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Efficient Multi-Processor Scheduling in Increasingly Realistic Models (Brief Summary)

Efficient Multi-Processor Scheduling in Increasingly Realist...

引用

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

作者： Pál András Papp Georg Anegg Aikaterini Karanasiou Albert-Jan N. Yzelman Computing Systems Lab Huawei Zurich Research Center Zurich Switzerland

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

We study the problem of efficiently scheduling a computational DAG on multiple processors. While previous works have mostly studied this problem in rather simple models, we instead consider the well-established BSP model, and extend it with non-uniform memory access (NUMA) effects. This results in a notably more realistic model that captures communication costs, synchronization costs, and the hierarchical structure of modern computing architectures. We develop a range of algorithms to minimize the scheduling cost in this more complex setting: several initialization heuristics, a hill-climbing local search, and different approaches to solve the problem as an Integer Linear Program (ILP). We analyze our scheduling algorithms on a diverse set of real-world computational DAGs to show that they outperforms both academic and practical baselines: without NUMA effects, our scheduler finds solutions of 44%-51% smaller cost on average, and with NUMA effects, it achieves up to a factor 3× improvement. Finally, we develop a multilevel scheduling algorithm for the special case when the problem is dominated by very high communication costs.

关键词： distributed processing Costs Program processors Scheduling algorithms Computational modeling Heuristic algorithms Conferences

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Multi-Agent Reinforcement Learning for Edge Resource Management with Reconstructed Environment 19

Multi-Agent Reinforcement Learning for Edge Resource Managem...

引用

19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Miao, Weiwei Zeng, Zeng Zhang, Mingxuan Quan, Siping Zhang, Zhen Li, Shihao Zhang, Li Sun, Qi State Grid Jiangsu Elect Power CO LTD Informat & Telecommun Branch Nanjing Peoples R China State Grid Taizhou Power Supply Co Nanjing Peoples R China

ISBN: (纸本)9781665435741

In edge computing it is pivotal to automatically manage the resources to increase efficiency with limited resources. Deep reinforcement learning, which aims to maximize the long term cumulative reward, has recently been adopted in such scenarios. However, training the policy in real-world edge computing environment is challenging since arbitrary exploration in real world could drastically impair user utilities. In this paper, we propose a novel imitation learning approach to construct a virtual environment, in which the policy can be trained freely without additional costs. Under the virtual environment, we use a multi-agent reinforcement learning to manage the edge resources. Our method adopts a decentralized, sequential approach to deal with the uncertainties in the environment. Specifically, we decompose the target reward function into separated global part and local parts, where the global part is shared by all agents for cooperation, and the local parts are owned by each individual edge to accelerate the model learning. Extensive experimental results demonstrate that the constructed environment is very close to the real environment. In addition, the proposed multi agent reinforcement learning algorithm can converge very fast in the training phase and outperforms other state-of-art methods significantly in a variety of scenarios.

关键词： Edge computing Imitation Learning Multi-Agent Reinforcement Learning

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Exploration of Trade-offs Between General-Purpose and Specialized Processing Elements in HPC-Oriented CGRA

Exploration of Trade-offs Between General-Purpose and Specia...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Emanuele Del Sozzo Xinyuan Wang Boma Adhi Carlos Cortes Jason Anderson Kentaro Sano Center for Computational Science (R-CCS) RIKEN Kobe Japan Department of Electrical and Computer Engineering University of Toronto Toronto Canada

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Coarse-Grained Reconfigurable Arrays (CGRAs) are a class of reconfigurable accelerators traditionally used in embedded computing. Recently, CGRA-like devices have gained traction for HPC and AI acceleration; however, typical HPC and AI workloads often require operations that current CGRAs cannot implement, such as complex mathematical calculations. In this work, we present a broad architectural study exploring potential heterogeneous computational resources in CGRA architectures for HPC, which are not commonly considered in typical CGRA architecture research. We first improved the general-purpose Processing Element (PE) of a baseline CGRA to optimize computational resources and then developed a new specialized PE for mathematical functions commonly found in HPC applications. Finally, we evaluated multiple CGRA configurations concerning floorplan, size, general-purpose/specialized PE ratio, and Power, Performance, and Area (PPA) results from hardware synthesis.

