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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

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Reducing computational cost in federated ensemble learning via rank-one matrix

Reducing computational cost in federated ensemble learning v...

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international symposium on Soft Computing and Intelligent systems (SCIS)

作者： YongHoon Kang HoSeung Kim Jee-Hyong Lee Department of Computer Science and Engineering Sungkyunkwan University Suwon Republic of Korea Department of Artificial Intelligence Sungkyunkwan University Suwon Republic of Korea

As distributed environments have developed and data privacy has become more important, federated learning attracts more attentions. Federated learning is the method to train deep learning models without data exchange in distributed environments. However, in a Non-Independent and Identically distributed data environment, performance degradation occurs in the federated learning environment. We want solve this problem by federated ensemble learning but computational cost problem occurs in edge devices. To solve this problem, we proposes FedRE. FedRE reduces computational cost on ensemble model via rank-one matrix and using shared weights to minimize performance degradation when Non-IID situations. As a result of the experiments, SVHN and CIFAR-10 image classification tasks showed high accuracy compared to the previous federated learning methods.

关键词： Performance evaluation Degradation Federated learning Computational modeling Image edge detection distributed databases Computational efficiency

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EDAML 2022 Invited Speaker 1: Application of Machine Learning in High Level Synthesis

EDAML 2022 Invited Speaker 1: Application of Machine Learnin...

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

作者： Ankush Sood Cadence Design Systems

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Traditionally high-level optimizations like CSA and sharing have been done technology independent. Doing word level optimizations PPA aware require accurate models for power, timing and area and that needs mapping to technology library and iterations which are runtime intensive and not suitable for multimillion instance designs. In this talk, we discuss how machine learning could help make the right power/area/delay tradeoffs early in the synthesis flow not sacrificing on turnaround time.

关键词： Optimization Machine learning distributed processing Conferences Timing Runtime Microelectronics

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Keynote Talk 1: Efficient DNN Training at Scale: from Algorithms to Hardware

Keynote Talk 1: Efficient DNN Training at Scale: from Algori...

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

作者： Gennady Pekhimenko University of Toronto

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The recent popularity of deep neural networks (DNNs) has generated a lot of research interest in performing DNN-related computation efficiently. However, the primary focus of systems research is usually quite narrow and limited to inference (i.e., how to efficiently execute already trained models) and image classification networks as the primary benchmark for evaluation. In this talk, we will demonstrate a holistic approach to DNN training acceleration and scalability starting from the algorithm, to software and hardware optimizations, to special development and optimization tools.

关键词： distributed processing Conferences Training Hardware

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CORtEX 2022 Invited Speaker 3: Neuromorphic computing: from modelling the brain to bio-inspired AI

CORtEX 2022 Invited Speaker 3: Neuromorphic computing: from ...

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

作者： Oliver Rhodes University of Manchester United Kingdom

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

This talk will introduce the field of neuromorphic computing: researching how to build machines to explore brain function; and using our enhanced understanding of the brain to build better computer hardware and algorithms. Specifically it will discuss spiking neural networks, including how they can be used to model neural circuits, and how these models can be harnessed to develop low-power bio-inspired AI systems.

关键词： Computational modeling Brain modeling Biological system modeling Artificial intelligence Neuromorphic engineering Integrated circuit modeling distributed processing

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Essentials of parallel Graph Analytics

Essentials of Parallel Graph Analytics

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

作者： Muhammad Osama Serban D. Porumbescu John D. Owens University of California Davis

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

We identify the graph data structure, frontiers, operators, an iterative loop structure, and convergence conditions as essential components of graph analytics systems based on the native-graph approach. Using these essential components, we propose an abstraction that captures all the significant programming models within graph analytics, such as bulk-synchronous, asynchronous, shared-memory, message-passing, and push vs. pull traversals. Finally, we demonstrate the power of our abstraction with an elegant modern C++ implementation of single-source shortest path and its required components.

