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Performance study of openMP and hybrid programming models on CPU–GPU cluster 5th

Performance study of openMP and hybrid programming models on...

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5th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2018

作者： Chandrashekhar, B.N. Sanjay, H.A. Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru560064 India

ISBN: (纸本)9789811360008

Optimizing complex code of scientific and engineering applications is a challenging area of research. there are many parallel and distributed programming frameworks which efficiently optimize the code for the performance. In this study, we did a comparison study of the performance of parallel computing models. We have used irregular graph algorithms such as Floyd’s algorithm (shortest path problems) and Kruskal’s algorithm (minimum spanning tree problems). We have considered OpenMP and hybrid [OpenMP + MPI] on CPU cluster and MPI + CUDA programming strategies on the GPU cluster to improve the performance on shared–distributed memory architecture by minimizing communication and computation overlap overhead between individual nodes. A single MPI process per node is used to launch small chunks of large irregular graph algorithm on various nodes on the cluster. CUDA is used to distribute the work between the different GPU cores within a cluster node. Results show that from the performance perspective GPU, implementation of graph algorithms is effective than the CPU implementation. Results also show that hybrid [MPI + CUDA] parallel programming framework for Floyd’s algorithm on GPU cluster yields an average speedup of 19.03 when compared to the OpenMP and a speedup of 15.96 is observed against CPU cluster with hybrid [MPI + OpenMP] frameworks. For Kruskal’s algorithm, average speedup of 27.26 is observed when compared against OpenMP and a speedup of 20.74 is observed against CPU’s cluster with hybrid [MPI + OpenMP] frameworks. © Springer Nature Singapore Pte Ltd 2019.

关键词： Graphics processing unit

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LHCnn: A novel efficient multivariate time series prediction framework utilizing convolutional neural networks 21

LHCnn: A novel efficient multivariate time series prediction...

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21st IEEE international conference on High Performance Computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Chengxi Li, Kenli Liu, Jing Chen, Cen College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781728120584

the Multivariate time series prediction problem has always been an important and hot issue in the human society. this problem has attracting a large number of researchers to conduct related research. However, the time series data in the real world is highly complex and the data size is large, which brings great inconvenience and challenge to the research work. the RNN-based model made some great progress on the traditional Sequence to Sequence problem such as NLP nowadays. But using the RNN-based model to predict the multivariate time series data still have some problems. For example, the time series data in the real world usually has a certain periodicity, the RNN-based model is hard to capture the global information. In this paper, we propose a CNN-based model called LHCnn(Low-High Cnn). LHCnn combines the CNN with the Attention mechanisms. the model uses multi-layer CNNs to extract the surface and the deep features of the data. then we combine these two kinds of features with the Attention mechanisms to produce the final prediction. We also train the model in parallel on multiple GPUs to accelerate the training process. In the final part of this paper, we discuss the effort of real-time series data in the experiment, and then proved that this model not only can ensure reliability and stability, but also can achieve higher accuracy. Analyzing working principle of many time series sequencing models and comparing them each other, this one is better than others right now. © 2019 IEEE.

关键词： Forecasting

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the GFPA Finger: parallel-Adaption Merged Robot Finger based on Gear-Flexible Belt Mechanism

The GFPA Finger: Parallel-Adaption Merged Robot Finger based...

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international conference on Advanced Robotics and Mechatronics (ICARM)

作者： Yixin Wang Ruitong He Wenzeng Zhang State Key Laboratory of Tribology the Key Laboratory for Advanced Materials Processing Technology (MOE) Tsinghua University Beijing China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

this paper proposes a novel design of an underactuated finger that integrating the advantages of two most frequently used grasping method: parallel-pinching method and self-adaptive method. the finger has an underactuated mechanism using gears, tension spring and flexible belt, GFPA finger. the designed gear transmission and flexible belt-tension spring in the fingers could let the finger grasping object in a merged movement of parallel pinching and self-adaptive, which not only has the advantages of parallel-pinching to grasp the shape of the plate, but also has the advantages of adaptive way to adapt to the shape of the object. Due to the special structure including gear mechanism and tension spring-flexible belt mechanism, the finger could firstly rotate the proximal phalanx while keep the distal phalanx in a parallel motion through the meshed series gear. the distal phalanx will rotate to envelope the object only after the proximal phalanx contact with the object. the GFPA finger has important application prospects in the field of humanoid, aeronautics and Astronautics, etc.

