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检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4332 条记录，以下是1221-1230 订阅

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Leveraging compute clusters for large-scale parametric screens of reaction-diffusion systems 26

Leveraging compute clusters for large-scale parametric scree...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Karim, Md. Shahriar Othmer, Hans G. Umulis, David M. Purdue Univ Dept Ag & Biol Engn W Lafayette IN 47906 USA Univ Minnesota Sch Math Minneapolis MN 55455 USA Purdue Univ Weldon Sch Biomed Engn Dept Ag & Biol Engn W Lafayette IN 47906 USA

ISBN: (纸本)9781538649756

Reaction-diffusion (RD) models are widely used to study the spatio-temporal evolution of pattern formation during development. Nonlinear RD models are often analytically intractable, and require numerical solution methods. Interrogation of RD models for a large physiological range of parameters covers many orders of magnitude, establishing situations where solutions are stiff and solvers fail to provide accurate results to the time-dependent problem. The spatial dependence of these parameters, and the nonlinearity of the underlying dynamics, impose additional challenges. We developed an efficient approach for simulating stiff RD models of pattern formation and we used supercomputer clusters to carry out a large screen of spatially varying parameters. The proposed approach generated data for screening of RD systems within a reasonable amount of time (a few days), which scales down further if additional cluster nodes are available. The approaches outlined herein are applicable to any systems biology problem requiring numerical approximation of RD equations with spatially non-uniform properties and stiff nonlinear reactions.

关键词： Reaction-diffusion models Systems biology Cluster-computing In-silico data Large-scale screens

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Cynthia: Cost-Efficient Cloud Resource Provisioning for Predictable distributed Deep Neural network Training 19

Cynthia: Cost-Efficient Cloud Resource Provisioning for Pred...

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48th International conference on parallel processing (ICPP)

作者： Zheng, Haoyue Xu, Fei Chen, Li Zhou, Zhi Liu, Fangming East China Normal Univ Dept Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Univ Louisiana Lafayette Dept Comp Sci Lafayette LA 70504 USA Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781450362955

It becomes an increasingly popular trend for deep neural networks with large-scale datasets to be trained in a distributed manner in the cloud. However, widely known as resource-intensive and time-consuming, distributed deep neural network (DDNN) training suffers from unpredictable performance in the cloud, due to the intricate factors of resource bottleneck, heterogeneity and the imbalance of computation and communication which eventually cause severe resource under-utilization. In this paper, we propose Cynthia, a cost-efficient cloud resource provisioning framework to provide predictable DDNN training performance and reduce the training budget. To explicitly explore the resource bottleneck and heterogeneity, Cynthia predicts the DDNN training time by leveraging a lightweight analytical performance model based on the resource consumption of workers and parameter servers. With an accurate performance prediction, Cynthia is able to optimally provision the cost-efficient cloud instances to jointly guarantee the training performance and minimize the training budget. We implement Cynthia on top of Kubernetes by launching a 56-docker cluster to train four representative DNN models. Extensive prototype experiments on Amazon EC2 demonstrate that Cynthia can provide predictable training performance while reducing the monetary cost for DDNN workloads by up to 50.6%, in comparison to state-of-the-art resource provisioning strategies, yet with acceptable runtime overhead.

关键词： cloud resource provisioning deep neural network training predictable performance resource bottleneck resource heterogeneity

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ParallNormal: an efficient variant calling pipeline for unmatched sequencing data 26

ParallNormal: an efficient variant calling pipeline for unma...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Follia, Laura Tordini, Fabio Pernice, Simone Romano, Greta Piaggeschi, Giulia Beatrice Ferrero, Giulio Univ Turin Comp Sci Dept Turin Italy Ctr Expt Res & Med Studies CeRMS Turin Italy Univ Turin Mol Biotechnol & Hlth Sci Dept Turin Italy Italian Inst Genom Med Turin Italy Univ Turin Clin & Biol Sci Dept Turin Italy

