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Conflictless Task Scheduling Concept 36th

Conflictless Task Scheduling Concept

引用

36th international conference on Information systems Architecture and Technology (ISAT)

作者： Smolinski, Mateusz Lodz Univ Technol Inst Informat Technol Wolczanska 215 PL-90924 Lodz Poland

ISBN: (纸本)9783319285559;9783319285535

this paper presents new concept of task scheduling without resource conflicts. the proposed task scheduling bases on conflict array, which was created using two binary resource identifiers as representation of task. those resource identifiers support fast conflict detection between tasks. Preparation of conflict array can be supported by modern GPU. Prepared schedule enable parallel tasks execution without resource conflicts. Elimination of resource conflicts provides deadlock free task processing. Proposed conflict free task scheduling can be used in local and distributed environments with high contention for limited resources. therefore tasks can be executed without other synchronization mechanisms.

关键词： Rigorous history Conflict free parallel task execution Resource conflict free task scheduling Resource conflict elimination with conflict array Deadlock avoidance GPU deadlock detection parallel programming

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Matrix representation of parallel computation for spiking neural P systems 11th

Matrix representation of parallel computation for spiking ne...

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11th international conference on Bio-inspired computing – theories and Applications, BIC-TA 2016

作者： Hu, Juan Chen, Guangchun Peng, Hong Wang, Jun Huang, Xiangnian Luo, Xiaohui School of Computer and Software Engineering Xihua University ChengduSichuan610039 China School of Electrical and Information Engineering Xihua University ChengduSichuan610039 China

ISBN: (纸本)9789811036101

Spiking neural P systems (in short, SN P systems) is a class of distributed parallel computing models. parallel computation of matrix operations has been supported on some new computing devices such as GPU, which provides a promising way to simulate the parallel computation of SN P systems. In this paper a matrix representation method of parallel computation for SN P systems is developed. In firing mechanism of SN P systems, the delay factor plays the role of controlling the receiving of spikes in neurons and the opportunity of emitting the spikes after the firing. In order to achieve the parallel computation of SN P systems, several matrices or vectors are introduced to decompose the firing mechanism of neurons. the parallel computation procedure of SN P systems can be achieved by the operations of the matrices or vectors. Two examples are used to illustrate the parallel computation procedure using the matrix operations. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Matrix algebra

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DASH: A C++ PGAS Library for distributed Data Structures and parallel Algorithms

DASH: A C++ PGAS Library for Distributed Data Structures and...

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IEEE international conference on High Performance computing and Communications

作者： Karl Fuerlinger Tobias Fuchs Roger Kowalewski Comput. Sci. Dept. Ludwig-Maximilians-Univ. (LMU) Munich Munich Germany

ISBN: (纸本)9781509042982

We present DASH, a C++ template library that offers distributed data structures and parallel algorithms and implements a compiler-free PGAS (partitioned global address space) approach. DASH offers many productivity and performance features such as global-view data structures, efficient support for the owner-computes model, flexible multidimensional data distribution schemes and inter-operability with STL (standard template library) algorithms. DASH also features a flexible representation of the parallel target machine and allows the exploitation of several hierarchically organized levels of locality through a concept of Teams. We evaluate DASH on a number of benchmark applications and we port a scientific proxy application using the MPI two-sided model to DASH. We find that DASH offers excellent productivity and performance and demonstrate scalability up to 9800 cores.

关键词： Structures parallel Algorithms

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More Effective Synchronization Scheme in ML using Stale Parameters

More Effective Synchronization Scheme in ML using Stale Para...

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IEEE international conference on High Performance computing and Communications

作者： Yabin Li Han Wan Bo Jiang Xiang Long Sch. of Comput. Sci. & Eng. Beihang Univ. Beijing China

ISBN: (纸本)9781509042982

In Machine learning (ML) the model we use is increasingly important, and the model's parameters, the key point of the ML, are adjusted through iteratively processing a training dataset until convergence. Although data-parallel ML systems often engage a perfect error tolerance when synchronizing the model parameters for maximizing parallelism, the synchronization of model parameters may delay in completion, a problem that generally gets worse at a large scale. this paper presents a Bounded Asynchronous parallel (BAP) model of computation that allows computations using stale model parameters in order to reduce synchronization overheads. In the meanwhile, our BAP model ensures theoretical convergence guarantees for large scale data-parallel ML applications. this model permits distributed workers to use the stale parameters storing in the local cache, instead of waiting until the Parameter Server (PS) produces a new version. this expressively reduces the time workers spend on waiting. Furthermore, the BAP model guarantees the convergence of ML algorithm by bounding the maximum distance of the stale parameters. Experiments conducted on 4 cluster nodes with up to 32 GPUs showed that our model significantly improved the proportion of computing time relative to the waiting time and led to 1.2-2 X speedup. Besides, we elaborated how to choose the staleness threshold when considering the tradeoff between Efficiency and Speed.