关键词： Gears thumb Shafts Belts Grasping Springs

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processing Millions of Single Cells by SHARP 20

Processing Millions of Single Cells by SHARP

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11th ACM conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Wan, Shibiao Kim, Junil Fan, Yiping Won, Kyoung Jae St Jude Childrens Res Hosp Ctr Appl Bioinformat 332 N Lauderdale St Memphis TN 38105 USA Univ Copenhagen Biotech Res & Innovat Ctr Copenhagen Denmark

ISBN: (纸本)9781450379649

Single-cell technologies have received extensive attention from bioinformatics and computational biology communities due to their evolutionary impacts on uncovering novel cell types and intra-population heterogeneity in various domains of biology and medicine. Recent advances on single-cell RNA-sequencing (scRNA-seq) technologies have enabled parallel transcriptomic profiling of millions of cells. However, existing scRNA-seq clustering methods are lack of scalability, time-consuming and prone to information loss during dimension reduction. To address these concerns, we present SHARP [1], an ensemble random projection-based algorithm which is scalable to clustering 10 million cells. By adopting a divide-and-conquer strategy, a sparse random projection and two-layer meta-clustering, SHARP has the following advantages: (1) hyper-faster than existing algorithms;(2) scalable to 10-million cells;(3) accurate in terms of clustering performance;(4) preserving cell-to-cell distance during dimension reduction;and (5) robust to dropouts in scRNA-seq data. Comprehensive benchmarking tests on 20 scRNA-seq datasets demonstrate SHARP remarkably outperforms state-of-the-art methods in terms of speed and accuracy. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering 10 million cells. With an avalanche of single cells in different tissues to be sequenced in multiple international projects like the Human Cell Atlas, we believe SHARP will serve as one of the useful and important tools for large-scale single-cell data analysis. Several potential future directions include while keeping the scalability and speed of SHARP, how to extend its functions into rare cell type detection and integrating single cell data from different platforms, experiments and conditions.

关键词： Single cell analysis 10 million cells random projection dimension reduction clustering

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Reliability and Modelability Advantages of Distributed Switching for Reconfigurable 2D Processor Arrays

Reliability and Modelability Advantages of Distributed Switc...

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international conference and Workshop on Computing and Communication (IEMCON)

作者： Behrooz Parhami University of California Santa Barbara CA USA

ISBN: (数字)9781728184166

ISBN: (纸本)9781728184173

Processor arrays have been used, either as the main computation engine or as special-purpose adjuncts, for a variety of applications requiring very high performance. As the size of such an array increases, the possibility of processor malfunctions, leading to loss of computational capabilities, can no longer be ignored. While many switching architectures and reconfiguration algorithms have been proposed for building processor arrays, modeling of their reliability has been inadequately addressed. In this paper, I study differences between 2D processor arrays with centralized and distributed switching, pointing to advantages of the latter in terms of reliability, regularity, modularity, and VLSI realizability. As important side results, I formulate the notions of reliability inversion (a less reliable system prevailing over a more reliable one due to modeling uncertainties) and modelability (the property of a system that makes it possible to derive tight reliability bounds, thus making reliability inversion less likely).

关键词： Uncertainty Systematics Two dimensional displays Switches parallel processing Very large scale integration Reliability engineering

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Mapping Exceptions to High-Level Source Code on a Heterogeneous Architecture 9

Mapping Exceptions to High-Level Source Code on a Heterogene...

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9th international conference on parallel architectures, algorithms and Programming (PAAP)

作者： Duncan, Ralph Gougol, Nima Frandeen, Jim LookingGlass Cyber Solut San Jose CA 95134 USA

ISBN: (纸本)9781538694039

Network packet processing architectures use heterogeneous processors as accelerators to speed-up classic application domain tasks. Our platform compiles applications to bytecodes for a generalized packet processing machine, then uses microcoded interpreters running in parallel to trigger accelerators as needed. To make the system effective requires helping users debug apps, which includes tracking runtime exceptions. Exception tracking is complicated when a system-thrown exception is detected on an accelerator and the current binary form is far removed from the original high-level language source or associated assembly code. We tackle this problem by (1) instrumenting the compiler and a low-level bytecode tool, (2) reporting exceptions with the interpreter, (3) creating a specialized tool to collate the higher-level program forms with the lower-level bytecode forms. this functionality provides data needed for post-mortem program analysis.

关键词： parallel processing exceptions accelerators debugging software tools packet processing

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Data Management Mechanisms for IoT: Architecture, Challenges and Solutions

Data Management Mechanisms for IoT: Architecture, Challenges...

