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Learning-based Dynamic Pinning of parallelized Applications in Many-Core systems 27

Learning-based Dynamic Pinning of Parallelized Applications ...

引用

27th Euromicro international Conference on parallel, distributed and Network-Based Processing (PDP)

作者： Chasparis, Georgios C. Janjic, Vladimir Rossbory, Michael Hammond, Kevin Software Competence Ctr Hagenberg GmbH Softwarepk 21 A-4232 Hagenberg Austria Univ St Andrews Sch Comp Sci St Andrews Fife Scotland

ISBN: (纸本)9781728116440

This paper introduces a learning-based framework for dynamic placement of threads of parallel applications to the cores of Non-Uniform Memory Access (NUMA) architectures. Adaptation takes place in two levels, where at the first level each thread independently decides on which group of cores (NUMA node) it will execute, and on the second level it decides to which particular core from the group it will be pinned. Naturally, these two adaptation levels run on different time-scales: a low-frequency switching for the NUMA-node adaptation, and a high-frequency switching for the CPU-node level adaptation. In addition, the learning dynamics have been designed to handle measurement noise and rapid variations in the performance of the threads. The advantage of the proposed learning scheme is the ability to easily incorporate any multi-objective criterion and easily adapt to performance variations during runtime. Our objective is to demonstrate that this framework is appropriate for supervising parallel processes and intervening with respect to better resource allocation. Under the multi-objective criterion of maximizing total completed instructions per second (i.e., both computational and memory-access instructions), we compare the performance of the proposed scheme with the Linux operating system scheduler. We have observed that performance improvement could be significant especially under limited availability of resources and under irregular memory-access patterns.

关键词： Instruction sets Optimization Switches Computer architecture scheduling Current measurement resource management

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Preemptive Multi-Queue Fair Queuing 19

Preemptive Multi-Queue Fair Queuing

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28th international Symposium on High-Performance parallel and distributed Computing (ACM HPDC)

作者： Zhao, Yong Suo, Kun Wu, Xiaofeng Rao, Jia Wu, Song Jin, Hai Univ Texas Arlington Arlington TX 76019 USA Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol & Syst Lab Cluster & Grid Comp LabSch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781450366700

Fair queuing (FQ) algorithms have been widely adopted in computer systems to share resources among multiple users. Modern operating systems and hypervisors use variants of FQ algorithms to implement the critical OS resource management - the thread scheduler. While the existing FQ algorithms enforce fair CPU allocation on a per-core basis, there lacks an algorithm to fairly allocate CPU on multiple cores. This common deficiency in state-of-the-art multicore schedulers causes unfair CPU allocations to parallel programs using blocking synchronization, leading to severe performance degradation. parallel threads that frequently block due to synchronization exhibit deceptive idleness and are penalized by the thread scheduler. To this end, we propose a preemptive multi-queue fair queuing (P-MQFQ) algorithm that uses a centralized queue to fairly dispatch threads from different programs based on their received CPU bandwidth from multiple cores. We demonstrate that P-MQFQ can be approximated by augmenting the existing load balancing in the OS without requiring to implement the centralized queue or undermining scalability. We implement P-MQFQ in Linux and Xen, respectively, and show significantly improved utilization and performance for parallel programs.

关键词： Performance scheduling Multicore Cloud Computing

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Proceedings of the 48th international Conference on parallel Processing, ICPP 2019

Proceedings of the 48th International Conference on Parallel...

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48th international Conference on parallel Processing, ICPP 2019

ISBN: (纸本)9781450362955

The proceedings contain 106 papers. The topics discussed include: a parallel graph algorithm for detecting mesh singularities in distributed memory ice sheet simulations;machine learning for fine-grained hardware prefetcher control;AVR: reducing memory traffic with approximate value reconstruction;incorporating probabilistic optimizations for resource provisioning of data processing workflows;stage delay scheduling: speeding up DAG-style data analytics jobs with resource interleaving;unleashing the scalability potential of power-constrained data center in the microservice era;dynamic load balancing in hybrid switching data center networks with converters;when power oversubscription meets traffic flood attack: re-thinking data center peak load management;and HyperPRAW: architecture-aware hypergraph restreaming partition to improve performance of parallel applications running on high performance computing systems.

