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检索条件"任意字段=8th IFIP International Conference on Network and Parallel Computing"

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POSTER: Elastic Reconfiguration for Heterogeneous NoCs with BiNoCHS 26

POSTER: Elastic Reconfiguration for Heterogeneous NoCs with ...

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26th international conference on parallel Architectures and Compilation Techniques (PACT)

作者： Mirhosseini, Amirhossein Sadrosadati, Mohamad Soltani, Behnaz Sarbazi-Azad, Hamid Wenisch, thomas F. Univ Michigan Ann Arbor MI 48109 USA Sharif Univ Technol Tehran Iran

ISBN: (纸本)9781509067640

CPU-GPU heterogeneous systems are emerging are emerging as architectures of choice for high-performance energy-efficient computing. Designing on-chip interconnects for such systems is challenging: CPUs typically benefit greatly from optimizations that reduce latency, but rarely saturate bandwidth or queueing resources. In contrast, GPUs generate intense traffic that produces local congestion, harming CPU performance. Congestion-optimized interconnects can mitigate this problem through larger virtual and physical channel resources. However, when there is little traffic, such networks become suboptimal due to higher unloaded packet latencies and critical path delays. We argue for a reconfigurable network that can activate additional channels under high load/congestion and shut them off when the network is unloaded. However, these additional resources consume more power, making it difficult to statically provision a power budget for the network. We propose Elastic network Reconfiguration, wherein we aggressively reduce voltage to free power budget to activate additional channels. Our key observation is that, under high load, the reduced queueing due to additional channels more than compensates for the increase in per-hop latency of the reduced clock frequency. We introduce BiNoCHS as a voltage-scalable NoC that specifically targets CPU-GPU heterogeneous systems and employs elastic network reconfiguration to maintain a constant power budget while adapting between latency- and congestion-optimized modes.

关键词： throughput Delays Clocks Routing Bandwidth Computer architecture network-on-chip

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MD-IDN: Multi-Domain Intent-Driven networking in Software-Defined Infrastructures 13

MD-IDN: Multi-Domain Intent-Driven Networking in Software-De...

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13th AMC/IEEE/ifip international conference on network and Service Management (CNSM)

作者： Arezoumand, Saeed Dzeparoska, Kristina Bannazadeh, Hadi Leon-Garcia, Alberto Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

ISBN: (纸本)9783901882982

Intent-Driven networking is recently gaining interest, with all major SDN control platforms now providing an intent Northbound Interface (NBI) as a high-level abstraction for network management. With these frameworks network operators can conveniently define "what needs to be done", rather than "how it should be done". Current IDN frameworks pose two main limitations that affect deployment in production grade and multi-domain networks. they are mainly concerned with a single network domain, and thus enabling end-to-end network intents over a multi-domain and large-scale setup is still a challenge. Furthermore, these frameworks do not consider any differentiation between user intents and provider intents, and a limited set of intent classes are available for both. In this paper we present MD-IDN, which provides an intent framework for the users of multi-domain cloud infrastructures. We first propose a graph-based abstraction model for user-defined intents and a generic intent compilation process. then, we propose compilation algorithms to achieve scalability in multi-domain networks: First, user-defined intents get processed over an abstracted multi-graph of network domains and their interconnections, and a set of local intents will be generated for each of the involved domains. Afterwards, the local intents will be compiled and installed in local regions in parallel. MD-IDN is deployed as a public service in the SAVI Testbed over more than ten data centers spanning across Canada. In multi-domain environments, our experiments show that MD-IDN outperforms current practices that compile intents over a flat network topology.

关键词： Topology network topology Peer-to-peer computing Authorization Logic gates Scalability Middleboxes

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POSTER: Statement Reordering to Alleviate Register Pressure for Stencils on GPUs 26

POSTER: Statement Reordering to Alleviate Register Pressure ...

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26th international conference on parallel Architectures and Compilation Techniques (PACT)

作者： Rawat, Prashant S. Sukumaran-Rajam, Aravind Rountev, Atanas Rastello, Fabrice Pouchet, Louis-Noel Sadayappan, P. Ohio State Univ Columbus OH 43210 USA INRIA Grenoble France Colorado State Univ Ft Collins CO 80523 USA

ISBN: (纸本)9781509067640

Compute-intensive GPU architectures allow the use of high-order 3D stencils for better computational accuracy. these stencils are usually compute-bound. While current state-of-the-art register allocators are satisfactory for most applications, they are unable to effectively manage register pressure for such complex high-order stencils, resulting in a sub-optimal code with a large number of register spills. We develop an optimization framework that models stencils as a forest of trees and performs statement reordering to reduce register use. the effectiveness of the approach is demonstrated through experimental results on several high-order stencils.

