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检索条件"任意字段=13th IEEE International Symposium on Parallel and Distributed Processing with Applications"

共 2574 条记录，以下是161-170 订阅

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DAG-based Scheduling with Resource Sharing for Multi-task applications in a Polyglot GPU Runtime 35

DAG-based Scheduling with Resource Sharing for Multi-task Ap...

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35th ieee international parallel and distributed processing symposium (IPDPS)

作者： Parravicini, Alberto Delamare, Arnaud Arnaboldi, Marco Santambrogio, Marco D. Politecn Milan Milan Italy Oracle Labs Zurich Switzerland

ISBN: (纸本)9781665440660

GPUs are readily available in cloud computing and personal devices, but their use for data processing acceleration has been slowed down by their limited integration with common programming languages such as Python or Java. Moreover, using GPUs to their full capabilities requires expert knowledge of asynchronous programming. In this work, we present a novel GPU run time scheduler for multi-task GPU computations that transparently provides asynchronous execution, space-sharing, and transfer-computation overlap without requiring in advance any information about the program dependency structure. We leverage the GrCUDA polyglot API to integrate our scheduler with multiple high-level languages and provide a platform for fast prototyping and easy GPU acceleration. We validate our work on 6 benchmarks created to evaluate task-parallelism and show an average of 44% speedup against synchronous execution, with no execution lime slowdown compared to hand-optimized has' code written using the C++ CUDA Graphs API.

关键词： GPU Scheduling Software Runtime Hardware Acceleration

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Optimizing Memory-Compute Colocation for Irregular applications on a Migratory thread Architecture 35

Optimizing Memory-Compute Colocation for Irregular Applicati...

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35th ieee international parallel and distributed processing symposium (IPDPS)

作者： Rolinger, thomas B. Krieger, Christopher D. Sussman, Alan Univ Maryland College Pk MD 20742 USA Lab Phys Sci College Pk MD 20740 USA

ISBN: (纸本)9781665440660

the movement of data between memory and processors has become a performance bottleneck for many applications. this is made worse for applications with sparse and irregular memory accesses, as they exhibit weak locality and make poor utilization of cache. As a result, colocating memory and compute is crucial for achieving high performance on irregular applications. there are two paradigms for memory-compute coloration. the first is the conventional approach of moving the data to the compute. the second paradigm is to move the compute to the data, which is less conventional and not as well understood. An example are migratory threads, which physically relocate upon remote accesses to the compute resource that hosts the data. In this paper. we explore the paradigm of moving compute to the data by optimizing memory-compute coloration for irregular applications on a migratory thread architecture. Our optimization method includes both initial data placement as well as data replication. We evaluate our optimization on sparse matrix-vector multiply (SpMV) and sparse matrix-matrix multiply (SpGEMM). Our results show that we can achieve speed-ups as high as 4.2x on SpMV and 6x on SpGEMM when compared to the default data layout. We also highlight that our optimization to improve memory-compute colocation can be applicable to both migratory threads and more conventional systems. To this end, we evaluate our optimization approach on a conventional compute cluster using the Chapel programming language. We demonstrate speed-ups as high as 18x for SpMV.

关键词： colocation optimizations migratory threads irregular applications

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Co-designing the Topology/Algorithm to Accelerate distributed Training 19

Co-designing the Topology/Algorithm to Accelerate Distribute...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Hou, Xiang Xu, Rui Ma, Sheng Wang, Qiong Jiang, Wei Lu, Hongyi Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Coll Comp Changsha Peoples R China

