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Using Supercomputer to Speed up Neural Network Training

Using Supercomputer to Speed up Neural Network Training

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international conference on parallel and distributed Systems (ICPADS)

作者： Yue Yu Jinrong Jiang Xuebin Chi Department of High Performance Computing Technology and Application Development University of Chinese Academy of Sciences Beijing China Department of High Performance Computing Technology and Application Development Center of Scientific Computing Applications & Research Beijing China Department of High Performance Computing Technology and Application Development Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509053827

Recent works in deep learning have shown that large models can dramatically improve performance. In this paper, we accelerated the deep network training using many GPUs. We have developed a framework based on Caffe called Caffe-HPC that can utilize computing clusters with multiple GPUs to train large models. Caffe[6] provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. And Caffe-HPC retains all the features of the original Caffe, the model trained on original Caffe can be continue to trained on Caffe-HPC. It provides a convenient solution for people who are using Caffe and want to speed up the training. Using an Asynchronous Stochastic Gradient Descent optimizer, We made a good acceleration on training a CNN model on ILSVRC[5] 2012 dataset. And we have compared the convergence of different SGD algorithms. We believe our work will makes it possible to train larger networks on larger training sets in a reasonable amount of time.

关键词： Servers Training Graphics processing units Neural networks Machine learning Computational modeling Acceleration

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Lightweight Dependency Checking for parallelizing Loops with Non-Deterministic Dependency on GPU

Lightweight Dependency Checking for Parallelizing Loops with...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Hongyuan Liu King Tin Lam Huanxin Lin Cho-Li Wang Junchao Ma Department of Computer Science The University of Hong Kong Shannon Cognitive Computing Laboratory Huawei Technologies

ISBN: (纸本)9781509053827

General-purpose GPUs have been prevalent for a decade. Nevertheless, GPU programming remains an onerous job practically exclusive to veteran developers who must know both domain-specific knowledge and GPU architecture well. Although current parallelizing compilers that automatically parallelize and offload sizable loops onto the GPU have helped in unfettering the power of the GPU with minimal programming effort, there are still a family of loops that carry statically non-deterministic data dependencies and cannot be parallelized. To tackle this issue, we propose two lightweight dependency checking schemes that are very different from existing conservative compilers to assist parallelizing loops with non-deterministic data dependencies. Our schemes feature linear work complexity for memory operations, lower memory consumption compared to previous work, and minimal false positives by leveraging the lockstep execution on the GPU's SIMD lanes. Experiments done using microbenchmarking and real-life applications on the latest advanced AMD discrete GPUs show that our schemes can achieve 2.2 × speedup over existing solutions in dependency-free cases while only taking about 20% of time compared to existing solutions in the case with statically unproven loop-carried dependencies.

关键词： Graphics processing units Instruction sets Kernel Programming Memory management Registers Complexity theory

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Flex: Flexible and Energy Efficient Scheduling for Big Data Storage 44

Flex: Flexible and Energy Efficient Scheduling for Big Data ...

引用

44th Annual international conference on parallel processing Workshops (ICPPW)

作者： Ma, Daokuan Wu, Yongwei Chen, Kang Zheng, Weimin Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781467375894

Traditional distributed schedulers only consider the scheduling of jobs and treat the storage system as static and already deployed. However, individual application often needs its own configuration of storage system. Therefore, traditional distributed schedulers are not able to serve multiple tenants. To improve the resource utilization, some mechanisms are needed to consolidate multiple applications running on top of the same computing resources. Flex is such a system for helping the distributed scheduler to deploy a customized storage system before a job is scheduled without too much overhead incurred. The lightweight operating system level virtualization technology, aka containers in Linux, is used to wrap the computing environment and isolate different applications. Flex can achieve two goals that traditional distributed scheduler cannot provide. One is to deploy a specific storage system for the requirement of an individual application;the other is to consolidate applications for a better use of the underlying computing resources. Thus, by employing storage scheduling, Flex improves the resource utilization of distributed systems.

关键词： Containers Flexible printed circuits Hardware High performance computing Operating systems Virtual machining Virtualization Big Data distributed Scheduler distributed System Linux Container Cache Memory System big data distributed Systems virtual machines Operating systems High Performance Computing Virtualization CONTAINERS Computer hardware Flexibility Resource utilization Flexible printed circuits virtual machining jobs

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paralleled Fast Search and Find of Density Peaks clustering algorithm on GPUs with CUDA

Paralleled Fast Search and Find of Density Peaks clustering ...

