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Archpipe: Fast and Flexible Pipelined Erasure-coded Archival Scheme for Heterogeneous Networks

Archpipe: Fast and Flexible Pipelined Erasure-coded Archival...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Bin Xu Jianzhong Huang Xiao Qin Qiang Cao Yuanyuan Dong Weikang Kong Wuhan National Laboratory for Optoelectronics Huazhong University of Sci.&#x0026 Tech Auburn University Alibaba Group

Erasure-coded archival converts the redundancy mechanism of low access-frequency data from replication to erasure coding for balancing access performance and storage efficiency. A variety of pipelined schemes are designed to speed up the archival operation, however they neglect such three factors as heterogeneous network, under-utilization of replica resources and tight coupling with underlying platforms which restrict or even negate the performance gains. In this paper, we propose Archpipe, a fast and flexible pipelined erasure-coded archival scheme. It exhibits three distinct features: 1) heterogeneous network awareness, for a single-pipelined construction, sufficient-bandwidth links are given high scheduling priority to avoid network congestion, while considering locality to reducing network transmissions; 2) parallel encoding, the unused replica resources are exploited to adaptively construct multiple pipelines for each stripe based on the single-pipelined algorithm, thereby enabling parity blocks to be encoded in parallel; 3) loose coupling, it does not rely on specific block placement policies and stripe construction algorithms. Experimental results indicate that, Archpipe can be seamlessly integrated with common distributed storage systems, and it improves the erasure-coded archival performance by 3.6 ∼ 4.7× and 1.3 ∼ 2.6× in on-disk and in-memory scenarios, respectively.

关键词： Couplings distributed processing Systematics Shape Redundancy Boosting Heterogeneous networks

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Sampling Policies for Near-Optimal Device Choice in parallel Simulations on CPU/GPU Platforms

Sampling Policies for Near-Optimal Device Choice in Parallel...

引用

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

作者： Philipp Andelfinger Alessandro Pellegrini Romolo Marotta University of Rostock Germany Tor Vergata University of Rome Italy

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

Heterogeneous hardware platforms comprised of CPUs, GPUs, and other accelerators offer the opportunity to choose the best-suited device for executing a given scientific simulation in order to minimize execution time and energy consumption. To this end, the recently proposed "Follow the Leader" approach dynamically selects a suitable device based on runtime performance measurements during speculative discrete-event simulations. A currently active "leader" device is periodically challenged by a "follower" device in order to negotiate the new leader. The optimality of the device choices and the associated overhead depends critically on the challenge frequency and timing. Here, we explore policies to schedule challenges with the goal of attaining Pareto-optimal combinations of execution time and energy consumption. Several heuristics are first evaluated in an abstract fashion using a "meta-simulation" by mimicking the progress and energy consumption of an idealized co-execution. In this setting, we optimize the heuristics’ tuning parameters to assess their relative merits in near-optimal configurations when compared to challenge timings based on perfect knowledge. We find that under challenging stochastic workloads based on a class of mean-reverting random walks, the best heuristics can closely approximate the execution time and energy consumption achievable under an optimal device choice. Empirical support for this observation is given by measurements of a CPU/GPU co-execution of the Time Warp algorithm on physical hardware.

关键词： Energy consumption Schedules Runtime Approximation algorithms Hardware Robustness Real-time systems Timing Object recognition Tuning

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Role-Based Access Control on Graph databases 47th

Role-Based Access Control on Graph Databases

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47th international Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)

作者： Chabin, Jacques Ciferri, Cristina D. A. Halfeld-Ferrari, Mirian Hara, Carmem S. Penteado, Raqueline R. M. Univ Orleans LIFO EA INSA CVL Orleans France Univ Sao Paulo Sao Carlos Brazil Univ Fed Parana Curitiba Parana Brazil Univ Estadual Maringa Maringa Parana Brazil

ISBN: (纸本)9783030677305;9783030677312

We propose a novel access control system for graph-based models, which supports schema constraints and constraint rules to protect the data, as well as user context rules. We consider systems with huge volumes of data, where the efficient processing of aggregation operations is of paramount importance. To comply with this goal, we introduce an architecture with modules for rewriting, planning and executing queries in parallel, respecting the access constraints. Performance tests show the efficiency of our distributed query processing mechanism. Compared to a centralized approach, it reduces execution time from 25% to 68% for conjunctive queries and from 12% to 59% for queries involving aggregation.