ISBN: (纸本)9781665435741

With the development of Deep Learning (DL), Deep Neural Network (DNN) models have become more complex. At the same time, the development of the Internet makes it easy to obtain large data sets for DL training. Large-scale model parameters and training data enhance the level of AI by improving the accuracy of DNN models. But on the other hand, they also present more severe challenges to the hardware training platform because training a large model needs a lot of computing and memory resources that can easily exceed the capacity of a single processor. In this context, integrating more processors on a hierarchical system to conduct distributed training is a direction for the development of training platforms. In distributed training, collective communication operations (including all-to-all, all-reduce, and all-gather) take up a lot of training time, making the interconnection network between computing nodes one of the most critical factors affecting the system performance. the hierarchical torus topology, combined with the Ring All-Reduce collective communication algorithm, is one of the current mainstream distributed interconnection networks. However, we believe that its communication performance is not the best. In this work, we first designed a new intra-package communication topology, i.e. the switch-based fully connected topology, which shortens the time consumed by cross-node communication. then, considering the characteristics of this topology, we carefully devised more efficient all-reduce and all-gather communication algorithms. Finally, combined with the torus topology, we implemented a novel distributed DL training platform. Compared with the hierarchical torus, our platform improves communication efficiency and provides 1.16-2.68 times speedup in distributed training of DNN models.

关键词： Terms topology hardware training platform distributed training collective communication

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Attention-based Encoder-Decoder Recurrent Neural Networks for HTTP Payload Anomaly Detection 19

Attention-based Encoder-Decoder Recurrent Neural Networks fo...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Wu, Shang Wang, Yijie Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9781665435741

Attack payloads are often short segments hidden in HTTP requests;thus they can be found by HTTP payload anomaly detection. Deep learning models learn data features during training without manual feature extraction, and better performance has received more attention. Recurrent Neural Network models process sequences directly, which are widely used in payload anomaly detection. However, due to the gradient vanishing problem, RNN has limits on processing the long sequences. Meanwhile, RNN uses its final hidden state for detection and pays more attention to the content of the end of the payload. Besides, deep learning generally lacks interpretability. the paper proposes an unsupervised deep learning model for HTTP payload Anomaly Detection, namely Attention-based Encoder-Decoder Recurrent Neural Networks Anomaly Detection model (AEDRAD). AEDRAD utilizes the encoder-decoder RNN and attention mechanism to detect anomalies by reconstructing the original sequences. AEDRAD filters the fields of HTTP protocol that cannot exist anomalies, focusing on the suspicious segments. through the encoder-decoder network, the normal payload can be well-reconstructed while the anomaly payload fails. With the attention mechanism, AEDRAD generates practical features for further anomaly detection from a global perspective. Meanwhile, it marks abnormal fragments visually, which is conducive to a subsequent analysis by experts. the experimental results show that AEDRAD significantly outperforms three state-of-the-art unsupervised algorithms on two real datasets.

关键词： HTTP Payload Anomaly Detection Network Security Deep learning Encoder-Decoder Recurrent Neural Networks Attention Mechanism

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distributed Deep Learning Using Volunteer Computing-Like Paradigm

Distributed Deep Learning Using Volunteer Computing-Like Par...

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35th ieee international parallel and distributed processing symposium (IPDPS)

作者： Atre, Medha Jha, Birendra Rao, Ashwini Eydle Inc Los Angeles CA 90803 USA

ISBN: (纸本)9781665435772

Use of Deep Learning (DL) in commercial applications such as image classification, sentiment analysis and speech recognition is increasing. When training DL models with large number of parameters and/or large datasets, cost and speed of training can become prohibitive. distributed DL training solutions that split a training job into subtasks and execute them over multiple nodes can decrease training time. However, the cost of current solutions, built predominantly for cluster computing systems, can still be an issue. In contrast to cluster computing systems, Volunteer Computing (VC) systems can lower the cost of computing, but applications running on VC systems have to handle fault tolerance, variable network latency and heterogeneity of compute nodes, and the current solutions are not designed to do so. We design a distributed solution that can run DL training on a VC system by using a data parallel approach. We implement a novel asynchronous SGD scheme called VC-ASGD suited for VC systems. In contrast to traditional VC systems that lower cost by using untrustworthy volunteer devices, we lower cost by leveraging preemptible computing instances on commercial cloud platforms. By using preemptible instances that require applications to be fault tolerant, we lower cost by 70-90% and improve data security.

关键词： distributed systems distributed training deep learning volunteer computing ASGD

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Lightweight Function Monitors for Fine-Grained Management in Large Scale Python applications 35

Lightweight Function Monitors for Fine-Grained Management in...

