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Scalable I/O-aware job scheduling for burst buffer enabled HPC clusters 16

Scalable I/O-aware job scheduling for burst buffer enabled H...

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25th ACM international Symposium on High-Performance parallel and distributed Computing, HPDC 2016

作者： Herbein, Stephen Ahn, Dong H. Lipari, Don Scogland, Thomas R.W. Stearman, Marc Grondona, Mark Garlick, Jim Springmeyer, Becky Taufer, Michela University of Delaware NewarkDE United States Lawrence Livermore National Laboratory LivermoreCA United States

ISBN: (纸本)9781450343145

The economics of flash vs. disk storage is driving HPC centers to incorporate faster solid-state burst buffers into the storage hierarchy in exchange for smaller parallel file system (PFS) bandwidth. In systems with an underprovisioned PFS, avoiding I/O contention at the PFS level will become crucial to achieving high computational efficiency. In this paper, we propose novel batch job scheduling techniques that reduce such contention by integrating I/O awareness into scheduling policies such as EASY backfilling. We model the available bandwidth of links between each level of the storage hierarchy (i.e., burst buffers, I/O network, and PFS), and our I/O-aware schedulers use this model to avoid contention at any level in the hierarchy. We integrate our approach into Flux, a next-generation resource and job management framework, and evaluate the effectiveness and computational costs of our I/O-aware scheduling. Our results show that by reducing I/O contention for underprovisioned PFSes, our solution reduces job performance variability by up to 33% and decreases I/O-related utilization losses by up to 21%, which ultimately increases the amount of science performed by scientific workloads. Copyright © 2016 by the Association for Computing Machinery, Inc. (ACM).

关键词： scheduling algorithms

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scheduling Matters: Area-Oriented Heuristic for resource management 28

Scheduling Matters: Area-Oriented Heuristic for Resource Man...

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28th IEEE international Symposium on Computer Architecture and High Performance Computing, SBAC-PAD 2016

作者： Benson, Jeremy Estrada, Trilce Rosenberg, Arnold L. Taufer, Michela University of New Mexico AlbuquerqueNM United States Northeastern University BostonMA United States University of Delaware NewarkDE United States

ISBN: (纸本)9781509061082

parallel and distributed systems that provide compute resources on demand are convenient, cost-effective, and becoming increasingly common. Boosting workload performance in such environments through scheduling has been of great interest, as users and providers aim to increase parallelism and reduce execution times. For modern data centers, leaving a smaller carbon footprint while maintaining high performance and low cost is becoming the next big challenge. With this in mind, we analyze the relative impacts on resource utilization of three well-motivated platform-oblivious scheduling heuristics. We simulate over 50,000 DAG workflow executions and measure performance, cost, and resource utilization under the three scheduling heuristics. Our results provide insights to better enable high-performance execution of workflows and advanced capacity planning while increasing resource utilization and reducing costs. © 2016 IEEE.

关键词： Carbon footprint

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31st international Conference on High Performance Computing, ISC High Performance 2016

31st International Conference on High Performance Computing,...

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31st international Conference on High Performance Computing, ISC High Performance 2016

ISBN: (纸本)9783319413204

The proceedings contain 25 papers. The special focus in this conference is on Thread Mapping, Decomposition, Data Locality, Scalable Applications, Machine Learning and Communication Runtime. The topics include: An analytical model-based auto-tuning framework for locality-aware loop scheduling;performance, design, and autotuning of batched GEMM for GPUS;a multi-objective hardware thread mapping unit for HPC clusters;dynamic sparse-matrix allocation on GPUS;an efficient parallel load-balancing framework for orthogonal decomposition of geometrical data;parallel community detection algorithm using a data partitioning strategy with pairwise subdomain duplication;high-level programming abstractions for data locality management;scalable ab-initio molecular dynamics with diverse capabilities;a fast parallel SNP detection pipeline;predictive modeling for job power consumption in HPC systems;towards machine learning on the automata processor;automatic multi-objective modeling with machine learning;supercomputing centers and electricity service providers;a geographically distributed perspective on demand management in Europe and the united states;resource management for running HPC applications in container clouds;mitigating MPI message matching misery;infiniband network analysis and monitoring with MPI;comparing runtime systems with exascale ambitions using the parallel research kernels;high order seismic simulations on the Intel Xeon phi processor;leveraging a cluster-booster architecture for brain-scale simulations;efficient and predictable group communication for manycore NOCS;distributed job allocation for large-scale manycores;many-core acceleration of a discrete ordinates transport mini-app at extreme scale and efficiency of high order spectral element methods on petascale architectures.

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Milky way analysis through a science gateway: Workflows and resource monitoring 8

Milky way analysis through a science gateway: Workflows and ...

