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11th IEEE Int Conf on Cloud Comp Technol and Sci (CloudCom) / 19th IEEE Int Conf Comp and Informat Technol (CIT) / 2019 Int Workshop on Resource Brokering with Blockchain (RBchain) / Asia-Pacific Serv Comp Conf (APSCC)

作者： Nguyen, Tri D. T. Park, Jae Ho Hossain, Md Imtiaz Hossain, Md Delowar Lee, Seung-Jin Jang, Jin Woong Jo, Seo Hui Huynh, Luan N. T. Tran Trong Khanh Huh, Eui-Nam Kyung Hee Univ Dept Comp Sci & Engn Seoul South Korea CHIENCE 100210F Ace Techno Tower 2Digital Ro 31 Gil 19 Seoul South Korea

ISBN: (纸本)9781728150116

Human activity recognition (HAR), driven by large deep learning models, has received a lot of attention in recent years due to its high applicability in diverse application domains, manipulate time-series data to speculate on activities. Meanwhile, the cloud term "as-a-service" has essentially revolutionized the information technology industry market over the last ten years. These two trends somehow are incorporating to inspire a new model for the assistive living application: HAR as a service in the cloud. However, with frequently updates deep learning frameworks in open source communities as well as various new hardware features release, which make a significant software management challenge for deep learning model developers. To address this problem, container techniques are widely employed to facilitate the deep learning software development cycle. In addition, models and the available datasets are being larger and more complicated, and so, an expanding amount of computing resources is desired so that these models are trained in a feasible amount of time. This requires an emerging distributed training approach, called data parallelism, to achieve low resource utilization and faster execution in training time. Therefore, in this paper, we apply the data parallelism to build an assistive living HAR application using LSTM model, deploying in containers within a Kubernetes cluster to enable the real-time recognition as well as prediction of changes in human activity patterns. We then systematically measure the influence of this technique on the performance of the HAR application. Firstly, we evaluate our system performance with regard to CPU and GPU when deployed in containers and host environment, then analyze the outcomes to verify the difference in terms of the model learning performance. Through the experiments, we figure out that data parallelism strategy is efficient for improving model learning performance. In addition, this technique helps to increase the sc

关键词： human activity recognition data parallelism machine learning LSTM containers

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Contention-free communication scheduling for group communication in data parallelism

Contention-free communication scheduling for group communica...

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OTM Confederated International Conference and Workshop

作者： Wang, Jue Hu, Changjun Li, Jianjiang Univ Sci & Technol Beijing Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9783540768357

Group communication significantly influences the performance of data parallel applications. It is required often in two situations: one is array redistribution from phase to phase;the other is array remapping after loop partition. Nevertheless, the important factor that influences the efficiency of group Communication is often neglected: a larger communication idle time may occur when there is node contention and difference among message lengths during one particular communication step. This paper is devoted to develop an efficient scheduling strategy using the compiling information provided by array subscripts, array distribution pattern and array access period. Our strategy not only avoids inter-processor contention, but it also minimizes real communication cost in each communication step. Our experimental results show that our strategy has better performance than the traditional implement of MPI_Alltoallv, alltoall based scheduling, and greedy scheduling.

关键词： parallel compiling group communication communication scheduling data parallelism distributed memory multi-computers

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Accelerator: Using data parallelism to program GPUs for general-purpose uses

Accelerator: Using data parallelism to program GPUs for gene...

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12th International Conference on Architectural Support for Programming Languages and Operating Systems

作者： Tarditi, David Puri, Sidd Oglesby, Jose Microsoft Research

GPUs are difficult to program for general-purpose uses. Programmers can either learn graphics APIs and convert their applications to use graphics pipeline operations or they can use stream programming abstractions of GPUs. We describe Accelerator, a system that uses data parallelism to program GPUs for general-purpose uses instead. Programmers use a conventional imperative programming language and a library that provides only high-level data-parallel operations. No aspects of GPUs are exposed to programmers. The library implementation compiles the data-parallel operations on the fly to optimized GPU pixel shader code and API calls. We describe the compilation techniques used to do this. We evaluate the effectiveness of using data parallelism to program GPUs by providing results for a set of compute-intensive benchmarks. We compare the performance of Accelerator versions of the benchmarks against hand-written pixel shaders. The speeds of the Accelerator versions are typically within 50% of the speeds of hand-written pixel shader code. Some benchmarks significantly outperform C versions on a CPU: they are up to 18 times faster than C code running on a CPU.

