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Analytic Clock Frequency Selection for Global DVFS

Analytic Clock Frequency Selection for Global DVFS

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Euromicro Conference on parallel, distributed and Network-Based processing

作者： Marco E.T. Gerards Johann L. Hurink Philip K.F. Hölzenspies Jan Kuper Gerard J.M. Smit Department of EEMCS University of Twente Enschede AE The Netherlands

Computers can reduce their power consumption by decreasing their speed using Dynamic Voltage and Frequency Scaling (DVFS). A form of DVFS for multicore processors is global DVFS, where the voltage and clock frequency is shared among all processor cores. Because global DVFS is efficient and cheap to implement, it is used in modern multicore processors like the IBM Power 7, ARM Cortex A9 and NVIDIA Tegra 2. This theory oriented paper discusses energy optimal DVFS algorithms for such processors. There are no known provably optimal algorithms that minimize the energy consumption of nontrivial real-time applications on a global DVFS system. Such algorithms only exist for single core systems, or for simpler application models. While many DVFS algorithms focus on tasks, this theoretical study is conceptually different and focuses on the amount of parallelism. We provide a transformation from a multicore problem to a single core problem, by using the amount of parallelism of an application. Then existing single core algorithms can be used to find the optimal solution. Furthermore, we extend an existing single core algorithm such that it takes static power into account.

关键词： Clocks Multicore processing Energy consumption Program processors Time-frequency analysis Real-time systems parallel processing

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Topology-Aware Optimization of Communications for parallel Matrix Multiplication on Hierarchical Heterogeneous HPC Platform

Topology-Aware Optimization of Communications for Parallel M...

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

作者： Tania Malik Vladimir Rychkov Alexey Lastovetsky Jean-Noël Quintin School of Computer Science and Informatics University College Dublin Dublin 4 Ireland Extreme Computing R&D Bull SAS Paris France

ISBN: (纸本)9781479941155

Communications on hierarchical heterogeneous HPC platforms can be optimized based on topology information. For MPI, as a major programming tool for such platforms, a number of topology-aware implementations of collective operations have been proposed for optimal scheduling of messages. This approach improves communication performance and does not require to modify application source code. However, it is applicable to collective operations only and does not affect the parts of the application that are based on point-to-point exchanges. In this paper, we address the problem of efficient execution of data-parallel applications on interconnected clusters and present a topology-aware optimization that improves data partition by taking into account the entire communication flow of the application. This approach is also non-intrusive to the source code but application-specific. For illustration, we use parallel matrix multiplication, where the matrices are partitioned into irregular 2D rectangles assigned to different processors and arranged in columns, and the processors communicate over this partitioning vertically and horizontally. By rearranging the rectangles, we can minimize communications between different levels of the network hierarchy. Finding the optimal arrangement is NP-complete, therefore, we propose a heuristic based on evaluation of the communication flow on the given topology. We demonstrate the correctness and efficiency of the proposed approach by experimental results on multicore nodes and interconnected heterogeneous clusters.

关键词： Program processors Network topology Partitioning algorithms Topology Optimization Clustering algorithms Algorithm design and analysis

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OpenMP Task Scheduling Analysis via OpenMP Runtime API and Tool Visualization

OpenMP Task Scheduling Analysis via OpenMP Runtime API and T...

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

作者： Ahmad Qawasmeh Abid M. Malik Barbara M. Chapman Department of Computer Science University of Houston Houston Texas

OpenMP tasks propose a new dimension of concurrency to cap irregular parallelism within applications. The addition of OpenMP tasks allows programmers to express concurrency at a high level of abstraction and makes the OpenMP runtime responsible about the burden of scheduling parallel execution. The ability to observe the performance of OpenMP task scheduling strategies portably across shared memory platforms has been a challenge due to the lack of performance interface standards in the runtime layer. In this paper, we exploit our proposed tasking extensions to the OpenMP Runtime API (ORA), Known as Collector APIs, for profiling task level parallelism. We describe the integration of these Collector APIs, implemented in the OpenUH compiler, into the TAU performance system. Our proposed task extensions are in line with the new interface specification called OMPT, which is currently under evaluation by the OpenMP community. We use this integration to analyze various OpenMP task scheduling strategies implemented in OpenUH. The capabilities of these scheduling strategies are evaluated with respect to exploiting data locality, maintaining load balance, and minimizing overhead costs. We present a comprehensive performance study of diverse OpenMP benchmarks, from the Barcelona OpenMP Test Suite, comparing different task pools (DEFAULT, SIMPLE, SIMPLE_2LEVEL, PUBLIC PRIVATE), task queues (DEQUE, FIFO, CFIFO, LIFO, INV_DEQUE), and task queue storages (ARRAY, DYN_ARRAY, LIST, LOCKLESS) on an AMD Opteron multicore system (48 cores total). Our results show that the benchmarks with similar characteristics exhibit the same behavior in terms of the performance of the applied scheduling strategies. Moreover, the task pool configuration, which controls the organization of task queues, was found to have the highest impact on performance.

