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检索条件"任意字段=12th Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 1249 条记录，以下是171-180 订阅

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IO Challenges for Human Brain Atlasing using Deep Learning Methods - An in-depth analysis 27

IO Challenges for Human Brain Atlasing using Deep Learning M...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Oden, Lena Schiffer, Christian Spitzer, Hannah Dickscheid, Timo Pleiter, Dirk Forschungszentrum Julich JSC Julich Germany Forschungszentrum Julich Inst Neurosci & Med INM 1 Julich Germany Fernuniv Fak Math & Informat Hagen Germany

ISBN: (纸本)9781728116440

the use of Deep Learning methods have been identified as a key opportunity for enabling processing of extreme-scale scientific datasets. Feeding data into compute nodes equipped with several high-end GPUs at sufficiently high rate is a known challenge. Facilitating processing of these datasets thus requires the ability to store petabytes of data as well as to access the data with very high bandwidth. In this work, we look at two Deep Learning use cases for cytoarchitectonic brain mapping. these applications are very challenging for the underlying IO system. We present an in depth analysis of their IO requirements and performance. Both applications are limited by the IO performance, as the training processes often have to wait several seconds for new training data. Both applications read random patches from a collection of large HDF5 datasets or TIFF files, which result in many small non-consecutive accesses to the parallel file systems. By using a chunked data format or storing temporally copies of the required patches, the IO performance can be improved significantly. these leads to a decrease of the total runtime of up to 80%.

关键词： Training Task analysis Deep learning Graphics processing units Brain Bandwidth Image resolution

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Greedy Multi-cloud Selection Approach to Deploy an Application based on Microservices 27

Greedy Multi-cloud Selection Approach to Deploy an Applicati...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Carvalho, Juliana Vieira, Dario Trinta, Fernando Fed Univ Piaui UFPI Picos Brazil EFREI PARIS Paris France Fed Uniers Ceara UFC Fortaleza Ceara Brazil

ISBN: (纸本)9781728116440

the multi-cloud delivery model allows the deployment of a distributed application in a manner in which the software architect chooses the services that better meet the application and the software architect's requirements. However, choosing cloud services is a complex task for a software architect, as there are several cloud providers that offer many services, which makes the selection process from the user perspective a relevant research problem. therefore, in this paper, we propose a multiple providers selection approach to host a distributed application based on microservices to facilitate the software architect's decision-making. Our approach differs from others because each microservice is deployed in a single provider that better meets requirements from the software architect's perspective. Hence, our approach selects various services of a single provider for a microservice, and a distinct provider for each microservice, which makes this a complex task. We also use multi-criteria decision-making to rank the cloud services, and we map the selection process to multi-choice knapsack problem, which we develop using a greedy algorithm. Furthermore, we implement a tool to evaluate our approach performance, and the results show our approach feasibility, varying the number of cloud providers and services, microservices and requirements.

关键词： multi-cloud selection microservices multi-requirements multi-choice knapsack problem greedy algorithm

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New Scheduling Strategy based on Multi-Criteria Decision Algorithm 27

New Scheduling Strategy based on Multi-Criteria Decision Alg...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Menouer, Tarek Darmon, Patrice UMANIS Levallois Perret France

ISBN: (纸本)9781728116440

this paper presents a new scheduling strategy proposed to optimize the scheduling of several containers submitted online by users in a private infrastructure of nodes i.e. a cloud platform. In the literature, several scheduling frameworks and studies are proposed. the majority of these works use a scheduling strategy based on one criterion, such as Spread and Bin Packing strategies. the Spread strategy consists to select the node having the least number of executed containers to balance the containers load between all nodes of the infrastructure. the Bin Packing strategy consists to select the most compacted node in terms of resources to reduce the number of used nodes of the infrastructure. However, the submitted containers are defined according to a multi-criteria, such as the number of used CPUs and the used memory size. the state of each node is also defined according to a multi-criteria, such as the number of executed containers, the number of available CPUs and the size of available memory. the novelty of our scheduling strategy is to choose the node that executes a container by combining the Spread and the Bin Packing principles using the Technique for the Order of Prioritisation by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision analysis algorithm. Our proposed strategy is implemented in Docker Swarm. Docker swarm is an important container scheduler framework developed by Docker. Experiments demonstrate the potential of our strategy under different scenarios.

