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Accelerating FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture

Accelerating FDTD simulation of microwave pulse coupling int...

引用

IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2015

作者： Wang, Qinglin Liu, Jie Cui, Xiantao Fu, Guitao Gong, Chunye Xing, Zuocheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China Beijing Satellite Navigation Center Beijing100094 China

ISBN: (纸本)9781467377881

The coupling of microwaves into apertures plays an important part in many electromagnetic physics and engineering fields. When the width of apertures is very small, Finite Difference Time Domain (FDTD) simulation of the coupling is very time-consuming. As a many-core architecture, the Intel's Many Integrated Core (MIC) architecture owns 512-bit vector units and more than 200 threads. In this paper, we parallelize FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture. In the implementation, the parallel programming model OpenMP is used to exploit thread parallelism while loop unrolling and SIMD intrinsic functions are utilized to accomplish vectorization. Compared with the serial version on Intel Xeon E5-2670 CPU, the implementation on the MIC coprocessor including 57 cores obtains a speedup of 11.57 times. The experiment results also demonstrate that the parallelization has good scalability in performance. Additionally, how binding relationship between OpenMP threads and hardware threads in MIC influences performance is also reported. © 2015 IEEE.

关键词： Scalability

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Madness: A multiresolution, adaptive numerical environment for scientific simulation

Madness: A multiresolution, adaptive numerical environment f...

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作者： Harrison, Robert J. Beylkin, Gregory Bischoff, Florian A. Calvin, Justus A. Fann, George I. Fosso-Tande, Jacob Galindo, Diego Hammond, Jeff R. Hartman-Baker, Rebecca Hill, Judith C. Jia, Jun Kottmann, Jakob S. Ou, M-J. Yvonne Pei, Junchen Ratcliff, Laura E. Reuter, Matthew G. Richie-Halford, Adam C. Romero, Nichols A. Sekino, Hideo Shelton, William A. Sundahl, Bryan E. Thornton, W. Scott Valeev, Edward F. Vázquez-Mayagoitia, Álvaro Vence, Nicholas Yanai, Takeshi Yokoi, Yukina Stony Brook University Stony BrookNY11794 United States University of Colorado at Boulder BoulderCO80309 United States Institut für Chemie Humboldt-Universität Zu Berlin Unter den Linden 6 Berlin10099 Germany Department of Chemistry Virginia Tech. BlacksburgVA24061 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Chemistry and Biochemistry Florida State University TallahasseeFL32306 United States Parallel Computing Lab Intel Corporation PortlandOR97219 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States LinkedIn Mountain ViewCA94043 United States Department of Mathematical Sciences University of Delaware NewarkDE19716 United States State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Department of Physics University of Washington SeattleWA98195 United States Computer Science and Engineering Toyohashi University of Technology ToyohashiAichi441-8580 Japan Louisiana State University Baton RougeLA70803 United States Department of Physics LaSierra University RiversideCA92505 United States Theoretical and Computational Molecular Science Institute for Molecular Science OkazakiAichi444-8585 Japan

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision that are based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics. © 2016 Society for Industrial and Applied Mathematics.

关键词： Multiresolution analysis

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Accelerating FDTD simulation of microwave pulse coupling into narrow slots on the Intel MIC architecture

Accelerating FDTD simulation of microwave pulse coupling int...

引用

IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： Qinglin Wang Jie Liu Xiantao Cui Guitao Fu Chunye Gong Zuocheng Xing School of Computer Science National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Beijing Satellite Navigation Center Beijing China

ISBN: (纸本)9781467377898

关键词： Microwave integrated circuits Finite difference methods Time-domain analysis Chlorine Hardware

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Location-Aware Multi-user Resource Allocation in distributed Clouds

Location-Aware Multi-user Resource Allocation in Distributed...

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10th Annual Conference of Advanced computer Architecture, ACA 2014

作者： Li, Jiaxin Li, Dongsheng Zheng, Jing Quan, Yong National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Information Center of Logistics Department Beijing China School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9783662444900

Resource allocation for multi-user across multiple data centers is an important problem in cloud computing environments. Many geographically-distributed users may request virtualized resources simultaneously. And the distances from users to allocated resources have much impact on the quality of service (QoS) in multiple data centers environment. Most existing methods do not take all these factors into account when allocating resources. They usually result in poor runtime performance of users' virtual computing environment and the remarkable difference of users' QoS. In this paper, we propose RAMD, a resource allocation algorithm based on multi-stage decision in multiple data centers. The RAMD algorithm allocate VMs to users, taking into account the correlation and interaction between multiple users, so as to minimize the sum of all users' service distances (i.e. determined by user location and network distance of virtual machines). Experimental results show that the algorithm can effectively deal with the cloud resource allocation for multi-user across multiple data centers. It can improve the runtime performance of users' virtualized resources and reduce the difference of QoS. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Resource allocation

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RAFlow: Read Ahead Accelerated I/O Flow through Multiple Virtual Layers

RAFlow: Read Ahead Accelerated I/O Flow through Multiple Vir...