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8th international workshop on Science Gateways, IWSG 2016

作者： Sciacca, Eva Vitello, Fabio Becciani, Ugo Costa, Alessandro Hajnal, Akos Kacsuk, Peter Molinari, Sergio Di Giorgio, Anna Maria Schisano, Eugenio Liu, Scige John Elia, Davide Cavuoti, Stefano Riccio, Giuseppe Bresciax, Massimo INAF-Osservatorio Astrofisico di Catania Italy Laboratory of Parallel and Distributed Systems SZTAKI Budapest Hungary INAF-Istituto di Astrofisica e Planetologia Spaziali Roma Italy INAF-Osservatorio Astronomico di Capodimonte Napoli Italy

This paper presents the latest developments on the VIALACTEA Science Gateway in the context of the FP7 VIALACTEA project. This science gateway operates as a central workbench for the VIALACTEA community in order to allow astronomers to process the new-generation (from Infrared to Radio) surveys of the Galactic Plane to build and deliver a quantitative 3D model of our Milky Way Galaxy. The final model will be used as a template for external galaxies to study star formation across the cosmic time. The adopted AGILE software development process allowed to fulfill the community needs in terms of required workflows and underlying resources monitoring. The scientific requirements arose during the process highlighted the needs for easy parameter setting, fully embarrassingly parallel computations and large-scale input dataset processing. Therefore the science gateway based on the WS-PGRADE/gUSE framework has been able to fulfill the requirements mainly exploiting the parameter sweep paradigm and parallel jobs execution of the workflow management system. Moving from the development to the production environment an efficient resource monitoring system has been implemented to easily analyse and debug sources of failure due to workflows computations. The results of the resource monitoring system are exploitable not only for IT experts administrators and workflow developers but also for the final users of the gateway. The affiliation to the STARnet Gateway Federation ensures the sustainability of the presented products after the end of the project, allowing the usage of VIALACTEA Science Gateway to all the stakeholders and not only to the community members.

关键词： Software design

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Automated sensor registration, binding and sensor data provisioning

Automated sensor registration, binding and sensor data provi...

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CAiSE Forum, CAiSE-Forum 2016, at the 28th international Conference on Advanced Information systems Engineering, CAiSE 2016

作者： Hirmer, Pascal Wieland, Matthias Breitenbücher, Uwe Mitschang, Bernhard Institute of Parallel and Distributed Systems University of Stuttgart Universitätsstr. 38 Stuttgart Germany Institute of Architecture of Application Systems University of Stuttgart Universitätsstr. 38 Stuttgart Germany

Today, the Internet of Things has evolved due to an increasing interconnection of technical devices. However, the automated binding and management of things and sensors is still a major issue. In this paper, we present a method and system architecture for sensor registration, binding, and sensor data provisioning. This approach enables automated sensor integration and data processing by accessing the sensors and provisioning the data. Furthermore, the registration of new sensors is done in an automated way to avoid a complex, tedious manual registration. We enable (i) semantic description of sensors and things as well as their attributes using ontologies, (ii) the registration of sensors of a physical thing, (iii) a provisioning of sensor data using diferent data access paradigms, and (iv) dynamic sensor binding based on application requirements. We provide the resource management Platform as a prototypical implementation of the architecture and corresponding runtime measurements. Copyright © by the paper's authors. Copying permitted only for private and academic purposes.

关键词： Ontology

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Macaca: A Scalable and Energy-Efficient Platform for Coupling Cloud Computing with distributed Embedded Computing

Macaca: A Scalable and Energy-Efficient Platform for Couplin...

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

作者： Heng Zhang Chunliang Hao Yanjun Wu Mingshu Li Chinese Academy of Science Institute of Software Beijing China

ISBN: (纸本)9781509036837

Microservers (embedded devices) with built-in sensors and network connectivity have become increasingly pervasive and their computational capabilities continue to be improved. Many works present that a heterogeneous cluster with the low-power microservers and high-performance nodes can provide competitive performance efficiency. However, they make simple modifications in existing distributed systems for adaptation, which have been proven not to fully exploit various the heterogeneous resources. In this paper, we argue that such heterogeneous cluster also calls for flexible and efficient computational resource scheduling. We then introduce Macaca, a platform for sharing and scheduling the distributed resources from embedded devices and Linux servers including computational resources, scale-out storages, and various data to accomplish collaborative processing tasks. In Macaca, we propose a two-layer scheduling mechanism to enhance utilization of these heterogeneous resources. Internally, the resource abstraction layer supports various sophisticated schedulers of existing distributed frameworks and decides how many resources to offer computing frameworks, while resource management layer is constructed for overall coordination of computational effectiveness and energy management for devices. Furthermore, Macaca adopts a novel strategy to support smart switch in three system models for energy-saving effectiveness. We evaluate Macaca by a variety of datasets and typical datacenter workloads, and the result shows that Macaca can achieve more efficient utilization of resources when sharing the heterogeneous cluster among diverse frameworks.

关键词： Servers resource management Switches Benchmark testing Processor scheduling Big data Random access memory

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Controlling the Memory Subscription of distributed Applications with a Task-Based Runtime System

Controlling the Memory Subscription of Distributed Applicati...