关键词： Runtime Scheduling Benchmark testing parallel processing Standards Instruction sets Educational institutions

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Improving the Performance of Seismic Wave Simulations with Dynamic Load Balancing

Improving the Performance of Seismic Wave Simulations with D...

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Euromicro Conference on parallel, distributed and Network-Based processing

作者： Rafael Keller Tesser Laércio Lima Pilla Fabrice Dupros Philippe Olivier Alexandre Navaux Jean-François Méhaut Celso Mendes Instituto de Informática Universidade Federal do Rio Grande do Sul Brazil BRGM Orléans Cedex 2 France LIG — Laboratoire d'Informatique de Grenoble France National Center for Supercomputing Applications Urbana IL USA

Seismic wave models provide a way to study the consequences of future earthquakes. When modeling a restricted region, these models require a boundary condition to absorb the energy that goes out of the simulated domain. To parallelize these models, the domain is decomposed into a grid of smaller subdomains which are mapped to different tasks. Due to the boundary condition, this division gives rise to load imbalance between the tasks that simulate border regions and those assigned center subdomains. To deal with this imbalance, and therefore improve the simulation's performance, we propose the use of dynamic load balancing. To evaluate our solution, we ported a seismic wave simulator to Adaptive MPI to profit from its load balancing framework. By using dynamic load balancers, we improved the performance of the application by 23.85% when compared to the original MPI implementation. We also show that load balancers are able to adapt to the variation of load imbalance during the application's execution.

关键词： Load management Mathematical model Load modeling Equations Seismic waves Runtime Boundary conditions

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Supporting environmental modelling with Taverna workflows, web services and desktop grid technology 7

Supporting environmental modelling with Taverna workflows, w...

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7th international Congress on Environmental Modelling and Software, iEMSs 2014

作者： Horváth, Ferenc Ittzés, Péter Ittzés, Dóra Barcza, Zoltán Dobor, Laura Hidy, Dóra Marosi, Attila Hardisty, Alex Institute of Ecology and Botany Centre for Ecological Research Hungarian Academy of Sciences Alkotmány u. 2-4. Vácrátót2163 Hungary Department of Meteorology Eötvös Loránd University Pázmány sétány 1/A Budapest1117 Hungary Institute of Botany and Ecophysiology Szent Istvan University Páter K. u.1. Gödöllo2100 Hungary Laboratory of Parallel and Distributed Systems Institute for Computer Science and Control Hungarian Academy of Sciences P.O. Box 63 Budapest1518 Hungary School of Computer Science and Informatics Cardiff University Queen's Buildings 5 The Parade Roath CardiffCF24 3AA United Kingdom

Ecosystem functioning, climate change, and multiple interactions among biogeochemical cycles, climate system, site conditions and land use options are leading-edge topics in recent environmental modelling. Terrestrial ecosystem models are widely used to support carbon sequestration and ecosystem studies under various ecological circumstances. Our team uses the Biome-BGC model (Numerical Terradynamic Simulation Group, University of Montana), and develops an improved model version of it, called Biome-BGC MuSo. Both the original and the improved model estimate the ecosystem scale storage and fluxes of energy, carbon, nitrogen and water, controlled by various physical and biological processes on a daily time-scale. Web services were also developed and integrated with parallel processing desktop grid technology. Taverna workflow management system was used to build up and carry out elaborated workflows like seamless data flow to model simulation, Monte Carlo experiment, model sensitivity analysis, model-data fusion, estimation of ecosystem service indicators or extensive spatial modelling. Straightforward management of complex data analysis tasks, organized into appropriately documented, shared and reusable scientific workflows enables researchers to carry out detailed and scientifically challenging 'in silico' experiments and applications that could open new directions in ecosystem research and in a broader sense it supports progress in environmental modelling. The workflow approach built upon these web services allows even the most complicated computations to be initiated without the need of programming skills and deep understanding of model structure and initialization. The developments enable a wider array of scientists to perform ecosystem scale simulations, and to perform analyses not previously possible due to high complexity and computational demand.

关键词： Web services

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CLASSIFICATION OF LAnd COVER BASED ON DEEP BELIEF NETWORKS USING POLARIMETRIC RADARSAT-2 DATA

CLASSIFICATION OF LAND COVER BASED ON DEEP BELIEF NETWORKS U...

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IEEE international Geoscience and Remote Sensing symposium

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Baoliang Li National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

ISBN: (纸本)9781479953141

Urban land use and land cover (LULC) classification is one of the core applications in Geographic Information System (GIS). In this paper, a novel classification approach based on Deep Belief Network (DBN) for detailed urban mapping is proposed. Deep Belief Network (DBN) is a widely investigated and deployed deep learning model. By applying the DBN model, effective spatio-temporal mapping features can be automatically extracted to improve the classification performance. Six-date RADARSAT-2 Polarimetric SAR (PolSAR) data over the Great Toronto Area were used for evaluation. Experimental results showed that the proposed method can outperform SVM and contextual approaches using adaptive MRF.