ISBN: (纸本)9781538649756

Nowadays, next generation sequencing is closer to clinical application in the field of oncology. Indeed, it allows the identification of tumor-specific mutations acquired during cancer development, progression and resistance to therapy. In parallel with an evolving sequencing technology, novel computational approaches are needed to cope with the requirement of a rapid processing of sequencing data into a list of clinically-relevant genomic variants. Since sequencing data from both tumors and their matched normal samples are not always available (unmatched data), there is a need of a computational pipeline leading to variants calling in unmatched data. Despite the presence of many accurate and precise variant calling algorithms, an efficient approach is still lacking. Here, we propose a parallel pipeline (ParallNormal) designed to efficiently identify genomic variants from whole-exome sequencing data, in absence of their matched normal. ParallNormal integrates well-known algorithms such as BWA and GATK, a novel tool for duplicate removal (DuplicateRemove), and the FreeBayes variant calling algorithm. A re-engineered implementation of FreeBayes, optimized for execution on modern multi-core architectures is also proposed. ParallNormal was applied on whole-exome sequencing data of pancreatic cancer samples without considering their matched normal. The robustness of ParallNormal was tested using results of the same dataset analyzed using matched normal samples and considering genes involved in pancreatic carcinogenesis. Our pipeline was able to confirm most of the variants identified using matched normal data.

关键词： Genomics Sequential analysis Bioinformatics Pipelines Tools Tumors Bayes methods

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Evaluation of parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology 26

Evaluation of Parallel Tempering to Accelerate Bayesian Para...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Gupta, Sanjana Hainsworth, Liam Hogg, Justin S. Lee, Robin E. C. Faeder, James R. Univ Pittsburgh Sch Med Dept Computat & Syst Biol Pittsburgh PA 15260 USA

ISBN: (纸本)9781538649756

Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://***/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.

关键词： Bayesian parameter estimation Systems biology parallel tempering Rule-based modeling

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Developing and Using a Geometric Multigrid, Unstructured Grid Mini-Application to Assess Many-Core Architectures 26

Developing and Using a Geometric Multigrid, Unstructured Gri...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Owenson, A. M. B. Wright, S. A. Bunt, R. A. Jarvis, S. A. Ho, Y. K. Street, M. J. Univ Warwick Dept Comp Sci Coventry W Midlands England Rolls Royce Plc Design Syst Engn Derby England

ISBN: (纸本)9781538649756

Achieving high-performance of large scientific codes is a difficult task. This has led to the development of numerous mini-applications that are more tractable to analyse, while retaining performance characteristics of their full-sized counterparts. These "mini-apps" also enable faster hardware evaluation, and for sensitive codes allow evaluation of systems outside of access approval processes. In this paper we develop a mini-application of a geometric multigrid, unstructured grid Computational Fluid Dynamics (CFD) code, designed to exhibit similar performance characteristics without sharing code. We detail our experiences developing this application, using guidelines detailed in existing research, and contribute further additions to these to aid future mini-application developers. Our application is validated against the inviscid flux routine of HYDRA, a CFD code developed by Rolls-Royce, which confirms that the parent kernel and mini-application share fundamental causes of parallel inefficiency. We then use the mini-application to assess the impact of Intel's Knights Landing (KNL) on performance. We find that the mini-app and parent kernel continue to share scaling characteristics, however a comparison with Broadwell performance exposed significant differences between the kernels that were undetected by the validation.

关键词： scientific computing computational fluid dynamics performance analysis high performance computing

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CSSMT: Compiler based Software Simultaneous Multithreading (SMT) 26

CSSMT: Compiler based Software Simultaneous Multithreading (...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Chen, Yuanfang Shi, Qingchuan Li, Xiaoming Univ Delaware Elect & Comp Engn Dept Newark DE 19716 USA Adv Micro Devices Inc Santa Cruz CA USA

