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4th Workshop on parallel AI and systems for the Edge

Proceedings - 2022 IEEE 36th International Parallel and Dist...

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proceedings - 2022 IEEE 36th international parallel and distributed Processing symposium Workshops, IPDPSW 2022 2022年 982-984页

作者： Akkus, Istemi Ekin Desai, Nirmit V Nokia Bell Labs Germany Ibm Research United States

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Generalized Flow-Graph Programming Using Template Task-Graphs: Initial Implementation and Assessment

Generalized Flow-Graph Programming Using Template Task-Graph...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Joseph Schuchart Poornima Nookala Mohammad Mahdi Javanmard Thomas Herault Edward F. Valeev George Bosilca Robert J. Harrison Innovative Computing Laboratory The University of Tennessee Knoxville TN USA Institute for Advanced Computational Science Stony Brook University Stony Brook NY USA Meta Platforms Inc New York NY USA Department of Chemistry Virginia Polytechnic Institute and State University Blacksburg VA USA

We present and evaluate TTG, a novel programming model and its C++ implementation that by marrying the ideas of control and data flowgraph programming supports compact specification and efficient distributed execution of dynamic and irregular applications. Programming interfaces that support task-based execution often only support shared memory parallel environments; a few support distributed memory environments, either by discovering the entire DAG of tasks on all processes, or by introducing explicit communications. The first approach limits scalability, while the second increases the complexity of programming. We demonstrate how TTG can address these issues without sacrificing scalability or programmability by providing higher-level abstractions than conventionally provided by task-centric programming systems, without impeding the ability of these runtimes to manage task creation and execution as well as data and resource management efficiently. TTG supports distributed memory execution over 2 different task runtimes, PaRSEC and MADNESS. Performance of four paradigmatic applications (in graph analytics, dense and block-sparse linear algebra, and numerical integrodifferential calculus) with various degrees of irregularity implemented in TTG is illustrated on large distributed-memory platforms and compared to the state-of-the-art implementations.

关键词： Runtime Processor scheduling Scalability distributed databases C++ languages Programming Data models

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parallel Query Service for Object-centric Data Management systems 34

Parallel Query Service for Object-centric Data Management Sy...

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

作者： Tang, Houjun Byna, Suren Dong, Bin Koziol, Quincey Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

ISBN: (纸本)9781728174457

While large-scale scientific experiments and simulations produce massive amounts of data, a small fraction of data contains useful information. Efficient querying on such volume of data to extract that information increases the productivity of the scientific discovery process. Although querying has been explored extensively in relational databases, research and adoption of querying tools for scientific data that is stored in parallel file systems on high performance computing (HPC) systems are still in infancy. In this paper, we introduce a parallel query service, called PDC-Query, for an object data management systems (ODMS) on HPC systems. It operates on partitioned objects in parallel, and provides several optimization strategies for fast query evaluation. The ODMS paradigm for HPC systems is promising in reducing the burden on users in data management and in moving data transparently across the deep memory hierarchy in modern HPC systems. We propose a 'global histogram'-based approach to accelerate query evaluation, through selectivity estimation and reducing the amount of data that needs to be loaded from storage and processed. We compare querying performance and demonstrate the efficiency and scalability of different approaches PDC-Query supports, including using global histograms, bitmap indexes, sorting, and full scan, in performing various queries on top of a plasma physics dataset with 125 billion particles and an astronomy dataset with 25 million objects.

关键词： Information management

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Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines

Sequre: a high-performance framework for rapid development o...

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

作者： Haris Smajlović Ariya Shajii Bonnie Berger Hyunghoon Cho Ibrahim Numanagić University of Victoria BC Canada MIT Massachusetts USA Broad Institute of MIT and Harvard Massachusetts USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Genomic data leaks are irreversible. Leaked DNA cannot be changed, stays disclosed indefinitely, and affects the owner's family members as well. The recent large-scale genomic data collections [1], [2] render the traditional privacy protection mechanisms, like the Health Insurance Portability and Accountability Act (HIPAA), inadequate for protection against the novel security attacks [3]. On the other hand, data access restrictions hinder important clinical research that requires large datasets to operate [4]. These concerns can be naturally addressed by the employment of privacy-enhancing technologies, such as a secure multiparty computation (MPC) [5]–[10]. Secure MPC enables computation on data without disclosing the data itself by dividing the data and computation between multiple computing parties in a distributed manner to prevent individual computing parties from accessing raw data. MPC systems are being increasingly adopted in fields that operate on sensitive datasets [11]–[13], such as computational genomics and biomedical research [14]–[22].