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TOSS: A Topology-based Scheduler for Storm C1usters

TOSS: A Topology-based Scheduler for Storm C1usters

引用

IEEE international Symposium on parallel and distributed Processing workshops and Phd Forum (IPDPSW)

作者： Yi Zhou Yangyang Liu Chaowei Zhang Xiaopu Peng Xiao Oin TSYS School of Computer Science Columbus State University 4225 University Avenue GA Department of Computer Science and Software Engineering Auburn University Auburn AL USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Nowadays, our world is undergoing profound challenges in processing a massive amount of data. A handful of computation technologies emerge as a promising computation platform for data intensive processing. Apache Storm is an outstanding open-source platform for largescale streaming computation, which is widely used in the industry (eg., Twitter). Performance bottleneck problems encountered in streaming data applications motivate us to investigate scheduling issues in Storm. A key aspect of tuning Storm performance is to decide how to deploy components of a storm application among all available nodes in a cluster. Driven by our observations, we design and implement a new scheduling strategy called TOSS based on application structures. Compared to the existing round-robin scheduler, TOSS not only judiciously handles tight-bind components, but also balances workloads by introducing a self-tuning mechanism in the deployment stage. We conduct experiments by applying two popular and distinct topologies to evaluate the performance of TOSS. The experimental results suggest that TOSS significantly boost the performance of the round-robin scheduler. ln particular, TOSS substantially improves the system throughput of Storm while shortens latency of Storm applications.

关键词： Storms Topology Processor scheduling Real-time systems resource management Throughput Task analysis

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Heuristic Contention-Free scheduling Algorithm for Multi-core Processor using LET Model

Heuristic Contention-Free Scheduling Algorithm for Multi-cor...

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international workshop/Symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Shingo Igarashi Tasuku Ishigooka Tatsuya Horiguchi Ryotaro Koike Takuya Azumi Graduate School of Science and Engineering Saitama University Center for Technology Innovation - Controls Research and Development Group Hitachi Ltd.

ISBN: (数字)9781728173436

ISBN: (纸本)9781728173443

Embedded systems, e.g., self-driving systems and advanced driver-assistance systems (ADAS), require computing platforms with high computing power and low power consumption. Multi-/many-core platforms satisfy these requirements effectively. However, for hard real-time applications, multiple demands on shared resources can impede real-time performance, and memory is one resource that can impair the desired performance significantly. Therefore, it is important that memory access timing be deterministic to facilitate predictability. To realize this, the Logical Execution Time (LET) paradigm is currently attracting attention. This paper proposes a theoretical scheduling method for a model applying the LET paradigm to directed acyclic graph (DAG) nodes for a multi-/many-core platform. The proposed method considers communication timing between nodes and generates a schedule that does not cause communication contentions. In addition, the proposed method attempts to distribute tasks and reduce LET intervals to address increased execution times due to the implementation of the LET paradigm. In the evaluation, we observed that the proposed method improved the schedule length by up to 40%.

关键词： Task analysis Integrated circuits Computational modeling Timing Schedules Memory management Data models

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Elan: Towards Generic and Efficient Elastic Training for Deep Learning

Elan: Towards Generic and Efficient Elastic Training for Dee...

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international Conference on distributed Computing systems

作者： Lei Xie Jidong Zhai Baodong Wu Yuanbo Wang Xingcheng Zhang Peng Sun Shengen Yan Tsinghua University Sensetime Beijing China

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

Showing a promising future in improving resource utilization and accelerating training, elastic deep learning training has been attracting more and more attention recently. Nevertheless, existing approaches to provide elasticity have certain limitations. They either fail to fully explore the parallelism of deep learning training when scaling out or lack an efficient mechanism to replicate training states among different devices. To address these limitations, we design Elan, a generic and efficient elastic training system for deep learning. In Elan, we propose a novel hybrid scaling mechanism to make a good trade-off between training efficiency and model performance when exploring more parallelism. We exploit the topology of underlying devices to perform concurrent and IO-free training state replication. To avoid the high overhead of start and initialization, we further propose an asynchronous coordination mechanism. Powered by the above innovations, Elan can provide high-performance (~1s) migration, scaling in and scaling out support with negligible runtime overhead (0/ 00 ). For elastic training of ResNet-50 on ImageNet, Elan improves the time to solution by 20%. For elastic scheduling, with the help of Elan, resource utilization is improved by 21%+ and job pending time is reduced by 43%+.

关键词： Training Deep learning Performance evaluation Runtime parallel processing Topology resource management

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16th international Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2019

16th International Conference on the Economics of Grids, Clo...

