propose MD Loader, a hybrid in-memory data loader for distributed deep neural networks. MDLoader introduces a model-driven performance estimator to automatically switch between one-sided and collectiv... 详细信息

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

关键词： Training distributed processing Runtime Loading distributed databases Artificial neural networks Switches

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Base Station-Assisted Cooperative Network Coding for Cellular systems with Link Constraints

Base Station-Assisted Cooperative Network Coding for Cellula...

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IEEE international symposium on Information Theory (ISIT)

作者： Arslan, Suayb S. Pourmandi, Massoud Haytaoglu, Elif MEF Univ TR-34396 Maslak Istanbul Turkiye Bogazici Univ TR-34342 Bebek Istanbul Turkiye Pamukkale Univ TR-20067 Denizli Turkiye

ISBN: (纸本)9781665421607;9781665421591

We consider a novel distributed data storage/caching scenario in a cellular network, where multiple nodes may fail/depart simultaneously To meet reliability, we allow cooperative regeneration of lost nodes with the help of base stations allocated in a set of hierarchical layers1. Due to this layered structure, a symbol download from each base station has a different cost, while the link capacities between the nodes of the cellular system and the base stations are also constrained. Under such a setting, we formulate the fundamental trade-off with closed form expressions between repair bandwidth cost and the storage space per node. Particularly, the minimum storage as well as bandwidth cost points are formulated. Finally, we provide an explicit optimal code construction for the minimum storage regeneration point for a special set of system parameters.

关键词： Base stations Costs Codes Symbols distributed databases Bandwidth Cooperative systems

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Modeling pre-Exascale AMR parallel I/O Workloads via Proxy Applications 36

Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy A...

引用

36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Godoy, William F. Delozier, Jenna Watson, Gregory R. Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37830 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

ISBN: (纸本)9781665497473

The present work investigates the modeling of pre-exascale input/output (DO) workloads of Adaptive Mesh Refinement (AMR) simulations through a simple proxy application. We collect data from the AMReX Castro framework running on the Summit supercomputer for a wide range of scales and mesh partitions for the hydrodynamic Sedov case as a baseline to provide sufficient coverage to the formulated proxy model. The non-linear analysis data production rates are quantified as a function of a set of input parameters such as output frequency, grid size, number of levels, and the Courant-Friedrichs-Lewy (CFL) condition number for each rank, mesh level and simulation time step. Linear regression is then applied to formulate a simple analytical model which allows to translate AMReX inputs into MACSio proxy I/O application parameters, resulting in a simple "kernel" approximation for data production at each time step. Results show that MACSio can simulate actual AMReX nonlinear "static" I/O workloads to a certain degree of confidence on the Summit supercomputer using the present methodology. The goal is to provide an initial level of understanding of AMR I/O workloads via lightweight proxy applications models to facilitate autotune data management strategies in anticipation of exascale systems.

关键词： Proxy I/O AMR MACSio HPC exascale

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RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs 22

RSCache: A Tail Latency Friendly Cache Based on NVMe SSDs

引用

22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Lu, Jincheng Cai, Miao Ye, Baoliu Hohai University College of Computer Science and Software Engineering Nanjing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798331509712

High fan-out requests are prevalent in systems employing multi-tier architectures. These requests are divided into several sub-requests for parallel processing. However, a high fan-out request must await all sub-requests to be completed before returning, but the processing times of sub-requests are unpredictable due to their differences in characteristics, such as data volume and data popularity. Meanwhile, existing SSD-based caches struggle to adjust request processing speeds to ensure timely handling. As a result, some sub-requests are delayed, affecting the overall latency and causing long tail latency *** paper proposes RSCache, a tail latency-friendly cache based on NVMe SSDs. RSCache combines the NVMe Weighted Round-robin (WRR) arbitration mechanism with a priority-based scheduling mechanism to enable differentiated request processing. We propose a fan-out size-based priority assignment strategy along with a latency-aware sub-request sorting method. They collaborate to prioritize sub-requests according to their impacts on tail latency and schedule them to NVMe priority queues with various processing speeds, effectively reducing the processing time variations among sub-requests. In addition, we balance the load across queues to avoid congestion with a novel feedback mechanism. We implement the prototype of RSCache based on SPDK. Our experiments demonstrate that RSCache reduces tail latency by up to 40% and improves throughput by two times compared to state-of-the-art SSD-based cache designs. © 2024 IEEE.

关键词： parallel architectures

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Massive Natural Language Processing in distributed Environment 20th

Massive Natural Language Processing in Distributed Environm...

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20th international symposium on distributed Computing and Artificial Intelligence, DCAI 2023

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Kielce Poland

ISBN: (纸本)9783031383175

Natural Language Processing (NLP) models are one of the most promising topics nowadays. Applications like ChatGPT uncover the power of such models and their broad applications. However, developing such models requires an extensive amount of training data sets to operate on. As such data sets grow, it becomes impossible to process them on a single machine, even using parallel computations. In this paper, the problem of distributing the NLP computations is taken into consideration. The Scalable distributed Two-Layer Data Structures were used to organize the distributed environment. Stanford Core NLP library was used to perform accurate NLP computations. The experiments focused on the efficiency of the computation in the distributed environment and the accuracy of the predictions in relation to the scale of the distributions. The results clearly show that an increase in performance can be obtained without a significant decrease in accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Cybersecurity Insights: Analyzing IoT Data Through Statistical and Visualization Techniques

Cybersecurity Insights: Analyzing IoT Data Through Statistic...

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Jing Li Mohd Shahizan Othman Hewan Chen Lizawati Mi Yusuf Faculty of Computing University Technology Malaysia Johor Bahru Malaysia Research Center of Digital Twins China Jiliang University Hang Zhou China

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

The rising frequency of cyberattacks on Internet of Things (IoT) networks calls for sophisticated approaches to threat analysis and mitigation. The importance of data analysis-especially visualization-in identifying patterns that can guide successful mitigation efforts is emphasized in this research. To address class imbalance issues, we first developed thorough data preparation and basic class analysis. Next, we used the BoT-IoT and TON-IoT datasets to visualize an analysis of 5-tuple network properties. The Spearman and Pearson correlation coefficients were used to statistically validate these properties. Furthermore, class overlapping was revealed using unsupervised techniques using PCA and T-SNE, which shed light on the characteristics of IoT data. Our results show how these visualization methods can help reveal patterns in IoT datasets and illuminate the nature of the representative IoT data, in addition, this approach contributes to the development of robust cybersecurity safeguards for IoT security in the future.

关键词： Correlation coefficient Prevention and mitigation Data visualization distributed databases parallel processing Internet of Things Computer crime Principal component analysis

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