关键词： Graph databases

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Introducing parallel Computing in a Second CS Course

Introducing Parallel Computing in a Second CS Course

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

作者： Tia Newhall Kevin C. Webb Vasanta Chaganti Andrew Danner Computer Science Department Swarthmore College Swarthmore PA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The ubiquity of multicore processors, cloud computing, and hardware accelerators have elevated parallel and distributed computing (PDC) topics into fundamental building blocks of the undergraduate CS curriculum. Therefore, it is increasingly important for students to learn a common core of introductory PDC topics and develop parallel thinking skills early in their CS studies. We present the curricular design, pedagogy, and goals of an introductory-level course on com-puter systems that introduces parallel computing to students who have only a CS1 background. Our course focuses on three curricular goals that serve to integrate the ACM-IEEE TCPP guidelines throughout: a vertical slice through the computer of how it runs a program; evaluating system costs associated with running a program; and taking advantage of the power of parallel computing. We elaborate on the goals and details of our course's key modules, and we discuss our pedagogical approach that includes active-learning techniques. We find that the PDC foundation gained through early exposure in this course helps students gain confidence in their ability to expand and apply their understanding of PDC concepts throughout their CS education.

关键词： distributed processing Costs Multicore processing Instruction sets Education Focusing parallel processing

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The Universal Gossip Fighter

The Universal Gossip Fighter

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international symposium on parallel and distributed Processing (IPDPS)

作者： Anastasiia Gorbunova Rachid Guerraoui Anne-Marie Kermarrec Anastasiia Kucherenko Rafaë l Pinot Ecole Polytechnique F&#x00E9 d&#x00E9 rale de Lausanne

The notion of adversary is a staple of distributed computing. An adversary typically models “hostile” assumptions about the underlying distributed environment, e.g., a network that can drop messages, an operating system that can delay processes or an attacker that can hack machines. So far, the goal of distributed computing researchers has mainly been to develop a distributed algorithm that can face a given adversary, the abstraction characterizing worst-case scenarios. This paper initiates the study of the somehow opposite approach. Given a distributed algorithm, the adversary is the abstraction we seek to implement. More specifically, we consider the problem of controlling the spread of messages in a large-scale system, conveying the practical motivation of limiting the dissemination of fake news or viruses. Essentially, we assume a general class of gossip protocols, called all-to-all gossip protocols, and devise a practical method to hinder the dissemination. We present the Universal Gossip Fighter (UGF). Just like classical adversaries in distributed computing, UGF can observe the status of a dissemination and decide to stop some processes or delay some messages. The originality of UGF lies in the fact that it is universal, i.e., it applies to any all-to-all gossip protocol. We show that any gossip protocol attacked by UGF ends up exhibiting a quadratic message complexity (in the total number of processes) if it achieves sublinear time of dissemination. We also show that if a gossip protocol aims to achieve a message complexity $\alpha$ times smaller than quadratic, then the time complexity rises exponentially in relation to $\alpha$ . We convey the practical relevance of our theoretical findings by implementing UGF and conducting a set of empirical experiments that confirm some of our results.

关键词： Protocols Limiting Operating systems Delays Complexity theory Large-scale systems distributed computing

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Challenges and Opportunities in Designing High-Performance and Scalable Middleware for HPC and AI: Past, Present, and Future

Challenges and Opportunities in Designing High-Performance a...

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international symposium on parallel and distributed Processing (IPDPS)

作者： D K Panda

This talk focuses on challenges and opportunities emerging over the years (past, present, and future) in designing middleware for HPC and AI (Deep/Machine Learning) workloads on modern high-end computing systems. The talk initially presents the challenges in designing HPC runtime environments with MPI+X programming models by considering support for dense multi-core CPUs, high-performance interconnects, GPUs, and emerging DPUs. Advanced designs and solutions (such as RDMA, in-network computing, GPUDirect RDMA, on-the-fly compression) to exploit novel features of these emerging technologies and their benefits in the context of MVAPICH2 libraries are presented. Next, the talk focuses on MPI-driven solutions for the Deep/Machine Learning domains to extract performance and scalability for popular Deep Learning frameworks, large out-of-core models, GPUs, and DPUs. MPI-driven solutions to accelerate data science applications like Dask are highlighted. Challenges and experiences in deploying this middleware to the HPC cloud environments for Azure, AWS, and Oracle Cloud are presented. The talk concludes with an overview of the newly established NSF-AI Institute ICICLE (https://***/) to address challenges in designing future high-performance edge-to-HPC/ cloud middleware for AI-driven data-intensive applications.