关键词： tradeoff distributed systems Bounded Asynchronous parallel Bulk Synchronous parallel Total Asynchronous parallel stale parameters

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A fast, Scalable SLINK Algorithm for Commodity Cluster computing Exploiting Spatial Locality

A fast, Scalable SLINK Algorithm for Commodity Cluster Compu...

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IEEE international conference on High Performance computing and Communications

作者： Poonam Goyal Sonal Kumari Sumit Sharma Dhruv Kumar Vivek Kishore Sundar Balasubramaniam Navneet Goyal Advanced Data Analytics & Parallel Technologies Laboratory Department of Computer Science & Information Systems BITS-Pilani Pilani Campus INDIA

ISBN: (纸本)9781509042982

Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm over traditional partitioning-based clustering as it does not require the number of clusters as input. But, due to its high time complexity and inherent data dependencies, it does not scale well for large datasets. To the best of our knowledge, all existing parallel SLINK algorithms are based on the traditional SLINK algorithm and thus require large number of computing resources. In this paper, we present a novel optimization of SLINK algorithm, GridSLINK, which is an order of magnitude faster than the existing state-of-the-art implementation. the optimization in GridSLINK comes from reduction in number of distance calculations required by SLINK. this reduction is achieved by exploiting spatial locality of data points and using an adaptive gridding technique. GridSLINK is parallelized for distributed memory systems. Scalable performance is achieved for increasing number of compute nodes. the proposed parallel algorithm, dGridSLINK, is benchmarked against the best existing parallel algorithm in literature and found to outperform the latter for all the real datasets considered. dGridSLINK can cluster millions of data points in few seconds/minutes using a small number of processing elements, without compromising the quality of clustering.

关键词： parallel computing multi-core multi-node clustering SLINK adaptive gridding

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Keynote Speech 3: the Era of Big Data and Cognitive computing

Keynote Speech 3: The Era of Big Data and Cognitive Computin...

引用

IEEE international conference on parallel and distributed computing, Applications and Technologies (PDCAT)

作者： Hui Lei International Business Machines Corp Armonk NY US

ISBN: (纸本)9781509050826

Summary form only given. Big data and cognitive computing are driving fundamental changes in technology, business, and society. With its exponentially increasing volume, velocity and variety, big data is hailed as the world's new natural resource. It promises to be for the 21st century what steam power was for the 18th, electricity for the 19th, and oil and gas for the 20th. the biggest value of big data lies in the deep, actionable insights that can be derived from the raw data and leveraged for competitive advantages. Cognitive computing is a key enabling technology for turning big data into insights. Different from the traditional programmable systems, cognitive systems are able to scale and extend human knowledge, reason with purpose, and learn and improve over time. the rise of cognitive systems represents the dawn of a new era of computing, whose focus has shifted from automating the world to understanding the world. In this talk, I will discuss the implications of big data and cognitive computing. I will reflect on our experience at IBM building the Watson Health Cloud, and discuss how big data and cognitive computing can come together to enable innovative health solutions that tackle many of the clinical, societal, and economic issues faced by today's health industry. I will present use cases, highlight the challenges, describe our approaches, and relate to client experiences as appropriate.

关键词： Big Data cognitive systems health care big data cognitive function Health Care bulk velocity

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HPGraph: A High parallel Graph Processing System Based on Flash Array

HPGraph: A High Parallel Graph Processing System Based on Fl...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Yuxuan Xing Ya Feng Songping Yu Zhengguo Chen Fang Liu Nong Xiao State Key Laboratory of High Performance Computing National University of Defense Technology NUDT Changsha China National University of Defense Technology Changsha Hunan CN

ISBN: (纸本)9781509042982

Large graphs analytics has been an important aspect of many big data applications, such as web search, social networks and recommendation systems. Many research focuses on processing large scale graphs using distributed system over past few years. And numbers of studies turn to construct graph processing system on a single server-class machine in consideration of cost, usability and maintainability. HPGraph is a high parallel graph processing system which adopts the edge-centric model, our contributions are as follows: (1) designing an efficient data allocation and access strategy for NUMA machine, and providing tasks scheduling to keep load balance, (2) raising a fine-grained edge-block filtering mechanism to avoid accessing unnecessary edge data, (3) constructing a high-speed flash array as the second storage. We made a detailed evaluation on a 16-core machine using asset of popular real word and synthetic data sets, and the results show that HPGraph always outperforms the state-of-the-art single machine graph processing systems-GridGraph. And HPGraph can achieve 1.27X faster than GridGraph for specific application. Our source code is available at https://***/xinghuan1990/HPGraph.