关键词： measurement performance experimentation languages graphics processing units data parallelism just-in-time compilation

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Enhancing data parallelism of Fully Homomorphic Encryption 19th

Enhancing Data Parallelism of Fully Homomorphic Encryption

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19th International Conference on Information Security and Cryptology (ICISC)

作者： Martins, Paulo Sousa, Leonel Univ Lisbon INESC ID Inst Super Tecn Rua Alves Redol9 P-1000029 Lisbon Portugal

ISBN: (纸本)9783319531762;9783319531779

With Fully Homomorphic Encryption (FHE), it is possible to produce encryptions of the addition and multiplication of encrypted values without access to the private-key. Since homomorphic multiplication is the most burdensome operation of FHE, every possible improvement to it has a significant impact on the performance of the homomorphic evaluation of arbitrary functions. In this paper, we propose an optimized homomorphic multiplication algorithm and apply it to the NTT-based Fast Lattice library (NFLlib), which is a library designed for the implementation of Lattice-based Cryptography (LBC). When implemented with AVX2 Single Instruction Multiple data (SIMD) extensions on a i7-4770k CPU, the proposed algorithm produces a normalized speed-up of 1.93 when compared with the fastest AVX2 implementation of the state of the art. Furthermore, when extended to decryption, the new method achieves a normalized speed-up of 2.0 when compared with related art.

关键词： Homomorphic Encryption data parallelism Ring learning with errors

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Distributed Hybrid DL Cyber-Attacks Detection using data parallelism in Cloud-Dew Computing

Distributed Hybrid DL Cyber-Attacks Detection using Data Par...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Moussa, Mohamed Mounir Alazzawi, Lubna Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)9798350397420

Mobility is a synonym for ubiquitous devices on the ground and is more related to secure and distributed connectivity. Research in Machine Learning (ML) has developed techniques and systems that parallelize Deep Learning (DL) training over numerous devices or compute nodes as the pace of innovation in these domains has quickened recently. Many systems have failed to deliver overall performance on a variety of DL models as the models' structural complexity increases. Particularly, the knowledge and time needed to map from a suitable distribution strategy to the model are frequently underestimated when it comes to ML scale-up. More specifically, when Autonomous and Connected Vehicles (CAVs) are used as dew devices in cloud-dew computing systems and are planning a safe route in specific road circumstances. Applying parallel training systems to complicated models leads to lower-than-expected performance and nontrivial development overheads in addition to model prototyping. This article identifies and addresses research difficulties in both usability and performance in parallel ML approaches and system implementations, when a CAV generates the dataset for path planning and communicates it on the cloud-dew network, we use a hybrid DL model that uses an LSTM-AE built-in a cyber-attacks detection system. We emulate the situation of numerous CAVs using the same data profile for motion planning on the same road environment. We examine the configuration in a distributed architecture employing synchronous data parallelism, which enables an optimized speedup and improved training runtime by comparing various processing units.

关键词： Mobility CAV data parallelism distributed architecture cloud-dew computing learning

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List Scheduling Strategies for Task Graphs with data parallelism

List Scheduling Strategies for Task Graphs with Data Paralle...

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1st International Symposium on Computing and Networking (CANDAR)

作者： Liu, Yang Taniguchi, Ittetsu Tomiyama, Hiroyuki Meng, Lin Ritsumeikan Univ Dept Elect & Comp Engn Kusatsu Shiga 5258577 Japan

ISBN: (纸本)9781479927951

This paper studies task scheduling algorithms which schedule a set of tasks on multiple cores so that the total scheduling length is minimized. Most of the algorithms developed in the past assume that a task is executed on a single core. Unlike the previous algorithms, the algorithms studied in this paper allow a task to be executed on multiple cores. This paper proposes six algorithms. All of the six algorithms are based on list scheduling, but the strategy for priority assignment is different. In our experiments, the six algorithms as well as an integer linear programming method are evaluated.

关键词： task scheduling multicore data parallelism

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Spatial computing as intensional data parallelism

Spatial computing as intensional data parallelism

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作者： Spicher, Antoine Michel, Olivier Giavitto, Jean-Louis LACL Université Paris XII - Paris Est 94010 Créteil cedex France IBISC CNRS Université d'Evry 91025 Evry cedex France

ISBN: (纸本)9780769542294

In this paper, we show that various concepts and tools developed in the 90's in the field of data-parallelism provide a relevant spatial programming framework. It allows high level spatial computation specifications to be translated into efficient low-level operations on processing units. We provide some short examples to illustrate this statement. © 2010 IEEE.