关键词： High-order stencils register pressure GPU computing

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Optimizing OpenCL Implementation of Deep Convolutional Neural network on FPGA 1

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14th ifip WG 10.3 international conference on network and parallel computing (NPC)

作者： Qiao, Yuran Shen, Junzhong Huang, Dafei Yang, Qianming Wen, Mei Zhang, Chunyuan Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (数字)9783319682105

ISBN: (纸本)9783319682105;9783319682099

Nowadays, the rapid growth of data across the Internet has provided sufficient labeled data to train deep structured artificial neural networks. While deeper structured networks bring about significant precision gains in many applications, they also pose an urgent demand for higher computation capacity at the expense of power consumption. To this end, various FPGA based deep neural network accelerators are proposed for higher performance and lower energy consumption. However, as a dilemma, the development cycle of FPGA application is much longer than that of CPU and GPU. Although FPGA vendors such as Altera and Xilinx have released OpenCL framework to ease the programming, tuning the OpenCL codes for desirable performance on FPGAs is still challenging. In this paper, we look into the OpenCL implementation of Convolutional Neural network (CNN) on FPGA. By analysing the execution manners of a CPU/GPU oriented verision on FPGA, we find out the causes of performance difference between FPGA and CPU/GPU and locate the performance bottlenecks. According to our analysis, we put forward a corresponding optimization method focusing on external memory transfers. We implement a prototype system on an Altera Stratix V A7 FPGA, which brings a considerable 4.76x speed up to the original version. To the best of our knowledge, this implementation outperforms most of the previous OpenCL implementations on FPGA by a large margin.

关键词： Field programmable gate arrays (FPGA)

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A Virtual Machine Migration Algorithm Based on Group Selection in Cloud Data Center 1

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14th ifip WG 10.3 international conference on network and parallel computing (NPC)

作者： Guo, Zhen Yao, Wenbin Wang, Dongbin Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Natl Engn Lab Mobile Network Secur Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (数字)9783319682105

ISBN: (纸本)9783319682105;9783319682099

Live migration of virtual machine (VM) is a promising technology that helps physical machines (PMs) adapt to load changes and guarantees Quality of Service (QoS) in cloud data center. Many individual- based VM migration studies ignore the association between VMs, resulting in high communication cost. Some research on multiple VMs migration migrates the VM group as a whole, which is likely to result in ineffective migration and increase the network burden. In this paper, a VM migration algorithm based on group selection (VMMAGS) is proposed, which takes into account the migration cost, communication cost, and VM heat to optimize migration performance. the appropriate VM groups are selected as migration options, and the optimal migration scheme is obtained according to the integration cost of partitions of selected VM groups. Extensive experiments show that our algorithm can effectively reduce the migration cost and communication cost, improve the system reliability compared with other related algorithms.

关键词： Virtual machine

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Optimalization of parallel GNG by Neurons Assigned to Processes 16th

Optimalization of Parallel GNG by Neurons Assigned to Proces...

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16th ifip TC8 international conference on Computer Information Systems and Industrial Management (CISIM)

作者： Vojacek, Lukas Drazdilova, Pavla Dvorsky, Jiri Tech Univ Ostrava VSB IT4Innovat 17 Listopadu 15-2172 Ostrava 70833 Czech Republic Tech Univ Ostrava VSB Dept Comp Sci 17 Listopadu 15-2172 Ostrava 70833 Czech Republic

ISBN: (纸本)9783319591056;9783319591049

the size, complexity and dimensionality of data collections are ever increasing from the beginning of the computer era. Clustering is used to reveal structures and to reduce large amounts of raw data. there are two main issues when clustering based on unsupervised learning, such as Growing Neural Gas (GNG) [9], is performed on vast high dimensional data collection - the fast growth of computational complexity with respect to growing data dimensionality, and the specific similarity measurement in a high-dimensional space. these two factors reduce the effectiveness of clustering algorithms in many real applications. the growth of computational complexity can be partially solved using the parallel computation facilities, such as High Performance computing (HPC) cluster with MPI. An effective parallel implementation of GNG is discussed in this paper, while the main focus is on minimizing of interprocess communication. the achieved speed-up was better than previous approach and the results from the standard and parallel version of GNG are same.

关键词： Growing neural gas High-dimensional dataset High performance computing MPI

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Proceedings - 2017 IEEE international conference on High Performance computing and Communications Workshops, HPCCWS 2017 and Multicore and Multithreaded Architectures and Algorithms, M2A2 2017

Proceedings - 2017 IEEE International Conference on High Per...