关键词： distributed processing Data privacy Pipelines Employment distributed databases Genomics Insurance

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19th IFIP WG 10.3 international Conference on Network and parallel Computing, NPC 2022

19th IFIP WG 10.3 International Conference on Network and Pa...

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19th IFIP WG 10.3 international Conference on Network and parallel Computing, NPC 2022

ISBN: (纸本)9783031213946

The proceedings contain 31 papers. The special focus in this conference is on Network and parallel Computing. The topics include: Adjust: An Online Resource Adjustment Framework for Microservice Programs;cloud-Native Server Consolidation for Energy-Efficient FaaS Deployment;neuProMa: A Toolchain for Mapping Large-Scale Spiking Convolutional Neural Networks onto Neuromorphic Processor;Multi-clusters: An Efficient Design Paradigm of NN Accelerator Architecture Based on FPGA;TrainFlow: A Lightweight, Programmable ML Training Framework via Serverless Paradigm;DRP:Discrete Rank Pruning for Neural Network;transMigrator: A Transformer-Based Predictive Page Migration Mechanism for Heterogeneous Memory;Hardware Acceleration for 1D-CNN Based Real-Time Edge Computing;DLC: An Optimization Framework for Full-State Quantum Simulation;Optimizing Winograd Convolution on GPUs via Partial Kernel Fusion;Approximation Algorithms for Reliability-Aware Maximum VoI on AUV-Aided Data Collections;CCSBD: A Cost Control System Based on Blockchain and DRG Mechanism;Number of UAVs and Mission Completion Time Minimization in Multi-UAV-Enabled IoT Networks;a Spatial-Temporal Similarity-Based Cooperative Surveillance Framework by Edge;A Progressive Transmission Method of Cloud Point Data for HD Map in Autonomous Driving;IMRSim: A Disk Simulator for Interlaced Magnetic Recording Technology;Alleviating Performance Interference Through Intra-Queue I/O Isolation for NVMe-over-Fabrics;WALOR: Workload-Driven Adaptive Layout Optimization of Raft Groups for Heterogeneous distributed Key-Value Stores;Efficient Data Placement for Zoned Namespaces (ZNS) SSDs;SchedP: I/O-aware Job Scheduling in Large-Scale Production HPC systems;adaptive Low-Cost Loop Expansion for Modulo Scheduling;SpacKV: A Pmem-Aware Key-Value Separation Store Based on LSM-Tree;Consistent and Efficient Batch Operations for NoSQL databases with Hybrid Timestamp;SADD: A Novel Systolic Array Accelerator with Dynamic Dataflow for Sparse GEMM i

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GrAPL 2021 Keynote 1: Sparse Adjacency Matrices at the Core of Graph databases: GraphBLAS the Engine Behind RedisGraph Property Graph Database

GrAPL 2021 Keynote 1: Sparse Adjacency Matrices at the Core ...

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

Provides an abstract of the keynote presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.

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AITurbo: Unified Compute Allocation for Partial Predictable Training in Commodity Clusters 21

AITurbo: Unified Compute Allocation for Partial Predictable ...

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30th international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Zhao, Laiping Li, Fangshu Qu, Wenyu Zhan, Kunlin Zhang, Qingman Tianjin Univ Coll Intelligence & Comp CIC Tianjin Key Lab Adv Networking TANKLAB Tianjin Peoples R China Tianjin Univ TANKLAB CIC Tianjin Peoples R China 58 Com Beijing Peoples R China UNICLOUD Tianjin Peoples R China

ISBN: (纸本)9781450382175

As the scale and complexity of deep learning models continues to grow, model training is becoming an expensive job and only a small number of well-financed organizations can afford. Are the resources in commodity clusters well utilized for training? or how much potential space are still there for further improving the training efficiency in commodity clusters? is an urgent question to answer. In this paper, we review the processing of distributed learning training (DDL) in commodity GPU clusters and find that the current resource utilization is not only low but also imbalanced. We observe two features that can be exploited for further improving the training efficiency: partial predictable training and unified CPU-GPU training. Based on the observations, we present AITurbo, a novel resource scheduler that treats predictable and unpredictable jobs separately, but allocates heterogeneous CPU-GPU resource in a unified way. For predictable jobs, AITurbo designs a predicting model to estimate their performance under various heterogeneous resource allocations. For unpredictable jobs, it schedules them following the least-attained-service-first manner. AITurbo further designs a Borda-count based multi-level feedback queue method to combine them together. AITurbo demonstrates that there is still significant space for improving the training efficiency in commodity clusters. We evaluate AITurbo using jobs from Tensorflow benchmarks, which are submitted following the real trace of three production systems. Experimental results show that, compared with the state-of-the-art, AITurbo can reduce the average job completion time of DDL jobs by 3x.