关键词： Collection data flow data parallelism data-fields Declarative definition Intensionnal programming Spatial computing Stream

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Measuring the Effects of data parallelism on Neural Network Training

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JOURNAL OF MACHINE LEARNING RESEARCH 2019年 20卷

作者： Shallue, Christopher J. Lee, Jaehoon Antognini, Joseph Sohl-Dickstein, Jascha Frostig, Roy Dahl, George E. Google Brain 1600 Amphiteatre Pkwy Mountain View CA 94043 USA Google AI Residency Program Mountain View CA USA

Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation hardware is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured by the number of steps necessary to reach a goal out-of-sample error. We study how this relationship varies with the training algorithm, model, and data set, and find extremely large variation between workloads. Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes. We find no evidence that larger batch sizes degrade out-of-sample performance. Finally, we discuss the implications of our results on efforts to train neural networks much faster in the future. Our experimental data is publicly available as a database of 71,638,836 loss measurements taken over the course of training for 168,160 individual models across 35 workloads.

关键词： neural networks stochastic gradient descent data parallelism batch size deep learning

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Combining stream with data parallelism abstractions for multi-cores

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JOURNAL OF COMPUTER LANGUAGES 2022年 73卷

作者： Loff, Junior Hoffmann, Renato B. Griebler, Dalvan Fernandes, Luiz G. Pontifical Catholic Univ Rio do Grande Sul PUCRS Sch Technol BR-90619900 Porto Alegre RS Brazil Tres de Maio Fac SETREM Lab Adv Res Cloud Comp LARCC BR-98910 Tres De Maio Brazil

Stream processing applications have seen an increasing demand with the raised availability of sensors, IoT devices, and user data. Modern systems can generate millions of data items per day that require to be processed timely. To deal with this demand, application programmers must consider parallelism to exploit the maximum performance of the underlying hardware resources. In this work, we introduce improvements to stream processing applications by exploiting fine-grained data parallelism (via Map and MapReduce) inside coarse-grained stream parallelism stages. The improvements are including techniques for identifying data parallelism in sequential codes, a new language, semantic analysis, and a set of definition and transformation rules to perform source-to-source parallel code generation. Moreover, we investigate the feasibility of employing higher-level programming abstractions to support the proposed optimizations. For that, we elect SPar programming model as a use case, and extend it by adding two new attributes to its language and implementing our optimizations as a new algorithm in the SPar compiler. We conduct a set of experiments in representative stream processing and data-parallel applications. The results showed that our new compiler algorithm is efficient and that performance improved by up to 108.4x in data-parallel applications. Furthermore, experiments evaluating stream processing applications towards the composition of stream and data parallelism revealed new insights. The results showed that such composition may improve latencies by up to an order of magnitude. Also, it enables programmers to exploit different degrees of stream and data parallelism to accomplish a balance between throughput and latency according to their necessity.

关键词： Programming language Parallel programming Stream parallelism data parallelism parallelism abstractions Stream processing

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Real-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, data parallelism and Deep Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第3期11卷 8-16页

作者： Premarathne, Ajith Halgamuge, Malka N. Samarakody, R. Nirmalathas, Ampalavanapillai Velrada Capital Pty Ltd Melbourne Vic 3000 Australia Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

Cryptocurrency has as of late pulled in extensive consideration in the fields of economics, cryptography, and computer science due to it is an encrypted digital currency, peer-to- peer virtual forex produced using codes, and it is much the same as another medium of the trade like real cash. This study mainly focuses to combine the Deep Learning with data parallelism and Cloud Computing Machine learning engine as "hybrid architecture" to predict new Cryptocurrency prices by using historical Cryptocurrency data. The study has exploited 266,776 of Cryptocurrency prices values from the pilot experiment, and Deep Learning algorithm used for the price prediction. The four hybrid architecture models, namely, (i) standalone PC, (ii) Cloud computing without data parallelism (GPU-1), (iii) Cloud computing with data parallelism (GPU-4), and (iv) Cloud computing with data parallelism (GPU-8) introduced and utilized for the analysis. The performance of each model is evaluated using different performance evaluation parameters. Then, the efficiency of each model was compared using different batch sizes. An experimental result reveals that Cloud computing technology exposes new era by performing parallel computing in IoT to reduce computation time up to 90% of the Deep Learning algorithm-based Cryptocurrencies price prediction model and many other IoT applications such as character recognition, biomedical field, industrial automation, and natural disaster prediction.

关键词： Internet of things IoT data parallelism deep learning cloud computing

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