引用

ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Mi Li Jie Huang Jingpeng Wang School of Software Engineering Tongji University Shanghai China

Fast Search and Find of Density Peaks (FSFDP) is a newly proposed clustering algorithm that has already been successfully applied in many applications. However, this algorithm shows a dissatisfactory performance on large dataset due to the time-consuming calculation of the distance matrix and potentials. In this paper, we proposed a GPU-accelerated FSFDP with CUDA to improve its performance. Thread/block models and the shared memory usage are dedicatedly designed to maximize the utilization of GPUs' hardware resources, and a merge accumulation algorithm based on the odd and even positions of an array is introduced as well. Experimental results show that our parallel implementation of FSFDP can reach a 4.39X and a 15.75X speedup for the calculation of the distance matrix and potentials respectively compared to the serial program on a single CPU core. Higher speedup can be expected for data of larger scales until the device limits are reached. Besides, CUDA stream mechanism is also employed and extra time savings can be obtained by hiding the corresponding memory latency of multiple kernels in a two-way streams' scheduling. Moreover, we evaluate our GPU-based implementation on GPU clusters of 9 nodes and compared to one GPU node, the program can achieve a further 7.55X speedup.

关键词： Graphics processing units Instruction sets Kernel Clustering algorithms Entropy Software algorithms Acceleration

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Complex event processing over uncertain events: techniques, challenges, and future directions

Complex event processing over uncertain events: Techniques, ...

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international conference on Computation of Power, Energy, Information and Communication (ICCPEIC)

作者： V. Akila V. Govindasamy S. Sandosh Pondicherry Engineering College Puducherry India Christ Institute Puducherry India

In the recent years, there is a massive surge in real-time data owing to the growing number of distributed software applications that continuously generates a large volume of data streams. These applications utilize new hardware technologies and data collection methods that inherently produce uncertain data. The Complex event processing (CEP) is a crucial software technology that manages highly dynamic event streams in a timely manner and determines the event patterns of interest. The CEP processes the massive data streams from different applications that depend on the reliability of data to produce accurate results. The business intelligence requires high-level of knowledge extracted from continuous streams. Ignoring uncertainty in event processing significantly degrades the performance and accuracy. Thus, efficient processing of uncertain data is vital to improving the efficiency and the accuracy of the CEP results for better decision making in business. This survey discusses the basics of uncertainty, explores models and query processing methods to deal with uncertainty. The primary contribution of this work is the survey of CEP regarding event query language and event query processing involved in identifying the complex event patterns over uncertain events. The challenges and the future directions for handling uncertain events in CEP are discussed in detail.

关键词： Data models Uncertainty Lead Mathematical model Probabilistic logic

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Towards an auto-tuning system design for optimal sparse compression format selection with user expertise

Towards an auto-tuning system design for optimal sparse comp...

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IEEE/ACS international conference of Computer Systems and applications

作者： Ichrak MEHREZ Olfa HAMDI-LARBI Thomas DUFAUD Nahid EMAD Universite de Tunis El Manar Faculte des Sciences de Tunis URAPOP Universite de Versailles St-Quentin Universite Paris-Saclay

ISBN: (纸本)9781509043217

Several applications in numerical scientific computing process sparse matrices with either a regular or irregular structure. The very large size of these matrices requires to use compressing formats and target parallel/distributed architectures in order to reduce both space complexity and processing time. The optimal compression format (OCF) of such matrices may in fact vary according to both the application context of the numerical method and the target hardware architecture. In this paper, we propose a design of a system that automatically selects the OCF according to the two above cited parameters. The expert system obtained from our model targets dynamic integration of the user expertise thus allowing better performances. The optimal format selection is based on the makespan criterion. As a first validation test of our system, we studied the representative case of Horner scheme in the context of data parallel programming model and multicore cluster. Our experiments focus on the four compression formats CSR, CSC, COO and ELLPACK and their complexities in a data parallel programming model context.

关键词： Auto-tuning Sparse matrix Data parallel model Multicore cluster Horner method

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An Analysis Platform of Road Traffic Management System Log Data Based on distributed Storage and parallel Computing techniques

An Analysis Platform of Road Traffic Management System Log D...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Wei Peng Yongjiang Li Bing Li Xiangyuan Zhu Computer Center Kunming university of Science and Technology Kunming Yunnan China Department of Computer Science Georgia State University Atlanta Georgia USA Computer Technology Application Key Lab of Yunnan Province Kunming university of Science and Technology Kunming Yunnan China School of Computer Zhaoqing University Zhaoqing Guangdong China

ISBN: (纸本)9781509039371

Road traffic management system generates a huge number of log data which can help the managers to monitor the drivers' behavior, identify traffic condition and extract some useful information. However, due to the high production rate and diversity of log data, it still is a big challenge to store and analyze them. Applying distributed storage and parallel computing in the clusters can improve the respond speed of searching and analyzing big data. In this work, we design a platform which integrates some distributed storage and parallel computing technologies i.e. Hadoop, Spark, Hive, Flume etc. to collect, store and analyze the mass log data. Moreover, the platform can timely display analysis results in graphical format through integrating some traditional technologies. The experimental results show that combining Flume and Hive is reliable and flexible in collecting and storing log data. Moreover, the query operation on mass log data by using Spark techniques spends less time than that in traditional ways, which verifies the success of our solution.