关键词： Land cover classification Deep learning Restricted Boltzmann Machines (RBMs) Deep Belief Network (DBN) PolSAR

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Early Experience on the Blue Gene/Q Supercomputing System

Early Experience on the Blue Gene/Q Supercomputing System

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IEEE 27th international parallel and distributed processing symposium (IPDPS)

作者： Morozov, Vitali Kumaran, Kalyan Vishwanath, Venkatram Meng, Jiayuan Papka, Michael E. Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9780769549712

The Argonne Leadership Computing Facility (ALCF) is home to Mira, a 10 PF Blue Gene/Q (BG/Q) system. The BG/Q system is the third generation in Blue Gene architecture from IBM and like its predecessors combines system-on-chip technology with a proprietary interconnect (5-D torus). Each compute node has 16 augmented PowerPC A2 processor cores with support for simultaneous multithreading, 4-wide double precision SIMD, and different data prefetching mechanisms. Mira offers several new opportunities for tuning and scaling scientific applications. This paper discusses our early experience with a subset of micro-benchmarks, MPI benchmarks, and a variety of science and engineering applications running at ALCF. Both performance and power are studied and results on BG/Q is compared with its predecessor BG/P. Several lessons gleaned from tuning applications on the BG/Q architecture for better performance and scalability are shared.

关键词： IBM computers application program interfaces multi-threading parallel machines system-on-chip

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The DFrame: parallel programming using a distributed framework implemented in MPI.

The DFrame: Parallel programming using a distributed framewo...

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12th international symposium on distributed Computing and applications to Business, Engineering and Science (DCABES)

作者： Mclay, Tony Hoppe, Andreas Greenhill, Darrel R. Khaddaj, Souheil Univ Kingston Fac Comp Informat Syst & Math Kingston Upon Thames KT1 2EE Surrey England

ISBN: (纸本)9780769550602

High content throughput imaging systems must apply time consuming complex image processing algorithms to multiple bio-medical image streams. These systems are typically designed to use parallel resources in order to achieve results in reasonable time scales. This paper presents the design of a distributed framework that provides separation of the largely orthogonal parallelisation from the domain image processing algorithm development. This allows reuse and pluggable extension of parallelising patterns, as well as providing for extensibility of domain image processing. (Abstract)

关键词： component distributed computing mpi image processing

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Integrating Online Compression to Accelerate Large-Scale Data Analytics applications

Integrating Online Compression to Accelerate Large-Scale Dat...

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IEEE 27th international parallel and distributed processing symposium (IPDPS)

作者： Bicer, Tekin Yin, Jian Chiu, David Agrawal, Gagan Schuchardt, Karen Ohio State Univ Columbus OH 43210 USA Pacific Northwest Natl Labs Richland WA USA Washington State Univ Olympia WA 98504 USA

ISBN: (纸本)9780769549712

Compute cycles in high performance systems are increasing at a much faster pace than both storage and wide-area bandwidths. To continue improving the performance of large-scale data analytics applications, compression has therefore become promising approach. In this context, this paper makes the following contributions. First, we develop a new compression methodology, which exploits the similarities between spatial and/or temporal neighbors in a popular climate simulation dataset and enables high compression ratios and low decompression costs. Second, we develop a framework that can be used to incorporate a variety of compression and decompression algorithms. This framework also supports a simple API to allow integration with an existing application or data processing middleware. Once a compression algorithm is implemented, this framework automatically mechanizes multi-threaded retrieval, multi-threaded data decompression, and the use of informed prefetching and caching. By integrating this framework with a data-intensive middleware, we have applied our compression methodology and framework to three applications over two datasets, including the Global Cloud-Resolving Model (GCRM) climate dataset. We obtained an average compression ratio of 51.68%, and up to 53.27% improvement in execution time of data analysis applications by amortizing I/O time by moving compressed data.

关键词： application program interfaces cache storage climatology data analysis data compression geophysics computing information retrieval middleware multi-threading

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Pattern-Direct and Layout-Aware Replication Scheme for parallel I/O Systems

Pattern-Direct and Layout-Aware Replication Scheme for Paral...

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IEEE 27th international parallel and distributed processing symposium (IPDPS)

作者： Yin, Yanlong Li, Jibing He, Jun Sun, Xian-He Thakur, Rajeev IIT Dept Comp Sci Chicago IL 60616 USA Argonne Natl Lab Math & Comp Sci Div 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (纸本)9780769549712

The performance gap between computing power and the I/O system is ever increasing, and in the meantime more and more High Performance Computing (HPC) applications are becoming data intensive. This study describes an I/O data replication scheme, named Pattern-Direct and Layout-Aware (PDLA) data replication scheme, to alleviate this performance gap. The basic idea of PDLA is replicating identified data access pattern, and saving these reorganized replications with optimized data layouts based on access cost analysis. A runtime system is designed and developed to integrate the PDLA replication scheme and existing parallel I/O system;a prototype of PDLA is implemented under the MPICH2 and PVFS2 environments. Experimental results show that PDLA is effective in improving data access performance of parallel I/O systems.

关键词： parallel I/O I/O optimization data replication data reorganization data access pattern

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