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Effectiveness of Moldable and Malleable Scheduling in Deep Learning Tasks 24

Effectiveness of Moldable and Malleable Scheduling in Deep L...

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24th ieee International Conference on parallel and distributed Systems (ICPADS)

作者： Fujiwara, Ikki Tanaka, Masahiro Taura, Kenjiro Torisawa, Kentaro Natl Inst Informat & Commun Technol NICT Universal Commun Res Inst Data Driven Intelligent Syst Res Ctr DIRECT 3-5 Hikaridai Seika Kyoto 6190289 Japan Univ Tokyo Grad Sch Informat Sci & Technol Dept Informat & Commun Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1130033 Japan

ISBN: (纸本)9781538673089

Research and development of deep learning (DL) applications often involves exhaustive trial-and-error, which demands that shared computational resources, especially GPUs, be efficiently allocated. Most DL tasks are moldable or malleable (i.e., the number of allocated GPUs can be changed before or during execution). However, conventional batch schedulers do not take advantage of DL tasks' moldability/malleability, inhibiting speedup when some GPU resources are unallocated. Another opportunity for speedup is to run multiple tasks concurrently on one GPU, which may improve the overall throughput because a single task does not always fully utilize the GPU's computational resources. We propose designing a batch scheduling system that exploits these opportunities to accelerate DL tasks. As a first step, this study conducts an extensive case study to evaluate the speedup of DL tasks when a scheduler treats them as moldable or malleable. that is, the scheduler adjusts the number of GPUs to be (or already) allocated to a task in response to the fluctuating availability of GPUs. Simulations using our real workload trace show that if the scheduler can allocate 1-4 GPUs to a task or assign 1-4 tasks to a GPU, then the average flow time of moldable/malleable DL tasks is shortened by at least 15.1 %/42.5%, respectively, compared to a Rigid FCFS schedule in which one GPU is allocated to each task.

关键词： Task analysis Graphics processing units Artificial neural networks parallel processing Training Runtime Scheduling

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Finding simple sequence repeats (SSRs) within human genome using MapReduce based K-mer algorithm 5

Finding simple sequence repeats (SSRs) within human genome u...

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5th ieee International Conference on parallel, distributed and Grid Computing (PDGC)

作者： Mondal, Sudip Khatua, Sunirmal Univ Calcutta Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9781728106465

Simple sequence repeats (SSRs) is a collection of the different short and repetitive oligonucleotides sequence that is primarily observed in between any large DNA sequence. In comparison to the standard DNA sequence, these short oligonucleotides may have distinct functions and structural properties. In this article, we have presented a massive parallel processing model for basic operations with k-mers from DNA sequences, based on SSRs function. We introduce a novel approach that finds SSRs using k-mer in the first step. We implement the MapReduce based K-mer algorithm for the advantage of parallel execution, which also enables the analysis of large datasets using of the distributed platform. We have compared the result with KAnalyze, a pipelined K-mer toolkit for finding different sizes of k-mer, and found significant improvement in terms of computational time. Our study shows that MapReduce based k-mer has the potential which reduces memory footprint and accurately identifies every SSR of any specified length. Our proposed MapReduce based k-mer method is useful for finding any Simple sequence repeats (SSRs) in large DNA sequences on any distributed platform.

关键词： simple sequence repeats K-mer MapReduce Hadoop distributed Computing

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Brain Tumor Segmentation by Texture Feature Extraction with the parallel Implementation of Fuzzy C-Means using CUDA on GPU 5

Brain Tumor Segmentation by Texture Feature Extraction with ...

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5th ieee International Conference on parallel, distributed and Grid Computing (PDGC)

作者： Saxena, Sanjay Shama, Suraj Int Inst Informat Technol Bhubaneswar IIIT Bhuban Dept Comp Sci & Engn Bhubaneswar India

ISBN: (纸本)9781728106465

Exact segmentation of the brain tumor is one of the imperative tasks in medical image processing and its analysis as it deals with extracting the information of the tumorous region from the brain MRI sequences. Automated segmentation and detection of brain tumors from the brain MRI is an exigent issue caused by the texture, size, shape, and location. In this paper, a significant method of brain tumor segmentation from the FLAIR MRI sequences is by classifying local window followed by parallel fuzzy c means clustering. Fuzzy c- means methods have shown their efficiency in extracting a variety of objects in several medical image processing applications. However, one of the major issues of these algorithms is high computational requirements at the time of dealing with large data set. Nowadays, NVIDIA's GPU plays an extremely essential role in implementing such time-consuming algorithms to reduce the time complexity. Our experiments based on NCI-MICCAI BRATS 2017 FLAIR MRI of HGG (High-Grade Glioma) demonstrate the efficiency of the implemented parallel algorithm. For the segmentation of tumorous region, a mechanism of sliding window is implemented on CPU (host) in which a 45 X 45 sized window is taken to classify whether that particular window is having tumor region or not. For perfect segmentation at the GPU (device) side, fuzzy c means technique is used to get the exact location of the tumor. Approx 17.6 speed up obtained, for the BRATS data sets over the implementation of the algorithm on CPU. Apart from speed up significant dice similarity coefficients are obtained which shows the efficient segmentation in the reasonable time.