关键词： Container Technology Cloud Computing Scheduling Strategy

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Should PARSEC Benchmarks be More Parametric? A Case Study with Dedup 27

Should PARSEC Benchmarks be More Parametric? A Case Study wi...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Maron, Carlos A. F. Vogel, Adriano Griebler, Dalvan Fernandes, Luiz Gustavo Pontifical Catholic Univ Rio Grande do Sul PUCRS Sch Technol Porto Alegre RS Brazil Tres de Maio Fac SETREM LARCC Tres De Maio Brazil

ISBN: (纸本)9781728116440

parallel applications of the same domain can present similar patterns of behavior and characteristics. Characterizing common application behaviors can help for understanding performance aspects in the real-world scenario. One way to better understand and evaluate applications' characteristics is by using customizable/parametric benchmarks that enable users to represent important characteristics at run-time. We observed that parameterization techniques should be better exploited in the available benchmarks, especially on stream processing domain. For instance, although widely used, the stream processing benchmarks available in PARSEC do not support the simulation and evaluation of relevant and modern characteristics. therefore, our goal is to identify the stream parallelism characteristics present in PARSEC. We also implemented a ready to use parameterization support and evaluated the application behaviors considering relevant performance metrics for stream parallelism (service time, throughput, latency). We choose Dedup to be our case study. the experimental results have shown performance improvements in our parameterization support for Dedup. Moreover, this support increased the customization space for benchmark users, which is simple to use. In the future, our solution can be potentially explored on different parallel architectures and parallel programming frameworks.

关键词： Benchmark testing parallel processing Microsoft Windows Streaming media Computer architecture Measurement throughput

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Generalized Nets Model of Data parallel processing in Large Scale Wireless Sensor networks 12th

Generalized Nets Model of Data Parallel Processing in Large ...

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12th International conference on Large-Scale Scientific Computations (LSSC)

作者： Alexandrov, Alexander Monov, Vladimir Tashev, Tasho Bulgarian Acad Sci Inst Informat & Commun Technol Sofia Bulgaria

ISBN: (纸本)9783030410322;9783030410315

the Generalized Nets (GN) approach is an advanced way of parallel processes modeling and analysis of complex systems as Large-scale Wireless Sensor networks (LWSN). the LWSN such as meteorological and air quality monitoring systems could generate a large amount of data that can reach petabytes per year. the sensor data-parallel processing is one of the possible solutions to reduce inter-node communication to save energy. At the same time, the on-site parallel processing requires additional energy, needed for computational data processing. therefore, the development of a realistic model of the process is critical for the optimization analysis of every large scale sensor network. In the proposed paper, a new developed GN based model of a sensor nodes data-parallel processing of LWSN with cluster topology is presented. the proposed model covers all the aspects of the inter-node sensor data integration and the cluster-based parallel processes specific for large scale amounts of sensor data operations.

关键词： Generalized Nets Data parallel processing LSWSN

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3DEP: A Efficient Routing Algorithm to Evenly Distribute Traffic over 3D network-on-Chips 27

3DEP: A Efficient Routing Algorithm to Evenly Distribute Tra...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Vahdatpanah, Fatemeh Elahi, Mahdi Kashi, Somayeh Taheri, Ebadollah Patooghy, Ahmad Iran Univ Sci & Technol Sch Comp Engn Tehran Iran Univ Cent Arkansas Dept Comp Sci Conway AR 72035 USA

ISBN: (纸本)9781728116440

Due to high manufacturing cost of through Silicon Via (TSV) in 3D network-on-Chips (NoCs), not every router is vertically connected. In most 3D NoCs only a subset of TSVs are contrived which results into incomplete 3D NoCs in the vertical dimension. this irregularity introduces new complexity in the design of efficient routing algorithms for partially connected 3D NoCs. In this paper, we propose an efficient routing algorithm to evenly distribute traffic in incomplete 3D NoCs. the proposed algorithm uses turn model analysis to categorize layers, columns, and rows of the NoC into different groups. then, specific turns are prohibited in each group such that the whole routing is deadlock free, livelock free, and independent of the location of TSVs over the network. By finding the best combination for prohibited turns over the network, we limited the number of required virtual channels to two virtual channel per each physical channel. Simulation results show that the proposed partially adaptive routing has improved packet latency by 32.8% in comparison with Elevator-First algorithm. the advantage of the proposed algorithm is that it shows more improvements on the packet latency and network throughput when the size of network grows.

关键词： Turn Model Routing Algorithm 3D network-on-Chip Deadlock Partially Adaptive

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Pragma-oriented parallelization of the Direct Sparse Odometry SLAM Algorithm 27

Pragma-oriented Parallelization of the Direct Sparse Odometr...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Pereira, C. Falcao, G. Alexandre, L. A. Univ Coimbra Dept Elect & Comp Engn Inst Telecomunicacoes Coimbra Portugal Univ Beira Interior Dept Informat Covilha Portugal Inst Telecomunicacoes Covilha Portugal

ISBN: (纸本)9781728116440

Monocular 3D reconstruction is a challenging computer vision task that becomes even more stimulating when we aim at real-time performance. One way to obtain 3D reconstruction maps is through the use of Simultaneous Localization and Mapping (SLAM), a recurrent engineering problem, mainly in the area of robotics. It consists of building and updating a consistent map of the unknown environment and, simultaneously, saving the pose of the robot, or the camera, at every given time instant. A variety of algorithms has been proposed to address this problem, namely the Large Scale Direct Monocular SLAM (LSD-SLAM), ORB-SLAM, Direct Sparse Odometry (DSO) or parallel Tracking and Mapping (PTAM), among others. However, despite the fact that these algorithms provide good results, they are computationally intensive. Hence, in this paper, we propose a modified version of DSO SLAM, which implements code parallelization techniques using OpenMP, an API for introducing parallelism in C, C++ and Fortran programs, that supports multi-platform shared memory multi-processing programming. With this approach we propose multiple directive-based code modifications, in order to make the SLAM algorithm execute considerably faster. the performance of the proposed solution was evaluated on standard datasets and provides speedups above 40% without significant extra parallel programming effort.