关键词： Middleware Libraries distributed processing Deep learning Computational modeling Scalability Runtime environment

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Research on Medical Big Data of Health Management Platform Based on Hadoop

Research on Medical Big Data of Health Management Platform B...

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international symposium on distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Xuan Zheng Xiaopan Ding College of Medical Information Engineering GanNan Medical University GanZhou JiangXi

ISBN: (纸本)9781665454636

The digitization process in the medical field continues to advance in depth and gradually enters the stage of smart medical treatment. Starting from the construction of the medical big data of health platform, this paper aims at the current problems of massive medical heterogeneous data resources and sharing, the difficulty of effective fusion of multi-source heterogeneous information in intelligent health services, and the real-time early warning of diseases, combined with the necessity of Hadoop platform and clinical medical data sharing, the HDFS distributed file system is used to manage metadata to improve the efficiency of integrated management of massive data; the data sharing mode of HBase is adopted to improve the degree of data sharing, with the HBase engine, Hive engine, SQLServer engine, etc., to meet the intelligence of different business data between heterogeneous, establish a data center with storage and management of patient health records and disease early warning as the core, and propose a big data management platform based on Hadoop to promote the scientific, professional, digital and refined operation and management of hospitals.

关键词： Data centers Hospitals File systems distributed databases Big Data Metadata Real-time systems

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A Scalable parallel Partition Tridiagonal Solver for Many-Core and Low B/F Processors

A Scalable Parallel Partition Tridiagonal Solver for Many-Co...

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

作者： Tatsuya Mitsuda Kenji Ono Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Research Institute for Information and Technology Kyushu University Fukuoka Japan

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Tridiagonal systems are among the most fundamental computations in science, engineering, and mathematics, and one solver used in such systems is Tree Partitioning Reduction (TPR), which is a divide-and-conquer method that solves large-scale linear equations by dividing them and then computing the parts in parallel within different local memory threads. Herein, we propose an improved TPR algorithm that has a parallel cyclic reduction flavor, with which we reduced the number of algorithm steps by approximately half while simultaneously increasing arithmetic intensity and cache reusability. A performance evaluation conducted on an Intel Skylake-SP microprocessor showed a high hit ratio for the L1 cache and that our solver was as much as 31 times faster on 32 threads for 262144 equations. In the case of a Nvidia Tesla P100 GPU, our method processed 10 MRow/s more than TPR and cuSPARSE.

关键词： Performance evaluation Microprocessors Scalability Instruction sets Memory management Graphics processing units Approximation algorithms

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DistrEdge: Speeding up Convolutional Neural Network Inference on distributed Edge Devices

DistrEdge: Speeding up Convolutional Neural Network Inferenc...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Xueyu Hou Yongjie Guan Tao Han Ning Zhang New Jersey Institute of Technology USA Windsor University Canada

As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col-laboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3 x speedup compared to state-of-the-art methods.

关键词： distributed processing Adaptive systems Portable computers Distribution strategy Computational modeling Laboratories Reinforcement learning

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Paving the way for distributed Non-Blocking Algorithms and Data Structures in the Partitioned Global Address Space model 34

Paving the way for Distributed Non-Blocking Algorithms and D...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Dewan, Garvit Jenkins, Louis Indian Inst Technol Roorkee Roorkee Uttar Pradesh India Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781728174457

The partitioned global address space memory model has bridged the gap between shared and distributed memory, and with this bridge comes the ability to adapt shared memory concepts, such as non-blocking programming, to distributed systems such as supercomputers. To enable non-blocking algorithms, we present ways to perform scalable atomic operations on objects in remote memory via remote direct memory access and pointer compression. As a solution to the problem of concurrent-safe reclamation of memory in a distributed system, we adapt Epoch-Based Memory Reclamation to distributed memory and implement it such that it supports global-view programming. This construct is designed and implemented for the Chapel programming language but can be adapted and generalized to work on other languages and libraries.

关键词： Supercomputers

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