关键词： Brain Tumor Segmentation Computed Unified Device Architecture Graphics processing Unit High Performance Computing Texture Feature Extraction Fuzzy C-means

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PERFORMANCE ANALYSIS OF parallel/distributed PARTICLE FILTERS

PERFORMANCE ANALYSIS OF PARALLEL/DISTRIBUTED PARTICLE FILTER...

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Spring symposium on theory of Modeling and Simulation (SpringSim-TMS)

作者： Zhang, Xudong Mohamed, Ali Nguyen, Linda Gu, Feng CUNY Grad Ctr Dept Comp Sci 365 5th Ave New York NY 10016 USA CUNY Coll Staten Isl Dept Comp Sci 2800 Victory Blvd Staten Isl NY 10314 USA San Jose State Univ Dept Comp Engn One Washington Sq San Jose CA 95192 USA

ISBN: (纸本)9781510860209

Simulation models are widely used to study various systems. However, many of them lack of accuracy without incorporating real time data from the system under study. Particle filters can be used to assimilate real time data into dynamic systems to improve state inferences. parallel/distributed particle filters were introduced by deploying particles among multiple processing units (PUs) to improve the performance. In parallel/distributed particle filters, the resampling process needs particle exchanging among PUs, which affects the speedup factor of parallel computing. therefore, efficient particle routing policies are necessary to improve the performance. In this paper, we analyze the performance of parallel/distributed particle filters with three different particle routing policies. the analysis is able to provide the guidelines in optimizing the resampling strategies and applications. the simulation and experimental results showed the relationship between the speedup factor and system inherent complexity, the dimension of particle, and the number of PUs.

关键词： Particle filters Sequential Monte Carlo (SMC) methods performance analysis particle routing policy time complexity

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NCA 2018 - 2018 ieee 17th International symposium on Network Computing and Applications

NCA 2018 - 2018 IEEE 17th International Symposium on Network...

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17th ieee International symposium on Network Computing and Applications, NCA 2018

ISBN: (纸本)9781538676592

the proceedings contain 62 papers. the topics discussed include: Sycomore: a permissionless distributed ledger that self-adapts to transactions demand;analytical hierarchy process multi-metric objective function for RPL;LoRaWAN analysis under unsaturated traffic, orthogonal and non-orthogonal spreading factor conditions;DBFT: efficient leaderless Byzantine consensus and its application to blockchains;efficient multi-hop broadcasting in dense nanonetworks;securing WirelessHART: monitoring, exploring and detecting new vulnerabilities;response time characterization of microservice-based systems;low-reliable low-latency networks optimized for HPC parallel applications;and recurrent neural network-based prediction of TCP transmission states from passive measurements.

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proceedings - ieee 28th International parallel and distributed processing symposium Workshops, IPDPSW 2014

Proceedings - IEEE 28th International Parallel and Distribut...

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30th ieee International parallel and distributed processing symposium Workshops, IPDPSW 2016

ISBN: (纸本)9781509021406

the proceedings contain 210 papers. the topics discussed include: towards a green, QoS-enabled heterogeneous cloud infrastructure;predicting job completion time in heterogeneous MapReduce environments;minimizing rental cost for multiple recipe applications in the cloud;providing fairness in heterogeneous multicores with a predictive, adaptive scheduler;dynamic resource management for parallel tasks in an oversubscribed energy-constrained heterogeneous environment;evaluation of emerging energy-efficient heterogeneous computing platforms for biomolecular and cellular simulation workloads;latency, power, and security optimization in distributed reconfigurable embedded systems;and a reconfigurable fixed-point architecture for adaptive beamforming.

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Design Issues of Novel Round Robin Scheduling Scheme to Improve processing Performance in a 5G mmWave Network 24

Design Issues of Novel Round Robin Scheduling Scheme to Impr...

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24th ieee International Conference on parallel and distributed Systems (ICPADS)

作者： Yang, Shin-Jer Chen, Hsin-Chung Soochow Univ Dept Comp Sci & Informat Management Taipei Taiwan

ISBN: (纸本)9781538673089

Due to mature technologies in cloud computing, and the fast development of sensing devices, IoT applications popularized in recent years have become the main contributors to a drastic increasing in future wireless mobile network data traffic. Compared to 4G LTE, 5G provides higher network traffic and lower packet latency. However, 5G that transmits data to thousands of users at a speed of 10 Gbps still requires proper allocation of mobile network bandwidth resources. When B-RR executes resource allocation on the 5G mmWave network, the problem of inefficiency when the Channel Quality Indicator (CQI) value is nonexistent but data transmission and UE polling continue to take place is likely to be encountered, resulting in network resource loss during UE data transmission. therefore, a new NRRS was proposed in this paper to improve B-RR and achieve more efficient resource allocation. the NRRS scheduling scheme focuses on the use of a CQI report value as the basis for resource allocation. NRRS and B-RR simulation experiments were carried out to compare their performance. the simulation results of KPIs indicate that the Cell latency can achieve 27%, 11%, 15%, and 13% improvement ratios;throughput can attain 2%, 41%, 35%, and 30% improvement ratios;and also the Data failure ratio can get 19%, 24%, 33%, and 16% improvement ratios in 10UEs, 20UEs, 50UEs, and 100UEs, respectively. Preliminary research results have proven that the NRRS scheduling scheme can obtain a lower Cell latency, higher throughput and lower Data failure ratio, which are highly conducive to allocating better bandwidth resources and more efficient data retransmissions in the 5G mmWave network.