关键词： distributed processing Analytical models Conferences C++ languages Programming Data structures Iterative methods

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LightDAG: A Low-latency DAG-based BFT Consensus through Lightweight Broadcast

LightDAG: A Low-latency DAG-based BFT Consensus through Ligh...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Xiaohai Dai Guanxiong Wang Jiang Xiao Zhengxuan Guo Rui Hao Xia Xie Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Computer Science and Technology Hainan University China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

To improve the throughput of Byzantine Fault Tolerance (BFT) consensus protocols, the Directed Acyclic Graph (DAG) topology has been introduced to parallel data processing, leading to the development of DAG-based BFT consensus. However, existing DAG-based works heavily rely on Reliable Broadcast (RBC) protocols for block broadcasting, which introduces significant latency due to the three communication steps involved in each RBC. For instance, DAGRider, a representative DAG-based protocol, exhibits the best latency of 12 steps, considerably higher than non-DAG protocols like PBFT, which only requires 3 steps. To tackle this issue, we propose LightDAG, which replaces RBC with lightweight broadcasting protocols such as Consistent Broadcast (CBC) and Plain Broadcast (PBC). Since CBC and PBC can be implemented in two and one communication steps, respectively, LightDAG achieves low *** our proposal, we present two variants of LightDAG, namely LightDAG1 and LightDAG2, each providing a trade-off between the best latency and the expected worst latency. In LightDAG1, every block is broadcast using CBC, which exhibits a best latency of 5 steps and an expected worst latency of 14 steps. Since CBC cannot guarantee the totality property, we design a block retrieval mechanism in LightDAG1 to assist replicas in retrieving missing blocks. LightDAG2 utilizes a combination of PBC and CBC for block broadcasting, resulting in the best latency of 4 steps and an expected worst latency of 12(t+1) steps, where t represents the number of actual Byzantine replicas. Since a Byzantine replica may equivocate through PBC, LightDAG2 prohibits blocks from directly referencing contradictory blocks. To ensure liveness, we propose a mechanism to identify and exclude Byzantine replicas if they engage in equivocation attacks. Extensive experiments have been conducted to evaluate LightDAG, and the results demonstrate its feasibility and efficiency.

关键词： Fault tolerance distributed processing Directed acyclic graph Fault tolerant systems Broadcasting Throughput Data processing

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Benefits of Optimistic parallel Discrete Event Simulation for Network-on-Chip Simulation

Benefits of Optimistic Parallel Discrete Event Simulation fo...

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

作者： Maximilian Bremer Nirmalendu Patra Tan Nguyen Dilip Vasudevan Cy Chan Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA USA

The end of Moore's law has placed a two-fold demand on hardware simulation. Firstly, efficient co-design requires fast simulation of hardware systems in order to vet proposed designs. Secondly, modern simulator platforms need to become increasingly concurrent as well. To address these challenges, we develop an optimistic time warp-based parallelization for the Structural Simulation Toolkit (SST). Our optimistic PDES engine hides synchronization costs by speculatively executing tasks, leading to better compute resource utilization on modern multicore architectures. Given the significant engineering effort to make custom existing hardware models reversible, we also develop a new SST component, called escher. The escher workflow instruments arbitrary SST applications and replays event traces through the different SST parallelizations. This is a useful tool for hardware engineers who want to understand the benefits of optimistic parallelization before undertaking the significant software engineering effort to make their hardware models reversible. We demonstrate this workflow by generating traces for a tiled mesh-NOC architecture and show a 2.1x to 3.7x speed-up using our optimistic SST parallelization versus the current conservative SST parallelization.

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Batch-Schedule-Execute: On Optimizing Concurrent Deterministic Scheduling for Blockchains

Batch-Schedule-Execute: On Optimizing Concurrent Determinist...