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international Multidisciplinary conference on Computer and Energy Science (SpliTech)

作者： Bassirou Diène Ousmane Diallo Joel J. P. C. Rodrigues EL Hadji M. Ndoye Ciprian Teodorov University of Assane Seck Ziguinchor Senegal Federal University of Piauí (UFPI) Teresina PI Brazil Instituto de Telecomunicações Portugal Lab-STICC UMR CNRS 6285 ENSTA Bretagne France

ISBN: (数字)9789532901054

ISBN: (纸本)9781728173634

the current traditional database management mechanisms and analytics architectures are not generally suitable for addressing the intrinsic characteristics of diversity, heterogeneity, large-scale, dynamic and large amount of data generated by the Internet of things (IoT) networks and various IoT applications needs. then, it is challenging to provide efficient IoT data storage and query processing mechanisms for meeting the requirements of applications. this paper identifies the most relevant characteristics and mechanisms of IoT data management and categorizes them. Moreover, a new four layers fog-based architecture using the distributed approach is proposed for providing a secure storage infrastructure and efficient real-time IoT data discovering with better latency. Finally, this work shows advances on IoT data management mechanisms, highlights their advantages and limits, and discusses the challenging open research issues that need to be focused for providing guidelines for new contributions.

关键词： Query processing Memory Distributed databases Real-time systems Internet of things Secure storage Guidelines

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High-efficiency Numerical Simulation of Electromagnetic Response of Complex Geoelectric Structures Based on GPU parallel Symplectic Algorithm 5

High-efficiency Numerical Simulation of Electromagnetic Resp...

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5th IEEE international conference on Computational Electromagnetics, ICCEM 2019

作者： Lei, Jianwei Fang, Hongyuan College of Water Conservancy Environmental Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781538671115

For forward modeling of large size and fine structures, the numerical accuracy and computational efficiency are not high due to the stability conditions and dense grid number. In this paper, the symplectic Euler algorithm, surface conformal technique and GPU acceleration technique were combined to establish a precise and high-efficiency numerical model of electromagnetic wave propagation in complex geoelectric structures. And we realized the refined and efficient calculation of the electromagnetic response of arbitrary shape underground target. the results show that the accuracy and efficiency of ground penetrating radar (GPR) forward modeling are greatly improved, which provides a theoretical basis for accurately interpreting GPR detection data, and provides an accurate and efficient forward modeling for the inversion imaging. © 2019 IEEE.

关键词： Graphics processing unit

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Analysis and Implementation of a parallel Computing Cluster for Solving Computational Problems in Data Analytics

Analysis and Implementation of a Parallel Computing Cluster ...

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IEEE international conference on Computing, Communication and Security (ICCCS)

作者： Chavala Mutyala Rao K. Shyamala University College of Engineering Osmania University Hyderabad

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

As computers get updated by a couple of generations, they get disposed of as useless but when a large number of such computers are put together and they share each other's computational power, they can get really powerfulcluster defines as a parallel computing system which is inter connected with a group of independent computers working togeather as a single coherent system. Clusters uses message passing interface for communication, and code snippets have to be explicitly programmed to run on underlying hardware. More computing nodes to be added to the cluster based on the need. While designing parallel cluster in many cases we opt for open source technologies and Linux system components which reduce software costs. parallel Cluster is a way of connecting similar computers together and can get a task done by sharing the task load. the task is shared in coordination of the master and slave nodes in a cluster. parallel computing clusters allows group of computers with different hardware configurations, to work together to provide solutions to different computational problems. the resources like memory, storage and CPU cores in a cluster computing environment are integrated into one system. this paper is to set up a cluster environment for academic purposes and provide a huge computation power to run a program in parallel mode this will reduce the time taken to execute a program. A parallel code is always better then sequential code in terms of time taken to run.

关键词： Rocks Servers Task analysis parallel processing Linux Hardware Performance evaluation

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High-Performance Computation of LGCA Fluid Dynamics on an FPGA-Based Platform

High-Performance Computation of LGCA Fluid Dynamics on an FP...

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international conference on Computers, Communications, and Systems (ICCCS)

作者： Changdao Du Iman Firmansyah Yoshiki Yamaguchi University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161372

Lattice Gas Cellular Automata (LGCA) simulations are typical High-Performance Computing (HPC) applications commonly used to simulate fluid flows. Due to the computational locality and discretization of LGCA, these simulations can achieve high performance by using parallel computing devices like GPUs or multi-core CPUs. Nevertheless, many studies also have shown that state-of-the-art Field Programmable Gate Arrays (FPGAs) have enormous parallel computing potential and power-efficient for high-performance computations. In this paper, we present an FPGA-based fluid simulation architecture design for the LGCA method. Our design exploits both temporal and spatial parallelism inside the LGCA algorithm to scale up the performance on FPGA. We also propose an application-specific cache structure to overcome the memory bandwidth bottleneck. Furthermore, our development process is based on the High-Level Synthesis (HLS) approach that increases productivity. Experimental results on a Xilinx Vcu 1525 FPGA show that our design is able to achieve 17130.2 Million Lattice Updates Per Second (MLUPS).

关键词： Lattices Field programmable gate arrays parallel processing Computer architecture Computational modeling Mathematical model Bandwidth

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