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

作者： Marc Sergent David Goudin Samuel Thibault Olivier Aumage Inria Bordeaux CEA/CESTA University of Bordeaux

ISBN: (纸本)9781509036837

The ever-increasing supercomputer architectural complexity emphasizes the need for high-level parallel programming paradigms. Among such paradigms, task-based programming manages to abstract away much of the architecture complexity while efficiently meeting the performance challenge, even at large scale. Dynamic run-time systems are typically used to execute task-based applications, to schedule computation resource usage and memory allocations. While computation scheduling has been well studied, the dynamic management of memory resource subscription inside such run-times has however been little explored. This paper studies the cooperation between a task-based distributed application code and a run-time system engine to control the memory subscription levels throughout the execution. We show that the task paradigm allows to control the memory footprint of the application by throttling the task submission flow rate, striking a compromise between the performance benefits of anticipative task submission and the resulting memory consumption. We illustrate the benefits of our contribution on a compressed dense linear algebra distributed application.

关键词： Runtime Memory management Dynamic scheduling Processor scheduling Linear algebra Engines

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An Optimized RM Algorithm by Task Affinity on Multi-Core Processor

An Optimized RM Algorithm by Task Affinity on Multi-Core Pro...

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international Conference on parallel and distributed systems (ICPADS)

作者： Ying Li Jianwei Niu Jiong Zhang Mohammed Atiquzzaman Xiang Long State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science University of Oklahoma Norman OK USA

ISBN: (纸本)9781509053827

scheduling of real-time tasks on a multi-core processor is challenging due to the execution time being a nondeterministic value. Through studying the relationship between task affinity and execution time, we propose an accelerated multi-core real-time scheduling algorithm (RM-λ) for periodic and dependent real-time tasks on a homogeneous multi-core processor based on acceleration between tasks to obtain a real-time scheduling scheme with less resource utilization. We adopt an acceleration factor matrix to represent the degree of affinity and develop a real-time scheduling model to find the best accelerated pair. The heterogeneous multi-core architectures can execute tasks by sharing their dependent data on L1 Cache. The results demonstrate our approach can loosens the schedulability constraints of RM (maximum improvement of 25%) so that an un-schedulable real-time tasks set on a single-core processor might be schedulable, and for those still hard to be scheduled, could be made schedulable on a multi-core processor.

关键词： Real-time systems Acceleration scheduling Multicore processing scheduling algorithms resource management

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Hybrid Heuristics for Mapping Task Problem on Large Scale Heterogeneous Platforms

Hybrid Heuristics for Mapping Task Problem on Large Scale He...

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

作者： Ania Kaci Huy-Nam Nguyen Amir Nakib Patrick Siarry MVS service (Modélisation Vérification Systèmes) Bull SAS Les Clayes-Sous-Bois France Laboratoire Images Signaux et Systèmes Intelligents (LiSSi) Université de Paris-Est Créteil Vitry-sur-Seine France

ISBN: (纸本)9781509036837

Task allocation problem is one of most studied topic in the field of parallel computing. With the emergence of the large scale platforms, scheduling applications on these large heterogeneous parallel systems is a challenging task due to the large number of mapping possibilities. Indeed, different methods to compute the weights of nodes and edges when scheduling directed acyclic graphs onto heterogeneous platforms may lead to significant variations in the generated schedule, in other terms the problem is ill posed. In this work, a new task mapping using a hybrid clustering method based on a branch and bound is proposed to regularize the problem. The obtained results show that the proposed formulation allows to provide a robust solution in most cases.

关键词： Program processors Processor scheduling Optimal scheduling scheduling resource management Schedules Computational modeling

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An Online Auction for Deadline-Aware Dynamic Cloud resource Provisioning

An Online Auction for Deadline-Aware Dynamic Cloud Resource ...

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international Conference on parallel and distributed systems (ICPADS)

作者： Kai He Chuanhe Huang Zongpeng Li Aiwu Shi Jiaoli Shi School of Mathematics and Computer Science Wuhan University China Wuhan

ISBN: (纸本)9781509053827

Auction mechanisms have recently been studied as an efficient approach for dynamic resource allocation in a cloud market. Existing mechanisms are mostly limited to the offline setting or execute jobs in continuous time slots. This work focuses on a practical case of online auction design, where users bid for future cloud resources for executing their batch processing jobs with hard deadline constraints. We design an online primal-dual auction framework for Virtual Machine (VM) allocation with social welfare maximization, which is truthful, computationally efficient, and guarantees a small competitive ratio. We leverage the framework of post price auctions to design our online primal-dual algorithm, where a bid is accepted if its expected execution cost in future time slots is smaller than its bidding price. We interpret the dual variables as marginal prices per unit of resource, and iteratively update it according to the allocated amount of resource. Theoretical analysis and trace-driven simulation studies validate the efficacy of the online auction framework, including both its computational efficiency and economic efficiency.

关键词： Cloud computing resource management Algorithm design and analysis Batch production systems Dynamic scheduling Pricing Virtual machining

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