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16th international Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2019

ISBN: (纸本)9783030360269

The proceedings contain 26 papers. The special focus in this conference is on Economics of Grids, Clouds, systems, and Services. The topics include: Sensing as a Service Revisited: A Property Rights Enforcement and Pricing Model for IIoT Data Marketplaces;dominant Business Model Patterns of Regional IaaS Providers – An Exploratory Multiple-Case Study;SEConomy: A Framework for the Economic Assessment of Cybersecurity;Introducing Licensing Throughout SLAs in NFV Environment;blockchain-Enabled Participatory Incentives for Crowdsourced Mesh Networks;BUNKER: A Blockchain-based trUsted VNF pacKagE Repository;agent-Based Appliance scheduling for Energy management in Industry 4.0;leveraging Quality of Service and Cost in Cyber-Physical systems Design;conceptual Modeling for Corporate Social Responsibility: A Systematic Literature Review;GENEVIZ: A Visual Tool for the Construction and Blockchain-Based Validation of SFC Packages;Efficient Multi-resource, Multi-unit VCG Auction;cloud-Based Integrated Process Planning and scheduling Optimisation via Asynchronous Islands;stability Analysis of a Statistical Model for Cloud resource management;genetic Algorithms for Capacity Estimation in Pluralistic Spectrum Licensing Simulations;Towards a Roadmap for Cloud TV Services in the Internet of Things Era;meshDapp – Blockchain-Enabled Sustainable Business Models for Networks;modeling, Characterising and scheduling Applications in Kubernetes;a Smart Contract Based Recommender System;cost-Optimized parallel Computations Using Volatile Cloud resources;bill Estimation in Simplified Memory Progressive Second Price Auctions;voting for Superior Services: How to Exploit Cloud Hierarchies;energy-Aware Dynamic Pricing Model for Cloud Environments;architecture and Business Logic Specification for Dynamic Cloud Federations;Towards an Architecture Proposal for Federation of distributed DES Simulators.

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Characterizing the Performance of Executing Many-tasks on Summit

Characterizing the Performance of Executing Many-tasks on Su...

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IEEE/ACM Annual workshop on Emerging parallel and distributed Runtime systems and Middleware (IPDRM)

作者： Matteo Turilli Andre Merzky Thomas Naughton Wael Elwasif Shantenu Jha Rutgers University Oak Ridge National Laboratory Rutgers University Brookhaven National Laboratory

ISBN: (纸本)9781728159942

Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.

关键词： Religious Missions Workload management of resources TASK scheduling LAUNCHING resource utilization Lower bound

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Formalizing Gremlin pattern matching traversals in an integrated graph Algebra

Formalizing Gremlin pattern matching traversals in an integr...

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2019 Blockchain Enabled Semantic Web workshop and Contextualized Knowledge Graphs workshop, BlockSW-CKG 2019

作者： Thakkar, Harsh Auer, Sören Vidal, Maria-Esther University of Bonn Germany TIB and Leibniz University of Hannover Germany

Graph data management (also called NoSQL) has revealed beneficial characteristics in terms of flexibility and scalability by differently balancing between query expressivity and schema flexibility. This peculiar advantage has resulted into an unforeseen race of developing new task-specific graph systems, query languages and data models, such as property graphs, key-value, wide column, resource description framework (RDF), etc. Present-day graph query languages are focused towards flexible graph pattern matching (aka sub-graph matching), whereas graph computing frameworks aim towards providing fast parallel (distributed) execution of instructions. The consequence of this rapid growth in the variety of graph-based data management systems has resulted in a lack of standardization. Gremlin, a graph traversal language, and machine provide a common platform for supporting any graph computing system (such as an OLTP graph database or OLAP graph processors). In this extended report, we present a formalization of graph pattern matching for Gremlin queries. We also study, discuss and consolidate various existing graph algebra operators into an integrated graph algebra. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Information management

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scheduling Bag-of-Task-Chains in distributed systems

Scheduling Bag-of-Task-Chains in Distributed Systems

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international Symposium on Autonomous Decentralized systems (ISADAS)

作者： Georgios L. Stavrinides Helen D. Karatza Department of Informatics Aristotle University of Thessaloniki 54124 Thessaloniki Greece

ISBN: (数字)9781728116723

ISBN: (纸本)9781728116730

distributed resources required for processing complex parallel applications are becoming larger in scale day-by-day. They need to be used efficiently in order to provide Quality of Service (QoS). Therefore, resource allocation and scheduling are of paramount importance. In this paper we investigate issues involved with the scheduling of parallel jobs that are bag-of-task-chains in distributed systems. Two task-chain scheduling techniques in two different cases of resource allocation are studied. The performance of the task-chain scheduling algorithms is evaluated via simulation, under different cases of system workload. The experimental results show that the performance of the task-chain scheduling strategies depends on the employed resource allocation techniques.

关键词： Task analysis Routing Program processors Processor scheduling Probabilistic logic Time factors resource management

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