关键词： Embedded computing distributed processing Computer architecture Hardware Artificial intelligence

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Scalable and Differentiable Simulator for Quantum Computational Chemistry

Scalable and Differentiable Simulator for Quantum Computatio...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Zhiqian Xu Honghui Shang Yi Fan Xiongzhi Zeng Yunquan Zhang Chu Guo Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Science and Technology of China Hefei China Hunan Normal University Changsha China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

We develop a high-performance simulator for variational quantum eigensolver (VQE), the major innovations include: (1) A differentiable matrix product state (MPS) based VQE simulator that seamlessly integrates MPS into the automatic differentiation framework, which overcomes the exponential memory growth of state-vector simulator and can efficiently calculate gradients with a cost independent of the number of parameters; (2) A dynamic scheme to distribute the gradient calculations to achieve good load balance; (3) A parallel adaptive VQE which integrates our differentiable MPS simulator to further enhance the simulation performance; (4) Study of real chemical systems with convergence to chemical accuracy using our simulator, achieving nearly linearly strong and weak scaling for chemical systems with up to 100 qubits. Our simulator provides an ideal test ground for VQE and paves the way of benchmarking large-scale VQE experiments on near-term quantum computers.

关键词： Computers Technological innovation Quantum algorithm Quantum chemistry High performance computing Qubit Memory management

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Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs

Shared-Memory Scalable <i>k</i>-Core Maintenance on Dynamic ...

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

作者： Gabert, Kasimir Pinar, Ali Catalyurek, Umit, V Georgia Inst Technol Atlanta GA 30332 USA Sandia Natl Labs Livermore CA USA

ISBN: (纸本)9781665435772

computing k-cores on graphs is an important graph mining target as it provides an efficient means of identifying a graph's dense and cohesive regions. computing k-cores on hypergraphs has seen recent interest, as many datasets naturally produce hypergraphs. Maintaining k-cores as the underlying data changes is important as graphs are large, growing, and continuously modified. In many practical applications, the graph updates are bursty, both with periods of significant activity and periods of relative calm. Existing maintenance algorithms fail to handle large bursts, and prior parallel approaches on both graphs and hypergraphs fail to scale as available cores increase. We address these problems by presenting two parallel and scalable fully-dynamic batch algorithms for maintaining k-cores on both graphs and hypergraphs. Both algorithms take advantage of the connection between k-cores and h-indices. One algorithm is well suited for large batches and the other for small. We provide the first algorithms that experimentally demonstrate scalability as the number of threads increase while sustaining high change rates in graphs and hypergraphs.

关键词： distributed processing Heuristic algorithms Scalability Conferences Maintenance engineering Approximation algorithms Pins

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Contract-based Service Fairness Guarantee in Vehicular Fog Networks

Contract-based Service Fairness Guarantee in Vehicular Fog N...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Weinan Liu Long Chen Jigang Wu Ming Tao School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In vehicular ad-hoc networks, vehicle-to-vehicle fog computing (VFC) can not only alleviate the computing delay of inference tasks from vehicles, but also reduce the computational overheads of RSUs. Existing studies on cooperative vehicle task computation offloading assume that RSUs can obtain global computing capability information of vehicles and the service-providing vehicles are always willing to offer services, while overlooking the privacy and selfishness of vehicles. Motivated by contract theory, we propose a joint service caching and task offloading NP-hard problem for vehicular fog computing, aiming to maximize the minimum service completion rate and to offer incentives for both service vehicles and RSUs. By designing contract-based joint service caching and task offloading algorithms, vehicles are encouraged to provide fog computing resources while protecting privacy. Extensive simulation results show that the proposed greedy algorithm CGA can improve the minimum service completion rate by over 10.8% and 14.7%, given fixed number of edge servers, when compared to a benchmark algorithm without contract, and a designed contract-based algorithm that maximizes the total throughput. Moreover, the designed contract-based approximation algorithm CRA can achieve the performance that are close to the benchmark algorithm without contract.

关键词： Privacy Simulation Vehicular ad hoc networks Benchmark testing Approximation algorithms Throughput Servers Resource management Contracts Edge computing

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Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data 35

Correlation-wise Smoothing: Lightweight Knowledge Extraction...

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

作者： Netti, Alessio Tafani, Daniele Ott, Michael Schulz, Martin Leibniz Supercomp Ctr Garching Germany Tech Univ Munich Garching Germany Fujitsu Enabling Software Technol GmbH Munich Germany

ISBN: (纸本)9781665440660

Modern High-Performance computing (HPC) and data center operators rely more and more on data analytics techniques to improve the efficiency and reliability of their operations. They employ models that ingest time-series monitoring sensor data and transform it into actionable knowledge for system tuning: a process known as Operational Data Analytics (ODA). However, monitoring data has a high dimensionality, is hardware-dependent and difficult to interpret. This, coupled with the strict requirements of ODA, makes most traditional data mining methods impractical and in turn renders this type of data cumbersome to process. Most current ODA solutions use ad-hoc processing methods that are not generic, are sensible to the sensors' features and are not fit for visualization. In this paper we propose a novel method, called Correlation-wise Smoothing (CS), to extract descriptive signatures from time-series monitoring data in a generic and lightweight way. Our CS method exploits correlations between data dimensions to form groups and produces image-like signatures that can be easily manipulated, visualized and compared. We evaluate the CS method on HPC-ODA, a collection of datasets that we release with this work, and show that it leads to the same performance as most state-of-the-art methods while producing signatures that are up to tell times smaller and up to ten times faster, while gaining visualizability, portability across systems and clear scaling properties.