关键词： conferences High performance computing Smart cities Data science

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Implementing Molecular Dynamics Simulation on Sunway TaihuLight System

Implementing Molecular Dynamics Simulation on Sunway TaihuLi...

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IEEE international conference on High Performance computing and Communications

作者： Wenqian Dong Letian Kang Zhe Quan Kenli Li Keqin Li Ziyu Hao Xiang-Hui Xie College of Information Science and Engineering Hunan University Changsha Hunan 410082 China Department of Computer Science State University of New York New Paltz NY 12561 USA Jiangnan Institute of Computing Technology Wuxi Jiangsu 214000 China

ISBN: (纸本)9781509042982

Molecular dynamics (MD) simulation is a common tool to study the physical movements of atoms and molecules in many research fields. However, it is an extremely time-consuming application which takes researchers weeks or months to run a single simulation when simulation size scales up and computing demands keep growing. In this paper, an improved MD implementation on Sunway TaihuLight supercomputer is developed to solve the above mentioned issues. the new implementation is extended from an existing implementation (i.e., LAMMPS) which widely uses the MD application. Sunway TaihuLight is a heterogeneous supercomputer with a fully customized integration approach and a brand new many-core processor SW26010. the Sunway TaihuLight takes the world's first place with peak performance over 100PFlops. We propose an optimization method of the MD simulation in three steps: paralleled extensions to SW26010, memory-access optimizations, and vectorization. After the optimization process, an 8x speedup is achieved on a single computing node. Superiorly, we also scale up to 95 thousands computing nodes (6 millions cores) with an almost leaner speedup. Besides, the proposed methods can also be applied to many other molecular dynamics codes, or similar applications.

关键词： Sunway TaihuLignt Molecular Dynamics Simulation High performance computing parallel distributed computing

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A distributed Architecture for Rule Engine to Deal with Big Data

A Distributed Architecture for Rule Engine to Deal with Big ...

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international conference on Advanced Communication Technology

作者： Siyuan Zhu Hai Huang Lei Zhang School of Computer Science and Technology Beijing University and Telecommunications China

ISBN: (纸本)9781509016969

Rule engine, which acknowledges facts and draws conclusions by repeatedly matching facts with rules, is a good way of knowledge representation and inference. However, because of its low computational efficiency and the limitation of single machine's capacity, it cannot deal well with big data. As traditional MapReduce architecture can only address this problem in certain conditions, we have made some improvements and therefore proposed a distributed implementation of the rule engine using MapReduce-based architecture. It is designed to deal with a large amount of data in a parallel and distributed way by using a computing cluster that consists of multiple machines, on which certain part of the Rete algorithm would be operated. In the phase of splitting rules and the Rete-net, Apriori algorithm is also improved and adopted so as to gain a better system performance. this paper not only describes details of the design and its implementation, but also shows its high performance through several experiments.

关键词： rule engine big data rete algorithm map-reduce apriori algorithm

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parallel Particle Swarm Optimization for Reactive Power and Voltage Control Investigating Dependability 18

Parallel Particle Swarm Optimization for Reactive Power and ...

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2015 18th international conference on Intelligent System Application to Power systems (ISAP)

作者： Fukuyama, Yoshikazu Meiji Univ Sch Interdisciplinary Math Sci Dept Network Design Tokyo Japan

ISBN: (纸本)9781509001910

this paper presents a parallel particle swarm optimization (PSO) technique for reactive power and voltage control (Volt/Var Control: VVC) investigating dependability. High penetration of renewable energies and deregulation of power systems force power flow to change suddenly and operators in control centers have to control voltage in wider power systems. therefore, VVC is required to shorten the control interval and handle larger-scale power systems. One of the solutions for the problem is applications of parallel and distributed computing. Since power system is one of the infrastructures of social community, sustainable voltage control is crucial and dependability is strongly required for VVC. this paper investigates not only fast computation by parallel computation but also dependability of parallel PSO for VVC with IEEE 14, 30, 57, and 118 bus systems.

关键词： reactive power and voltage control parallel and distributed computing particle swarm optimization dependability

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