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2017 IEEE international conference on High Performance computing and Communications Workshops, HPCCWS 2017 and 8th Multicore and Multithreaded Architectures and Algorithms, M2A2 2017

ISBN: (纸本)9781538659304

the proceedings contain 12 papers. the topics discussed include: portable and vendor-independent low-level programming and performance benchmarking for graphics cards and processors;a hybrid parallel genetic algorithm with dynamic migration strategy based on Sunway many-core processor;a framework for WAN optimization and data center selection using NaaS;parallel grouping particle swarm optimization with stream processing paradigm;finding hierarchical network model in insulin production process;efficient generation of parallel spin-images using dynamic loop scheduling;a survey and recommendations for distributed, parallel, single pass, incremental Bayesian classification based on MapReduce for big data;AWEDF: an architecture-aware execution and debugging workflow for a highly reliable chip;performance evaluation of Intel broadwell nodes based supercomputer using computational fluid dynamics and climate applications;an efficient elasticity mechanism for server-based pervasive healthcare applications in cloud environment;and thai sentence generation machine employing fixed patterns.

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Sthira: A Formal Approach to Minimize Voltage Guardbands under Variation in networks-on-Chip for Energy Efficiency 26

Sthira: A Formal Approach to Minimize Voltage Guardbands und...

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26th international conference on parallel Architectures and Compilation Techniques (PACT)

作者： Pothukuchi, Raghavendra Pradyumna Ansari, Amin Gopireddy, Bhargava Torrellas, Josep Univ Illinois Champaign IL 61820 USA Qualcomm Res Champaign IL USA

ISBN: (纸本)9781509067640

networks-on-Chip (NoCs) in chip multiprocessors are prone to within-die process variation as they span the whole chip. To tolerate variation, their voltages (V-dd) carry over-provisioned guardbands. As a result, prior work has proposed to save energy by operating at reduced V-dd while occasionally suffering and fixing errors. Unfortunately, these proposals use heuristic controller designs that provide no error bounds guarantees. In this work, we develop a scheme that dynamically minimizes the V-dd of groups of routers in a variation-prone NoC using formal control-theoretic methods. the scheme, called Sthira, saves substantial energy while guaranteeing the stability and convergence of error rates. We also enhance the scheme with a low-cost secondary network that retransmits erroneous packets for higher energy efficiency. the enhanced scheme is called Sthira+. We evaluate Sthira and Sthira+ with simulations of NoCs with 64-100 routers. In an NoC with 8 routers per V-dd domain, our schemes reduce the average energy consumption of the NoC by 27%;in a futuristic NoC with one router per V-dd domain, Sthira+ and Sthira reduce the average energy consumption by 36% and 32%, respectively. the performance impact is negligible. these are significant savings over the state-of-the-art. We conclude that formal control is essential, and that the cheaper Sthira is more cost-effective than Sthira+.

关键词： network on chip Variation Control theory

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parallel multi-feature attention on neural sentiment classification 17

Parallel multi-feature attention on neural sentiment classif...

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8th international Symposium on Information and Communication Technology, SoICT 2017

作者： Pengcheng, Zhu Yujiu, Yang Graduate School at Shenzhen Tsinghua University Shenzhen GuangDong China

ISBN: (纸本)9781450353281

the analysis of the review's sentiment polarity is a fundamental task in NLP. However, most of the existing sentiment classification models only focus on extracting features but ignore features' own differences. Additionally, these models only pay attention to content information but ignore the user's ranking preference. To address these issues, we propose a novel parallel Multi-feature Attention (PMA) neural network which concentrates on fine-grained information between user and product level content features. Moreover, we use multi-feature, user's ranking preference included, to improve the performance of sentiment classification. Experimental results on IMDB and Yelp datasets show that PMA model achieves state-of-the-art performance. © 2017 Association for computing Machinery.

关键词： Classification (of information)

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A Trajectory Prediction Method with Sparsity Data 15

A Trajectory Prediction Method with Sparsity Data

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15th IEEE international Symposium on parallel and Distributed Processing with Applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Fan, Hengfei Yao, Wenbin Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Natl Engn Lab Mobile Network Secur Beijing Peoples R China

ISBN: (纸本)9781538637906

A timely and accurate prediction of the vehicle's trajectory can render a good service, help predict traffic flow, and in the more interesting case, detect potentially dangerous situations as early as possible. It is widespread to use historical data to predict trajectories. However, due to the sparsity of trajectory, sometimes it is difficult to predicate trajectory. A novel approach to trajectory prediction is proposed which has the capability to predict the trajectory with sparse data. To achieve this, we denote city route and trajectory as a series of fixed points. Under normal conditions, only the most similarly trajectory is used to predict trajectories, which may mislead the result. To remedy this problem, we use several most similarity trajectories to predict the next point, and regard the most points as our result. the experimental results show that the approach improves the prediction accuracy 8%, coverage 2%, and greatly outperform the baseline algorithm (30 ms) compared with Spatial Iterative Grid Partition (SIGP).

关键词： vehicle sparsity data trajectory prediction

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