关键词： Scheduling distributed deep learning partial predictable training heterogeneous resource

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HotQCD on Multi-GPU systems 38

HotQCD on Multi-GPU Systems

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38th international symposium on Lattice Field Theory, LATTICE 2021

作者： Altenkort, L. Bollweg, D. Clarke, D.A. Kaczmarek, O. Mazur, L. Schmidt, C. Scior, P. Shu, Hai-Tao Fakultät für Physik Universität Bielefeld Bielefeld Germany Paderborn Center for Parallel Computing Paderborn University Paderborn Germany Physics Department Brookhaven National Laboratory UptonNY United States Institut für Theoretische Physik Universität Regensburg Regensburg Germany

We present SIMULATeQCD, HotQCD's software for performing lattice QCD calculations on GPUs. Started in late 2017 and intended as a full replacement of the previous single GPU lattice QCD code used by the HotQCD collaboration, our software has been developed into an extensive framework for lattice QCD calculations distributed on multiple GPUs over many compute nodes. The code is built on C++, CUDA, and MPI and leverages modern C++ language features to provide high-level data structures, objects, and algorithms that allow users to express lattice QCD calculations in an intuitive way without sacrificing performance. Implemented algorithms range from gradient flow, correlator measurements, and mixed precision conjugate gradient solvers all the way to full HISQ gauge field configuration generation using RHMC. After successful deployment in large-scale computing projects, we want to share the result of our efforts with the lattice QCD community by making it publicly available. In these proceedings, we will present some of the key features of our code, demonstrate its ease of use, and show benchmarks of performance critical kernels on state-of-the-art supercomputers. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0)

关键词： Supercomputers

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parallel State Machine Replication from Generalized Consensus 39

Parallel State Machine Replication from Generalized Consensu...

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39th international symposium on Reliable distributed systems (SRDS)

作者： Ceolin Junior, Tarcisio Dotti, Fernando Pedone, Fernando Pontificia Univ Catolica Rio Grande do Sul Escola Politecn Porto Alegre RS Brazil Univ Fed Santa Maria Santa Maria RS Brazil Univ Svizzera Italiana Lugano Switzerland

ISBN: (纸本)9781728176260

State machine replication (SMR) is a established approach to building fault-tolerant services. In search for high SMR throughput, approaches that exploit semantic information in the ordering and execution of commands have emerged. Generalized consensus and parallel state machine replication are two representative examples, respectively. Although both approaches have been proved effective in isolation, no study in the literature has considered their integration. In this paper, we investigate the integration of generalized consensus and parallel SMR. We derive algorithms to parallelize the execution of commands based on the ordering of commands provided by consensus. As a prototype, we extended Egalitarian Paxos and conducted many experiments varying conflict rates, command computational costs, and number of cores at replicas. Compared to Egalitarian Paxos, the integrated approach (a) results in important throughput gains, as command independency and computational cost increase, and (b) converges to the same performance with high conflict rates or reduced number of cores.

关键词： State Machine Replication Generalized Consensus distributed Algorithms

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HRaft: Adaptive Erasure Coded Data Maintenance for Consensus in distributed Networks

HRaft: Adaptive Erasure Coded Data Maintenance for Consensus...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Yulei Jia Guangping Xu Chi Wan Sung Salwa Mostafa Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter UK

distributed data services usually rely on consensus protocols like Paxos and Raft to provide fault-tolerance and data consistency across global and local-distributed data centers. Erasure coding replication has appealing storage and network cost saving compared with full copy replication, which helps consensus protocols achieve low latency, high fault tolerance, and high throughput for data access. Applying erasure coding in consensus protocols directly will degrade the liveness level when the number of failure servers reaches a certain level. To address the challenge, CRaft just stores full copy replication instead of erasure coding replication when the number of failed servers reaches a certain threshold. In such situation, CRaft will be downgraded sharply to the same storage and network costs as Raft. To overcome the shortcoming of CRaft, we propose a protocol, called HRaft, which can adapt the placement of data blocks in order to always have enough blocks to recover the stored value when servers fail. By replenishing some coded blocks in healthy servers instead of full copy replication, it can avoid switching to the full replication when a certain threshold on the number of failures is reached. We designed and implemented a key-value (KV) storage prototype to validate the proposed protocol and evaluate its performance. The experimental results show HRaft can significantly reduce storage and network costs and improve write performance while keeping the liveness level compared to CRaft.

关键词： Fault tolerance Costs System performance Fault tolerant systems distributed databases Switches Throughput

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