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Reliable distributed systems

作者： Yaron Hay Roy Friedman Technion Israel

ISBN: (数字)9798331530037

ISBN: (纸本)9798331530044

Executing smart contracts is a compute and storage-intensive task, which currently dominates modern blockchain's performance. Given that computers are becoming increasingly multicore, concurrency is an attractive approach to improve programs' execution runtime. A unique challenge of blockchains is that all replicas (miners or validators) must execute all smart contracts in the same logical order to maintain the semantics of State Machine Replication (SMR). In this work, we study the maximal level of parallelism attainable when focusing on the conflict graph between transactions packaged in the same block. This exposes a performance vulnerability that block creators may exploit against existing blockchain concurrency solutions, which rely on a total ordering phase for maintaining consistency amongst all replicas. To facilitate the formal aspects of our study, we develop a novel generic framework for Active State Machine Replication (ASMR) that is strictly serializable. We introduce the concept of graph scheduling and the definition of the minimal latency scheduling problem, which we prove to be NP-hard. We show that the restricted version of this problem for homogeneous transactions is equivalent to the classic Graph Vertex Coloring Problem, yet show that the heterogeneous case is more complex. We discuss the practical implications of these results.

关键词： Concurrent computing Computers Runtime Multicore processing Smart contracts Semantics Focusing parallel processing Blockchains Reliability

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parallel Bayesian Optimization for Optimal Scheduling of Underground Pumped Hydro-Energy Storage systems

Parallel Bayesian Optimization for Optimal Scheduling of Und...

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

作者： Maxime Gobert Jan Gmys Jean– Franç ois Toubeau Nouredine Melab Daniel Tuyttens Franç ois Vallé e Mathematics and Operational Research University of Mons Mons Belgium INRIA Lille - Nord Europe Universit&#x00E9 de Lille CNRS/CRIStAL Lille France Power Systems and Markets Research University of Mons Mons Belgium Universit&#x00E9 de Lille CNRS/CRIStAL INRIA Lille - Nord Europe Lille France

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Underground Pumped Hydro-Energy Storage stations are sustainable options to enhance storage capacity and thus the flexibility of energy systems. Efficient management of such units requires high-performance optimization algorithms able to find solutions in a very restricted timing to comply with the responsive energy markets. In this context, parallel computing offers a valuable solution to ensure appropriate decisions that maximize the profit of the station operator, while guaranteeing the safety of the energy network. This study investigates the use of three existing algorithms in parallel Bayesian Optimization, namely q-EGO, BSP-EGO and TuRBO. The three algorithms have different inherent behaviors in terms of parallel potential and, even though TuRBO scales better, q-EGO remains the best choice regarding the final outcomes for all investigated batch sizes and manages to get up to 5 times more profits than other approaches.

关键词： distributed processing Conferences Optimal scheduling parallel processing Bayes methods Timing Safety

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P-ckpt: Coordinated Prioritized Checkpointing

P-ckpt: Coordinated Prioritized Checkpointing

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international symposium on parallel and distributed Processing (IPDPS)

作者： Subhendu Behera Lipeng Wan Frank Mueller Matthew Wolf Scott Klasky North Carolina State University Oak Ridge National Laboratory

Good prediction accuracy and adequate lead time to failure are key to the success of failure-aware Check-point/Restart (C/R) models on current and future large-scale High-Performance Computing (HPC) systems. This paper develops a novel checkpointing technique, called p-ckpt, that aims to maintain the performance efficiency of failure-aware C/R models even when failures are predicted with a small lead time. The p-ckpt technique is developed for HPC systems with multi-level memory systems to prioritize checkpoints from vulnerable nodes (nodes with predicted failure) in the event of failure prediction. It applies coordination among the nodes within an application so that vulnerable nodes' checkpoint data is stored to the parallel File System (PFS) first by assigning priorities based on the lead time to failure. Vulnerable nodes thus have low-latency access on the critical path to the PFS before any failure happens. Further, we create the hybrid p-ckpt model by integrating Live Migration (LM) because of its cost-effectiveness and to reduce checkpoint frequency. Our hybrid p-ckpt C/R model considers prediction lead time and checkpoint latency to the PFS to decide on a feasible proactive action such as p-ckpt and LM. Simulations of six real-world applications for the Summit supercomputer show a ≈53-65% reduction in overhead due to the hybrid p-ckpt model compared to a ≈31-61% reduction in a state-of-the-art solution. We assess our C/R models against multiple failure distributions and consider lead time variability and failure prediction accuracy. Based on this evaluation and assessment, we discuss the trade-offs of using these models and their impact on application overhead.

关键词： Checkpointing Fault tolerance distributed processing File systems Computational modeling High performance computing Fault tolerant systems

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