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Automatic stereo disparity search range detection on parallel computing architectures 17th

Automatic stereo disparity search range detection on paralle...

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17th international conference on Image Analysis and Recognition, ICIAR 2020

作者： Perera, Ruveen Low, Tobias University of Southern Queensland Toowoomba Australia

ISBN: (纸本)9783030503468

From the earliest to the state-of-the-art algorithms, stereo depth estimation techniques often require a disparity search range (DSR) value to be chosen manually. However, the optimal DSR varies from one scene to another making the results depend on the operator input and operator having to optimize the configuration by using trial-and-error. In this paper we present a novel technique suitable for parallel computing architectures which detects the optimum DSR for a given scene without requiring operator input or prior knowledge of the scene. Experiments on stereo images from Middlebury, KITTI and Sceneflow bench-mark datasets indicate that our technique can automatically extract a suitable DSR value from different scenes, which leads to better consistency in matching. the technique presented here can be used with existing stereo algorithms to limit the size of the cost volume as it is being built (without requiring pre-processing or operator input). A CUDA based implementation of our method can deliver real-time performance on consumer grade GPUs at high frame rates. © Springer Nature Switzerland AG 2020.

关键词： Graphics processing unit

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AutoTrans: Automating Transformer Design via Reinforced Architecture Search 10th

AutoTrans: Automating Transformer Design via Reinforced Arch...

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10th CCF conference on Natural Language processing and Chinese Computing, NLPCC 2021

作者： Zhu, Wei Wang, Xiaoling Ni, Yuan Xie, Guotong East China Normal University Shanghai China Ping An Healthcare Tech Beijing China Ping An Healthcare and Tech Shanghai China Ping An International Smart City Shenzhen China

ISBN: (纸本)9783030884796

though the transformer architectures have shown dominance in many natural language understanding tasks, there are still unsolved issues for the training of transformer models, especially the need for a principled way of warm-up which has shown importance for stable training of a transformer, as well as whether the task at hand prefer to scale the attention product or not. In this paper, we empirically explore automating the design choices in the transformer model, i.e., how to set layer-norm, whether to scale, number of layers, number of heads, activation function, etc., so that one can obtain a transformer architecture that better suits the tasks at hand. RL is employed to navigate along search space, and special parameter sharing strategies are designed to accelerate the search. It is shown that sampling a proportion of training data per epoch during search help to improve the search quality. Experiments on the CoNLL03, Multi-30k and WMT-14 shows that the searched transformer model can outperform the standard transformers. In particular, we show that our learned model can be trained more robustly with large learning rates without warm-up. © 2021, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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An OpenMP parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems 2020

An OpenMP Parallel Genetic Algorithm for Design Space Explor...

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11th Workshop on parallel Programming and Run-Time Management Techniques for Many-Core architectures / 9th Workshop on Design Tools and architectures for Multicore Embedded Computing Platforms, PARMA-DITAM 2020

作者： Muttillo, Vittoriano Giammatteo, Paolo Fiorilli, Giuseppe Pomante, Luigi University of L’Aquila L’Aquila Italy

ISBN: (纸本)9781450375450

Heterogeneous multiprocessor platforms are becoming widespread in the embedded system domain, mainly for the opportunity to improve timing performance and to minimize energy/power consumption and costs. therefore, when using such platforms, it is important to adopt a Design Space Exploration (DSE) strategy that considers compromises among different objectives. Existing DSE approaches are generally based on evolutionary algorithms to solve Multi-Objective Optimization Problems (MOOPs) by minimizing a linear combination of weighted cost functions (i.e., Weighted Sum Method, WSM). In this way, the main issues are related to reduce timing execution while trying to improve the evolutionary algorithm performance, introducing strategies that attempt to bring better solutions. Code parallelization is one of the most used approaches in this field, but no standard methods have been released since different aspects could affect the performance. this approach leads to exploit parallel and distributed processing elements in order to implement evolutionary algorithms. In the latter case, if we consider genetic algorithms, it is possible to talk about parallel Genetic algorithms (PGA). Considering this context, this paper focuses on DSE for heterogeneous multi-processor embedded systems and introduces an improvement that reduces execution time using parallel programming languages (i.e., OpenMP) inside the main genetic algorithm approach, while trying to lead to better partitioning solutions. the descriptions of the adopted DSE activities and the OpenMP implementation, validated by means of a case study, represent the core of the paper. © 2020 Association for Computing Machinery.

关键词： Multiobjective optimization

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HDF5-Based I/O Optimization for Extragalactic HI Data Pipeline of FAST 19th

HDF5-Based I/O Optimization for Extragalactic HI Data Pipeli...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Ji, Yiming Yu, Ce Xiao, Jian Tang, Shanjiang Wang, Hao Zhang, Bo Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci CAS Key Lab FAST NAOC Beijing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), which is the largest single-dish radio telescope in the world, has been producing a very large data volume with high speed. So it requires a high performance data pipeline to covert the huge raw observed data to science data product. However, the existing solutions of pipelines widely used in radio data processing cannot tackle this situation efficiently. the paper proposes a pipeline architecture for FAST based on HDF5 format and several I/O optimization strategies. First, we design the workflow engine driving the various tasks efficiently in the pipeline;second, we design a common radio data storage specification on the top of HDF5 format, and also developed a fast converter to map the original FITS format to the new HDF5 format;third, we apply several concrete strategies to optimize the I/O operations, including chunks storage, parallel reading/writing, on-demand dump, and stream process etc. In the experiment of processing 700 GB of FAST data, the results show that HDF5 based data structure without other optimizations was 1.7 times faster than original FITS format. If chunk storage and parallel I/O optimization are applied, the overall performance can reach 4.5 times as the original one. Moreover, due to the good expansibility and flexibility, our solution of FAST pipeline can be adapted to other radio telescopes.