关键词： parallel Computing Open Multi-processing (OpenMP) Multiprocessing Simultaneous Localization and Mapping (SLAM)

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Catalina: In-Storage processing Acceleration for Scalable Big Data Analytics 27

Catalina: In-Storage Processing Acceleration for Scalable Bi...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Torabzadehkashi, Mahdi Rezaei, Siavash Heydarigorji, Ali Bobarshad, Hosein Alves, Vladimir Bagherzadeh, Nader Univ Calif Irvine Engn Sch EECS Irvine CA 92717 USA NGD Syst Inc Irvine CA 92618 USA Univ Calif Irvine Donald Bren Sch ICS Irvine CA USA

ISBN: (纸本)9781728116440

Cloud applications are increasingly playing a crucial role in big data analytics. New use cases such as autonomous cars and edge computing call for novel approaches mixing heterogeneous computing and machine learning. these applications typically process petabyte-scale datasets, therefore, requiring low-power and scalable storage providing low-latency and high-throughput data access. While data centers have been focusing on migrating from legacy HDDs and SATA SSDs by deploying high-throughput and low-latency NVMe SSDs, the data bottlenecks appear as capacity scales. One approach to tackle this problem is to enable processing to happen within the storage device -in-storage processing ( ISP)-eliminating the need to move the data. In this paper, we investigated the deployment of storage units with embedded low-power application processors along with FPGA-based reconfigurable hardware accelerators to address both performance and energy efficiency. To this purpose, we developed a high-capacity solid-state drive ( SSD) named Catalina equipped with a quad-core ARM A53 processor running a Linux operating system along with a highly efficient FPGA accelerator for running applications in-place. We evaluated our proposed approach on a case study application for a similarity search library called Faiss.

关键词： Computational storage In-storage processing near-data processing System-on-Chip FPGA Accelerator Big data

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Partial Packet Forwarding to Improve Performance in Fully Adaptive Routing for Cache-coherent NoCs 27

Partial Packet Forwarding to Improve Performance in Fully Ad...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Eltaras, Tamer Ahmed Fornaciari, William Zoni, Davide DET Politecn Torino Turin Italy DEIB Politecn Milano Milan Italy

ISBN: (纸本)9781728116440

In the contest of cache-coherent networks-on-Chip (NoCs), fully adaptive routing algorithms guarantee maximum flexibility to implement power-performance, fault tolerant, thermal and Quality of Service (QoS) management policies. However, to get rid of deadlock at both protocol and network level, their implementation imposes a relevant resource increase. Moreover, their performance are inferior to the one of deterministic and partially adaptive schemes mainly due to the additional constraints imposed to the virtual channel (VC) re-use policy. this work proposes a novel flow control scheme to improve the performance of fully adaptive routing algorithms by allowing an aggressive reuse of VCs in presence of both long and short packets. Our proposal works by splitting long packets in multiple chunks and by reallocating the VCs to the chunks rather that to the entire packet. By carefully sizing each chunk to fit the available space in the reallocated, eventually not empty, VC, we are avoiding deadlocks while increasing the NoC utilization and performance. Experimental results show that our solution offers a 23.8% increase, on average, in the saturation point when compared to the best state of the art flow control scheme for fully adaptive routing algorithms. Moreover, our flow control scheme offers similar or better performance than the XY routing algorithm with the same number of resources, and we also ensure superior flexibility in the definition of the routing function.

关键词： network-on-chip routing algorithm cache fully adaptive

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Proceedings - 24th euromicro International conference on parallel, distributed, and network-based processing, PDP 2016

Proceedings - 24th Euromicro International Conference on Par...

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24th euromicro International conference on parallel, distributed, and network-based processing, PDP 2016

ISBN: (纸本)9781467387750

the proceedings contain 123 papers. the topics discussed include: row key designs of NoSQL database tables and their impact on write performance;DKPN: a composite dataflow/Kahn process networks execution model;black-box optimization of Hadoop parameters using derivative-free optimization;QuLa: service selection and forwarding table population in service-centric networking using real-life topologies;HPSVM: heterogeneous parallel SVM with factorization based IPM algorithm on CPU-GPU cluster;bio-inspired call-stack reconstruction for performance;RGBCC: a new congestion control mechanism for InfiniBand;a time synchronization protocol for modular robots;exploring parallel implementations of the Bayesian probabilistic matrix factorization;massively concurrent red-black trees with hardware transactional memory;stochastic thermal control of a multicore real-time system;a hybrid load-balancing solution for s4 stream processing systems;and a cluster-based method to detect and correct anomalies in sensor data.

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