ISBN: (纸本)9781538649756

Simultaneous multithreading (SMT) is a unique computer architecture feature to increase the pipeline utilization and therefore, increase the instruction throughput. It improves instruction level throughput by simultaneously filling both vertical and horizontal super-scalar pipeline slots that are left unfilled by native threads. So far SMT has been implemented in hardware. However, hardware SMT implementations have its limitations. First, it is complex and expensive to implement-only higher-end processors are equipped with it even though lower-end processors have same pipeline design as the higher-end variants and might also benefit from it. SMT also introduces great power/energy and area overheads. Moreover, being a hardware feature, SMT is limited by the range and depth of instruction analysis that it can afford at execution time, therefore it is unlikely to benefit from high-level software knowledge about instruction mix and might lose many improvement opportunities. In this paper, we address the limitation of the hardware-based SMT and introduce CSSMT: Compiler based Software Simultaneous Multithreading (SMT). The main contribution of CSSMT is that it exploits high-level program profiles to purposefully "re-mix" instructions from multiple programs to better fill vertical and horizontal super-scalar pipeline slots so that the overall throughput is improved. Furthermore, CSSMT is a software-transformation technique that enables SMT at software level during compilation time. Therefore, it can help overcome the limitation of the hardware-based SMT implementation and is more portable. We test CSSMT with programs from SPEC2006 and NAS benchmarks and achieve up to 12% speedup of execution time (30.7% improvement in terms of multi-program throughput).

关键词： Compiler LLVM Simultaneous multithreading SMT

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Performance Evaluation of the Metadata-driven MASi Research Data Management Repository Service 26

Performance Evaluation of the Metadata-driven MASi Research ...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Grunzke, Richard Mueller-Pfefferkorn, Ralph Nagel, Wolfgang E. Hartmann, Volker Jejkal, Thomas Kollai, Helen Herold, Hendrik Meinel, Gotthard Dressler, Christiane Dolhoff, Julia Schrade, Torsten Stanek, Julia Hoffmann, Alexander Herres-Pawlis, Sonja Tech Univ Dresden Ctr Informat Serv & High Performance Comp Dresden Germany Karlsruhe Inst Technol Inst Data Proc & Elect Karlsruhe Germany Inst Ecol & Reg Dev Monitoring Settlement & Open Space Dev Dresden Germany Akad Wissensch & Literatur Mainz Digitale Akad Mainz Germany Rheinisch Westfal Tech Hsch Aachen Inst Anorgan Chem Aachen Germany

ISBN: (纸本)9781538649756

Research data is increasingly important in order to gain insights from scientific data. To optimally foster this, the management of research data is required to be usable, customizable and fast. We enable this by building up the MASi research data management repository service, based on the KIT DM framework. The aim is on utilizing a single repository instance to serve multiple arbitrary community use cases. Due to their diverse data characteristics the performance of the MASi service has to be fitting across the different cases. We evaluate the performance along three initial heterogeneous use cases. Various aspects are investigated;First, the object insertion and query performance of the database along the object fill level. Second and third, the ingest and download performance of digital objects using real-life data sets. Highly favorable performance characteristics are shown.

关键词： Research Data Management Repository Performance

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Evaluating and Optimizing OpenCL Base64 Data Unpacking Kernel with FPGA 26

Evaluating and Optimizing OpenCL Base64 Data Unpacking Kerne...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Jin, Zheming Johnson, Iris Finkel, Hal Argonne Natl Lab Leadership Comp Facil Argonne IL 60439 USA Northern Illinois Univ Dept Compute Sci De Kalb IL 60115 USA

ISBN: (纸本)9781538649756

Development of applications using OpenCL targeting FPGAs is an emerging approach on heterogeneous computing systems. This paper uses the data unpacking algorithm in Base64 encoding as a case study to present programming and optimization techniques, and experimental results of the OpenCL-based implementations on an FPGA. We explain the algorithm and evaluate the performance of the kernel implementations with Intel's FPGA OpenCL SDK. The experimental results show kernel vectorization and duplication are two optimization techniques that can improve the kernel performance. The performance of kernel duplication is also closely related to the local work size. Our experiment shows 16-lane vectorization increases the bandwidth by a factor of 2 to 10 for large input data sizes. Moreover, the performance of kernel duplication using 16 compute units is 40% to 1.5% less than that of kernel vectorization depending on the input size. Tuning the local work size can improve the kernel performance by a factor of 3 to 23. For this kernel, using local memory is not an effective technique to improve the kernel performance because input data is not reused. A combination of vectorization and duplication achieves the highest performance of 12.3 GiB/s. Compared to an Intel Xeon E5 CPU and an Nvidia Tesla K80 GPU, the performance of the kernel on the Arria 10 FPGA is 6.7X faster than the CPU and 3X slower than the GPU. The performance per watt on the FPGA is 20.5X higher than the CPU and 1.19X lower than the GPU.