关键词： NRRS B-RR 5G 4G LTE

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Seal: Efficient Training Large Scale Statistical Machine Translation Models on Spark 24

Seal: Efficient Training Large Scale Statistical Machine Tra...

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24th ieee International Conference on parallel and distributed Systems (ICPADS)

作者： Gu, Rong Chen, Min Yang, Wenjia Yuan, Chunfeng Huang, Yihua Nanjing Univ Natl Key Lab Novel Software Technol Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781538673089

Statistical machine translation (SMT) is an important research branch in natural language processing (NLP). Similar to many other NLP applications, large scale training data can potentially bring higher translation accuracy for SMT models. However, the traditional single-node SMT model training systems can hardly cope with the fast-growing amount of large scale training corpus in the big data era, which makes the urgent requirement of efficient large scale machine translation model training systems. In this paper, we propose Seal, an efficient, scalable, and end-to-end offline SMT model training toolkit based on Apache Spark which is a widely-used distributed data-parallel platform. Seal parallelizes the training process of the entire three key SMT models that are the word alignment model, the translation model, and the N-Gram language model, respectively. To further improve the performance of the model training in Seal, we also propose a number of system optimization methods. In word alignment model training, by optimizing the block size tuning, the overhead of IO operation and communication is greatly reduced. In translation model training, by well encoding the training corpus, the data size transferred over the network can be reduced significantly, thus improving the overall training efficiency. We also optimize the maximum likelihood estimation (MLE) algorithm to solve the data skew issue on the join operation which is adopted both in the translation model training and the language model training. the experiment results show that Seal outperforms the well-known SMT training system Chaski with about 5x speedup for word alignment model training. For the syntactic translation model and language model training, Seal outperforms the existing cutting-edge tools with about 9 similar to 18x speedup and 8 similar to 9x speedup on average, respectively. On the whole, Seal outperforms the existing distributed system with 4 similar to 6x speedup and the single-node system w

关键词： statistical machine translation parallel word alignment algorithm parallel translation algorithm parallel language model algorithm distributed data-parallel system

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proceedings - 2016 ieee 30th International parallel and distributed processing symposium, IPDPS 2016

Proceedings - 2016 IEEE 30th International Parallel and Dist...

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30th ieee International parallel and distributed processing symposium, IPDPS 2016

ISBN: (纸本)9781509021406

the proceedings contain 114 papers. the topics discussed include: distributed-memory algorithms for maximum cardinality matching in bipartite graphs;ARCHER: effectively spotting data races in large OpenMP applications;algorithm and architecture independent benchmarking with SEAK;design and implementation of a parallel research kernel for assessing dynamic load-balancing capabilities;VNRE: flexible and efficient acceleration for network redundancy elimination;analyzing network health and congestion in dragonfly-based supercomputers;random regular graph and generalized de Bruijn graph with k-shortest path routing;deflection containment for bufferless network-on-chips;RUPS: fixing relative distances among urban vehicles with context-aware trajectories;hybrid dynamic trees for extreme-resolution 3D sparse data modeling;and optimization of an electromagnetics code with multicore wavefront diamond blocking and multi-dimensional intra-tile parallelization.

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Hyaline: A Transparent distributed Computing Framework for CUDA

Hyaline: A Transparent Distributed Computing Framework for C...

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International Conference for Convergence of Technology (I2CT)

作者： Akshat Jain Owais Kazi Raviraj Joshi Shraddha Basantwani Yogita Bang Rahul Khengare Computer Engineering PICT Pune Computer Science IIT Madras

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

the GPU usually handles the homogenous data parallel work, by taking advantage of its massive number of cores. In most of the applications, we use CUDA programming for utilizing the power of GPU. In data intensive high computational applications like neural networks, utilizing the GPU on a single machine is time consuming. Instead if multiple GPUs are used in a network the amount of time required will be significantly reduced. Traditionally to enable a set of program to be run in a distributed environment, programmer has to accordingly design components to make his system dynamic and resilient to changes in number of systems in cluster, which is a daunting task. this work distribution can be a poor solution as it may underutilize the GPUsIn our approach, we developed a framework which transparently distributes data parallel kernels across multiple GPUs in a distributed network. the programmer is responsible for developing a single data parallel kernel in CUDA while the framework automatically distributes the workload across an arbitrary set of CUDA enabled GPUs. Depending on current workload on GPUs and the amount of data to be processed optimal distribution is done. the goal is to maximally utilize the available resources with minimal programming complexity. the systems not compatible with CUDA can also utilize our solution. We expect our framework to reduce the processing time along with simplifying the task of programmers.

关键词： Graphics processing units Kernel distributed databases Computational modeling Programming Task analysis Computer architecture

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