关键词： High-performance computing Monitoring Operational data analytics Time-series analysis Compression

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UniFaaS: Programming across distributed Cyberinfrastructure with Federated Function Serving

UniFaaS: Programming across Distributed Cyberinfrastructure ...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Yifei Li Ryan Chard Yadu Babuji Kyle Chard Ian Foster Zhuozhao Li Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Data Science and Learning Division Argonne National Laboratory Lemont IL USA Dept. of Computer Science University of Chicago Chicago IL USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new approaches to programming, distributed execution, and function-level management. We present UniFaaS, a parallel programming framework that relies on a federated function-as-a-service (FaaS) model to enable composition of distributed, scalable, and high-performance scientific workflows, and to support fine-grained function-level management. UniFaaS provides a unified programming interface to compose dynamic task graphs with transparent wide-area data management. UniFaaS exploits an observe-predict-decide approach to efficiently map workflow tasks to target heterogeneous and dynamic resources. We propose a dynamic heterogeneity-aware scheduling algorithm that employs a delay mechanism and a re-scheduling mechanism to accommodate dynamic resource capacity. Our experiments show that UniFaaS can efficiently execute workflows across computing resources with minimal scheduling overhead. We show that UniFaaS can improve the performance of a real-world drug screening workflow by as much as 22.99% when employing an additional 19.48% of resources and a montage workflow by 54.41% when employing an additional 47.83% of resources across multiple distributed clusters, in contrast to using a single cluster.

关键词： Scheduling algorithms parallel programming Heuristic algorithms parallel processing Elasticity Dynamic scheduling Supercomputers

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MLF: Multi-level forwarding based on EdgeMesh in edge computing environment

MLF: Multi-level forwarding based on EdgeMesh in edge comput...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xin Wei Xin Li Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

KubeEdge is a widely used edge computing system. It ensures quality of service through multi-replica deployment, horizontal scaling, etc. EdgeMesh, provided by KubeEdge, can offer key functions such as edge service discovery, cross-subnet communication, load balancing, etc. However, the native load balancing policies provided by EdgeMesh do not consider the wide geographic distribution of edge nodes. It forwards user requests to each application replica on all nodes, leading to higher end-to-end latency and lower ***, we propose a multi-level forwarding (MLF) policy based on EdgeMesh. The MLF divides the edge nodes close to each other into the same cluster. Request forwarding within the same cluster is called intra-cluster forwarding while forwarding between different clusters is called extra-cluster forwarding. It prioritizes processing user requests locally, then considers forwarding the requests to another node to process in the intra-cluster, and finally selects the best extra-cluster node for forwarding and processing. Our experimental evaluation results show that MLF can effectively reduce the end-to-end latency and improve the application throughput in edge computing scenarios compared to EdgeMesh’s default load balancing policies.

关键词： Measurement Quality of service Bandwidth Load management Throughput Edge computing

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An Accurate Union Find Decoder for Quantum Error Correction on the Toric Code

An Accurate Union Find Decoder for Quantum Error Correction ...

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

作者： Federico Valentino Beatrice Branchini Davide Conficconi Donatella Sciuto Marco D. Santambrogio Dipartimento di Elettronica Informatica e Bioingegneria Politecnico di Milano Milan Italy

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Quantum computing represents an exciting computing paradigm that promises to solve problems untractable for a classical computer. One main limiting factor is the noise impacting qubits, which hinders the superpolynomial speedup promise. Thus, although Quantum Error Correction (QEC) mechanisms are paramount, QEC demands high speed and low latency to scale quantum computations. Within this context, hardware accelerators, such as FPGAs, represent a valuable approach to fulfilling the QEC requirements. Nevertheless, the literature falls short in proposing solutions optimized for the toric code, which is capable of encoding two logical qubits and, therefore, requires fewer qubits to correct errors. This manuscript presents QUEKUF, an FPGA-based QEC dataflow architecture dealing with toric code. QUEKUF disposes of parallel processing units to spatially parallelize QEC that a centralized controller orches-trates for data movement and operation decisions. Experimental results show that our design attains up to 20.14 × improvement in execution time over a C++ implementation while keeping a high accuracy, similar to the theoretical framework.

关键词： Codes Accuracy Qubit Quantum mechanics Computer architecture C++ languages parallel processing

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