关键词： FPGA OpenCL Base64 Encoding

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MixedCache: Enabling Flow Directed Rule-Caching Scheme based on Heterogeneous Cache for OpenFlow 3

MixedCache: Enabling Flow Directed Rule-Caching Scheme based...

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2020 3rd International conference on Computer Information Science and Artificial Intelligence, CISAI 2020

作者： Yang, Jialun Yang, Yi Liu, Jiahao Liu, Rulin Qiao, Peng Wang, Baosheng College of Computer Science and Technology National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

OpenFlow switches in SDN use Multiple Flow Tables (MFTs) for fine-grained flow control. Commodity switches integrate hardware storage resources such as SRAM and TCAM to store flow tables to achieve high-speed lookups. Many increased flow tables are rapidly exhausting these hardware storage resources, which makes the switches have to balance high-speed search and massive storage. The rule-caching scheme is a popular method to solve this problem, which caches the most commonly used rules into hardware storage resources. The existing rule-caching schemes are based on single hardware storage resources, and they cannot flexibly adjust the caching strategy according to the traffic characteristics. Simultaneously, the deployed commodity switches face the problem of difficulty in changing the size of SRAM and TCAM. This paper innovatively proposes the MixedCache scheme, which makes full use of the hardware storage resources in the switch according to the skewed characteristics of network traffic. MixedCache stores the large flows in SRAM by exact match and stores the small flows in the TCAM by wildcard match. MixedCache does not need to change the size of the deployed switch hardware storage resources, but makes full use of existing resources. Compared with the rule-caching scheme based on the exact match, the cache hit rate can increase by up to 15.61%. Compared with the rule-caching scheme based on the wildcard match, the cache hit rate can increase by up to 29.69%. © Published under licence by IOP Publishing Ltd.

关键词： Static random access storage

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Scheduling Placement-Sensitive BSP Jobs with Inaccurate Execution Time Estimation 38

Scheduling Placement-Sensitive BSP Jobs with Inaccurate Exec...

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39th IEEE International conference on Computer Communications (IEEE INFOCOM)

作者： Han, Zhenhua Tan, Haisheng Jiang, Shaofeng H-C Fu, Xiaoming Cao, Wanli Lau, Francis C. M. Univ Sci & Technol China Hefei Anhui Peoples R China Univ Hong Kong Hong Kong Peoples R China Weizmann Inst Sci Rehovot Israel Univ Gottingen Gottingen Germany

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164120

The Bulk Synchronous parallel (BSP) paradigm is gaining tremendous importance recently because of the popularity of computations such as distributed machine learning and graph computation. In a typical BSP job, multiple workers concurrently conduct iterative computations, where frequent synchronization is required. Therefore, the workers should be scheduled simultaneously and their placement on different computing devices could significantly affect the performance. Simply retrofitting a traditional scheduling discipline will likely not yield the desired performance due to the unique characteristics of BSP jobs. In this work, we derive SPIN, a novel scheduling designed for BSP jobs with placement-sensitive execution to minimize the makespan of all jobs. We first prove the problem approximation hardness and then present how SPIN solves it with a rounding-based randomized approximation approach. Our analysis indicates SPIN achieves a good performance guarantee efficiently. Moreover, SPIN is robust against misestimation of job execution time by theoretically bounding its negative impact. We implement SPIN on a production-trace driven testbed with 40 GPUs. Our extensive experiments show that SPIN can reduce the job makespan and the average job completion time by up to 3x and 4.68x, respectively. Our approach also demonstrates better robustness to execution time misestimation compared with heuristic baselines.

关键词： Graphics processing units Servers Processor scheduling Machine learning Robustness Estimation error

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