关键词： Sparks distributed databases Roads Data analysis Servers parallel processing Data collection

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Leveraging naturally distributed data redundancy to reduce collective I/O replication overhead 29

Leveraging naturally distributed data redundancy to reduce c...

引用

29th IEEE international parallel and distributed processing Symposium (IPDPS)

作者： Nicolae, Bogdan IBM Res Dublin Ireland

ISBN: (纸本)9781479986484

Dumping large amounts of related data simultaneously to local storage devices instead of a parallel file system is a frequent I/O pattern of HPC applications running at large scale. Since local storage resources are prone to failures and have limited potential to serve multiple requests in parallel, techniques such as replication are often used to enable resilience and high availability. However, replication introduces overhead, both in terms of network traffic necessary to distribute replicas, as well as extra storage space requirements. To reduce this overhead, state-of-art techniques often apply redundancy elimination (e.g. compression or deduplication) before replication, ignoring the natural redundancy that is already present. By contrast, this paper proposes a novel scheme that treats redundancy elimination and replication as a single co-optimized phase: remotely duplicated data is detected and directly leveraged to maintain a desired replication factor by keeping only as many replicas as needed and adding more if necessary. In this context, we introduce a series of high performance algorithms specifically designed to operate under tight and controllable constrains at large scale. We present how this idea can be leveraged in practice and demonstrate its viability for two real-life HPC applications.

关键词： data resilience high availability data replication deduplication collective I/O scalability redundancy management

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A GPU-parallel Algorithm for ECG Signal Denoising Based on the NLM Method

A GPU-Parallel Algorithm for ECG Signal Denoising Based on t...

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international conference on Advanced Information Networking and applications Workshops (AINAW)

作者： Salvatore Cuomo Pasquale De Michele Ardelio Galletti Livia Marcellino University of Naples Federico II Naples Italy Univ. of Naples Federico II Napoli Campania IT University of Naples “Parthenope” Naples Italy

In recent years, the real-time diagnosis in the E-health is a widely used practice. Employing distributed computing systems, it is possible to obtain excellent results, avoiding long delays and invasive processes. However, the data processing stage, generally assigned on standard computational CPU environments, is a critical aspect, especially when the computational complexity of the numerical method used for the analysis is very high. In this paper, we consider as case of study the analysis of electrocardiogram (ECG) signals. In order to obtain a diagnosis as quickly as possible, we propose to exploit the computational power of Graphics processing Unit (GPU) environment. Using GPUs on High Performance Computing (HPC), the signal processing step can be accelerated by speeding the whole diagnosis procedure. More in detail, we designed and implemented a GPUparallel algorithm, for ECG signals denoising based on the Non Local Means (NLM) method. This method is well suited for parallelization and multithreading implementation, especially for GPU architectures. The results show a significant improvement, in terms of execution time, of the entire healthcare practice procedure, with a percentage gain of 96% with respect to the sequential version on standard CPU environment.

关键词： Electrocardiography Graphics processing units Instruction sets Standards Noise reduction Kernel Data processing

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HPGraph: A High parallel Graph processing System Based on Flash Array

HPGraph: A High Parallel Graph Processing System Based on Fl...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Yuxuan Xing Ya Feng Songping Yu Zhengguo Chen Fang Liu Nong Xiao State Key Laboratory of High Performance Computing National University of Defense Technology NUDT Changsha China National University of Defense Technology Changsha Hunan CN

ISBN: (纸本)9781509042982

Large graphs analytics has been an important aspect of many big data applications, such as web search, social networks and recommendation systems. Many research focuses on processing large scale graphs using distributed system over past few years. And numbers of studies turn to construct graph processing system on a single server-class machine in consideration of cost, usability and maintainability. HPGraph is a high parallel graph processing system which adopts the edge-centric model, our contributions are as follows: (1) designing an efficient data allocation and access strategy for NUMA machine, and providing tasks scheduling to keep load balance, (2) raising a fine-grained edge-block filtering mechanism to avoid accessing unnecessary edge data, (3) constructing a high-speed flash array as the second storage. We made a detailed evaluation on a 16-core machine using asset of popular real word and synthetic data sets, and the results show that HPGraph always outperforms the state-of-the-art single machine graph processing systems-GridGraph. And HPGraph can achieve 1.27X faster than GridGraph for specific application. Our source code is available at https://***/xinghuan1990/HPGraph.

关键词： conferences High performance computing Smart cities Data science

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