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35th ieee international parallel and distributed processing symposium (IPDPS)

作者： Shaffer, Tim Li, Zhuozhao Tovar, Ben Babuji, Yadu Dasso, T. J. Surma, Zoe Chard, Kyle Foster, Ian thain, Douglas Univ Notre Dame Notre Dame IN 46556 USA Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781665440660

Python has become a widely used programming language for research, not only for small one-off analyses, but also for complex application pipelines running at supercomputer-scale. Modern parallel programming frameworks for Python present users with a more granular unit of management than traditional Unix processes and batch submissions: the Python function. We review the challenges involved in running native Python functions at scale, and present techniques for dynamically determining a minimal set of dependencies and for assembling a lightweight function monitor (LFM) that captures the software environment and manages resources at the granularity of single functions. We evaluate these techniques in a range of environments, from campus cluster to supercomputer, and show that our advanced dependency management planning and dynamic resource management methods provide superior performance and utilization relative to coarser-grained management approaches, achieving several-fold decrease in execution time for several large Python applications.

关键词： parallel programming Pipelines Tools Software Supercomputers Planning Resource management

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Entropy and Autoencoder-Based Outlier Detection in Mixed-Type Network Traffic Data 19

Entropy and Autoencoder-Based Outlier Detection in Mixed-Typ...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Wang, Zhongyang Wang, Yijie Huang, Zhenyu Wang, Yongjun Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Coll Comp Changsha Peoples R China

ISBN: (纸本)9781665435741

Mixed-type data containing categorical and numerical features are pervasive in real life, but very limited outlier detection methods are available for these data. Some existing methods handle mixed-type data by feature converting, whereas their performance is downgraded by information loss and noise caused by the transformation. Meanwhile, the existing general algorithms cannot combine the characteristics of outliers in specific fields, leading to an unsatisfying performance in actual scenarios, such as the field of network security. this paper proposes a novel Entropy and Autoencoder-based Outlier Detection in mixed-type network traffic data, termed EAOD, which combines characteristics of outliers in specific fields and machine learning models to detect outliers. EAOD utilizes the expert rules made by domain knowledge summarized based on characteristics of existing outliers to label known outlier data. It performs holoentropy and deep autoencoder for the category and numerical feature spaces, respectively, in unlabeled data to obtain outlier scores integrated to get the final outlier scores via a dynamic integration strategy. Especially in the numerical feature space, to fully mine known outlier behavior patterns, deep autoencoders of outlier and normal types are constructed separately to capture unknown outliers jointly. Experiments show that EAOD significantly outperforms eight state-of-the-art outlier detectors on seven real network traffic datasets.

关键词： Outlier detection Mixed-type data Unsupervised Learning

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Vapor: A GPU Sharing Scheduler with Communication and Computation Pipeline for distributed Deep Learning 19

Vapor: A GPU Sharing Scheduler with Communication and Comput...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Zhu, Xiaorui Gong, Lei Zhu, Zongwei Zhou, Xuehai Univ Sci & Technol China Dept Comp Sci & Technol Hefei Anhui Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou Peoples R China

ISBN: (纸本)9781665435741

Nowadays, distributed deep learning (DDL) workloads are typically trained on GPU clusters. Modern GPUs support the concurrent execution of multiple jobs to achieve efficient parallelization of computation and communication. While sharing GPU improves resource utilization and cuts down job completion time, interference among co-located jobs results in significant performance degradation. Recent studies such as interference-aware job placement carefully co-locate jobs with the least interference in a best-effort manner and are not ideal. Another challenge in GPU sharing is straggler due to unbalanced workloads in packing. When stragglers occur, GPU resources are wasted, so the training processes are delayed. We present Vapor, a GPU sharing scheduler for distributed deep learning on multi-GPUs featured by two novel scheduling policies: preemptive GPU sharing and adaptive batch redistribution to maximize GPU utilization and improve training efficiency. Firstly, Vapor uses preemptive scheduling that parallels computation and communication of co-located jobs in a pipelined manner. Secondly, Vapor uses an adaptive batch redistribution method to deal with stragglers in packing to improve resource utilization further. Vapor provides an AIMD model to predict the relationship between the batch size of the training data and the model calculation time in each iteration. We evaluate Vapor with other representative schedulers. Experiment on a Kubernetes cluster of 16 Tesla V100 GPU handling with popular DDL jobs shows that Vapor reduces job completion time by 21.8% compared with popular SOTA schedulers.