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International Conference on Networking, Architecture, and Storage (NAS)

作者： Zhaoning Zhang Kui Wu Huiba Li Jinghua Feng Yuxing Peng Xicheng Lu National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China Computer Science Department University of Victoria B.C. Canada National Super Computing Center Tianjin China

Virtualization is the foundation for cloud computing, and the virtualization can not be achieved without software defined, elastic, flexible and scalable virtual layers. Unfortunately, if multiple virtual storage devices are chained together, the system may be subject to severe performance degradation. While the read-ahead (RA) mechanism in storage devices plays a very important role to improve I/O performance, RA may not be effective as expected for multiple virtualization layers, since it is originally designed for one layer only. When I/O requests are passed through a long I/O path, they may trigger a chain reaction and lead to unnecessary data transmission and thus bandwidth waste. In this paper, we study the dynamic behavior of RA through multiple I/O layers and demonstrate that if controlled well, RA can greatly accelerate I/O speed. We present RAFlow, a RA control mechanism, to effectively improve I/O performance by strategically expanding RA window at each layer. Our real-world experiments show that it can achieve 20% to 50% performance improvement in I/O paths with up to 8 virtualized storage devices.

关键词： Radio access networks Acceleration distributed databases Performance evaluation Synchronization Benchmark testing Degradation

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GPU accelerated three-stage execution model for event-parallel simulation 13

GPU accelerated three-stage execution model for event-parall...

引用

2013 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM-PADS 2013

作者： Li, Xiaosong Cai, Wentong Turner, Stephen John Parallel and Distributed Computing Center School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

ISBN: (纸本)9781450319201

This paper introduces the concept of event-parallel discrete event simulation (DES) and its corresponding implementation on the GPU platform. Inspired by the typical spatial-parallel DES and time-parallel DES, the event-parallel approach on GPU uses each thread to process one of the N events, where N is the total number of events. By taking advantage of the high parallelism of GPU threads, this approach achieves greater speedup. The GPU architecture is adopted in the execution of the event-parallel approach, so as to take advantage of the parallel processing capability provided by the massively large number of GPU threads. A three-stage execution model composing of generating events, sorting events and processing events in parallel is proposed. This execution model achieves good speedup. Compared with the event scheduling approach on CPU, we achieve up to 22.80 speedup in our case study. © 2013 ACM.

关键词： Graphics processing unit

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Local Resource Shaper for MapReduce

Local Resource Shaper for MapReduce

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IEEE International Conference on Cloud computing Technology and science (CloudCom)

作者： Peng Lu Young Choon Lee Vincent Gramoli Luke M. Leslie Albert Y. Zomaya Center for Distributed and High Performance Computing The University of Sydney Australia School of Information Technologies NICTA and the University of Sydney Australia Department of Computer Science University of Illinois at Urbana-Champaign USA

Resource capacity is often over provisioned to primarily deal with short periods of peak load. Shaping these peaks by shifting them to low utilization periods (valleys) is referred to as "resource consumption shaping". While originally aimed at the data center level, the resource consumption shaping we consider focuses on local resources, like CPU or I/O as we have identified that individual jobs also incur load peaks and valleys on these resources. In this paper, we present Local Resource Shaper (LRS), which limits fairness in resource sharing between co-located MapReduce tasks. LRS enables Hadoop to maximize resource utilization and minimize resource contention independently of job type. Co-located MapReduce tasks are often prone to resource contention (i.e., Load peak) due to similar resource usage patterns particularly with traditional fair resource sharing. In essence, LRS differentiates co-located tasks through active and passive slots that serve as containers for interchangeable map or reduce tasks. LRS lets an active slot consume as much resources as possible, and a passive slot make use of any unused resources. LRS leverages such slot differentiation with its new scheduler, Interleave. Our results show that LRS always outperforms the best static slot configuration with three Hadoop schedulers in terms of both resource utilization and performance.

关键词： Resource management Scheduling Benchmark testing Educational institutions Heart beat Containers Linux

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How file access patterns influence interference among cluster applications

How file access patterns influence interference among cluste...

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IEEE International Conference on Cluster computing

作者： Chih-Song Kuo Aamer Shah Akihiro Nomura Satoshi Matsuoka Felix Wolf Department of Computer Science RWTH Aachen University Aachen Germany German Research School for Simulation Sciences Laboratory for Parallel Programming Aachen Germany Tokyo Institute of Technology Global Scientific Information and Computing Center Tokyo Japan

On large-scale clusters, tens to hundreds of applications can simultaneously access a parallel file system, leading to contention and in its wake to degraded application performance. However, the degree of interference depends on the specific file access pattern. On the basis of synchronized time-slice profiles, we compare the interference potential of different file access patterns. We consider both micro-benchmarks, to study the effects of certain patterns in isolation, and realistic applications to gauge the severity of such interference under production conditions. In particular, we found that writing large files simultaneously with small files can slow down the latter at small chunk sizes but the former at larger chunk sizes. We further show that such effects can seriously affect the runtime of real applications-up to a factor of five in one instance. In the future, both our insights and profiling techniques can be used to automatically classify the interference potential between applications and to adjust scheduling decisions accordingly.

关键词： Interference Servers Throughput Benchmark testing Market research Degradation Aggregates

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A mathematical model for empowerment of Beowulf clusters for exascale computing

A mathematical model for empowerment of Beowulf clusters for...

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2013 11th International Conference on High Performance computing and Simulation, HPCS 2013

作者： Mirtaheri, Seyedeh Leili Khaneghah, Ehsan Mousavi Grandinetti, Lucio Sharifi, Mohsen Center of High Performance Computing for Parallel and Distributed Processing University of Calabria Rende Italy School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9781479908363

High-performance computing (HPC) clusters are currently faced with two major challenges - namely, the dynamic nature of new generation of applications and the heterogeneity of platforms - if they are going to be useful for exascale computing. Processes running these applications may well demand unpredictable requirements and changes to system configuration and capabilities at runtime, thereby requiring fast system response without sacrificing the transparency and integrity of the reconfigured empowered system that is running on a heterogeneous platform. While a challenge in and of itself, platform heterogeneity is both useful and instrumental in the handling of unpredictable requests. The realization of such a dynamically reconfigurable and heterogeneous HPC cluster system for exascale computing requires a model to guide running processes to determine if they need empowerment of the current cluster, and if yes, by how much. To show the feasibility of empowerment of traditional HPC clusters for exascale computing, we have selected Beowulf as a noble candidate cluster and present a mathematical model for the empowerment of Beowulf clusters for exascale computing (EBEC). We have developed the model in line with Beowulf's cluster approach and by using vector space algebra. In contrast to traditional hardware-oriented approaches to improvise the performance of clusters, we use a software approach to the development of the proposed model by emphasizing processes, which act as the creators of the cluster and thus should decide on system (re)configuration, as the principal building blocks of the system. We have also adopted a new approach to heterogeneity by considering heterogeneity at different levels including hardware, system software, application software, and system functionality. In addition to support for heterogeneity and dynamic reconfiguration, the proposed model includes support for scalability that is crucial to exascale computing too. © 2013 IEEE.

关键词： Vector spaces

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TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPERVISED HIGH-PERFORMANCE SPEECH SEPARATION

TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPER...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Xuejun Yang Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan 410073 China

ISBN: (纸本)9781479928941

Regarding the non-negativity property of the magnitude spectrogram of speech signals, nonnegative matrix factorization (NMF) has obtained promising performance for speech separation by independently learning a dictionary on the speech signals of each known speaker. However, traditional NM-F fails to represent the mixture signals accurately because the dictionaries for speakers are learned in the absence of mixture signals. In this paper, we propose a new transductive NMF algorithm (TNMF) to jointly learn a dictionary on both speech signals of each speaker and the mixture signals to be separated. Since TNMF learns a more descriptive dictionary by encoding the mixture signals than that learned by NMF, it significantly boosts the separation performance. Experiments results on a popular TIMIT dataset show that the proposed TNMF-based methods outperform traditional NMF-based methods for separating the monophonic mixtures of speech signals of known speakers.

关键词： Speech Dictionaries as Topic Loudspeakers NMF protocol signals mixtures factorization HIGH-PERFORMANCE

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