关键词： Training Schedules parallel computing Load balancing Graphics processing units

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Kubeflow-based Automatic Data processing Service for Data Center of State Grid Scenario 19

Kubeflow-based Automatic Data Processing Service for Data Ce...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Xu, Chongyou Lv, Guangxian Du, Jian Chen, Lei Huang, Yu Zhou, Wang State Grid Ningbo Elect Power Supply Co Ningbo Peoples R China State Grid Shanghai Energy Interconnect Res Inst Shanghai Peoples R China State Grid Zhejiang Elect Power Co LTD Hangzhou Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing Peoples R China China Southern Power Grid Digital Grid Res Inst C Guangzhou Peoples R China

ISBN: (纸本)9781665435741

With the rapid development of machine learning and deep learning, more and more machine learning and deep learning have appeared in the power grid business. the data processing in the State Grid business is very complicated, the data processing is very cumbersome, and the reuse rate of the data processing code is also very low. In order to solve these problems, this paper proposes an efficient automated data processing service-EADP (Efficient Automated Data processing). EADP service is built on Kubeflow. Kubeflow/Pipeline is Google's open source workflow for building end-to-end services. Users can build the code as Pipeline/Component for use by Kubeflow/Pipeline. But Kubeflow's Component and Pipeline construction is extremely cumbersome and lacks management of Component and Pipeline. In order to solve these problems, EADP provides the function of automatically constructing Component and data processing DAG. there is a one-to-one correspondence between Component and Docker/Image. Docker/Image contains code blocks for data processing, which can be run after instantiation. the data processing flow can be constructed as a data processing DAG. the data processing DAG is composed of Component, and each node in the DAG corresponds to a Component. EADP uses a topological sorting algorithm to convert the data processing DAG into Kubeflow/Pipeline, thereby realizing automated data processing. On the surface of the experiment, EADP has high stability and convenience, which can greatly shorten the time-consuming data processing.

关键词： Pipeline technology Component technology Topological sorting Docker/Image

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Alleviating Imbalance in Synchronous distributed Training of Deep Neural Networks 19

Alleviating Imbalance in Synchronous Distributed Training of...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Lin, Haiyang Yan, Mingyu Wang, Duo Li, Wenming Ye, Xiaochun Tang, Zhimin Fan, Dongrui Chinese Acad Sci State Key Lab Comp Architecture ICT Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China State Key Lab Math Engn & Adv Comp Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781665435741

Deep neural networks achieve huge success in computer vision, natural language process and other fields. As larger networks and larger datasets bring better performance, the training time grows rapidly. distributed training uses parallelization strategy to accelerate the training of deep neural network. In the existing distributed training framework of deep neural network, the parameter server architecture has poor scalability and is not suitable for large-scale distributed training. Ring/HD All Reduce algorithm perform well in scalability under ideal circumstances. However, in many realistic scenarios, imbalance will occur frequently in the distributed training system, which is mainly the imbalance of compute ability and network. Due to the structure characteristics of synchronous distributed training and All Reduce algorithm, imbalance will bring serious impact on performance. At present, there is a lack of research on both compute ability imbalance and network imbalance, especially dynamic imbalance. To alleviate this problem, we analyze All Reduce algorithm first, and propose RingDL, which optimizes the performance of distributed training system by alleviating imbalance through customizable dynamic workloads strategy and link strategy. the experimental results show that RingDL significantly reduces idle time in computing phase and synchronous phase, improving the overall performance.

关键词： deep neural networks synchronous distributed training imbalance dynamic workloads strategy link strategy

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