关键词： FAST FITS HDF5 Pipeline in parallel High performance I/O

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parallel Computational Structure and Semantics For Soil Quality Analysis Based On LoRa and Apache Spark 14

Parallel Computational Structure and Semantics For Soil Qual...

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14th IEEE international conference on Semantic Computing (ICSC)

作者： Lima Pereira, Mauricio F. Cruvinel, Paulo E. Alves, Gabriel M. Beraldo, Jose Marcos G. Embrapa Instrumentat Sao Carlos SP Brazil Univ Fed Mato Grosso Inst Comp Sci Cuiaba MT Brazil Univ Fed Sao Carlos Post Grad Program Comp Sci Sao Carlos SP Brazil Fed Inst Sao Paulo Sj Da Boa Vista Matao SP Brazil

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163321

A large volume of data is being produced by agrometeorological stations, satellites, Unmanned Aerial Vehicles (UAV), agricultural machines, among other equipment's, all used for agricultural management in food production. Related to that, actually, there are demands for the organization of new computational techniques for agricultural risk management. therefore, the data volume is immense, and there are challenges to execute them based on both conventional algorithms and architectures. this paper presents the organization of a new parallel model based on semantics and the Apache Spark framework for agricultural risk management related to soil quality. Based in such concept, a parallel algorithm is proposed to distribute tomographic images of soil to several nodes, as well as, to ask them for not only pore recognition, but also for soil density and soil compaction measurements from those images. Besides, the developed parallel algorithm allowed to obtain a better performance gains and reduction of the time required to generate maps for agricultural soil quality, i.e., to support decision making in such field of interest.

关键词： parallel processing Apache Spark Tomographic Images Soil Quality LPWAN

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On GPU optimizations of stencil codes for highly parallel simulations

On GPU optimizations of stencil codes for highly parallel si...

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Euromicro conference on parallel, Distributed and Network-Based processing

作者： Nikolai Pfisterer Marco Berghoff Achim Streit Steinbuch Centre for Computing Karlsruhe Institute of Technology Karlsruhe Germany

Stencil codes are valuable methods to solve partial differential equations of models in a wide range of applications in science and engineering. Graphics processing units (GPUs) provide a highly parallel architecture with fast directly accessible memory that is desirable to run stencil codes. they enable larger and more complex simulations that are solved faster compared to simulating on CPUs. We provide a solution for users to run highly parallel stencil codes on GPUs, that can also be efficiently used on a distributed GPU *** this work, we present an extension to the multi-disciplinary framework NAStJA originally designed to efficiently run stencil codes on the CPUs in current high-performance computing (HPC) systems, to also be able to run on GPUs. We describe different methods to increase the performance of stencil codes on GPUs like a border exchange method which is transparent to the user, as well as a buffer for gradient values that are needed multiple times. We show their performance using the phase-field method as a stencil code *** this GPU extension and optimizations implemented into the NAStJA framework, we can show highly improved performance compared to the CPU implementation, and an efficiency of 92% (weak scaling) for a large-scale example simulation on 64 GPUs on the ForHLR II HPC system.

关键词： Partial differential equations Graphics processing units Clustering algorithms Performance gain parallel architectures Central processing Unit Mathematical model

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Off-Chip Congestion Management for GPU-based Non-Uniform processing-in-Memory Networks 28

Off-Chip Congestion Management for GPU-based Non-Uniform Pro...

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28th Euromicro international conference on parallel, Distributed and Network-Based processing (PDP)

作者： Punniyamurthy, Kishore Gerstlauer, Andreas Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781728165820

Advancements in packaging and 3D-stacking technology have enabled novel non-uniform processing-in-memory (NUPIM) architectures integrating a network of hybrid modules each with varying sizes and proportions of compute and memory. In such systems, network congestion can have a significant impact on performance and energy-efficiency scaling. At the same time, NUPIM systems often have strict power and thermal constraints, where traditional routing/redundancy or buffering based approaches to reduce congestion cannot be applied. In this paper, we propose a low-overhead yet scalable scheme for congestion management in off-chip networks of emerging GPU-based NUPIM systems. We note that congestion in NUPIM networks exhibits a form of tree saturation in which individually congested links form congested paths, where congestion effects due to back pressure vary across paths. Additionally, memory responses can have different performance impact due to memory divergence. Our approach exploits differences in performance impact of NUPIM traffic to mitigate congestion effects. We dynamically track congested paths and divergent data to manage priorities and accelerate performance-critical traffic. We further use critical paths knowledge to dynamically manage link widths and save I/O energy. Results show that our congestion management outperforms a configuration with double the number of virtual channels and buffers indicating its effectiveness in managing congestion under stringent constraints. It improves performance by 15% (and up to 31%) over baseline and 10% (and up to 29%) against a memory distance based approach for congestion-affected benchmarks. Our scheme reduces 78% of I/0 energy on average and up to 29% of system energy across all benchmarks.

关键词： Energy efficiency

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A Communication-Avoiding Algorithm for Molecular Dynamics Simulation 19th

A Communication-Avoiding Algorithm for Molecular Dynamics Si...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Wang, Bei Chen, Yifeng Hou, Chaofeng Peking Univ EECS Beijing 100871 Peoples R China Chinese Acad Sci Inst Proc Engn Beijing Peoples R China

ISBN: (纸本)9783030389918;9783030389901

Molecular dynamics and many similar time-dependent computing tasks are defined as simple state updates over multiple time steps. In recent years, modern supercomputing clusters have enjoyed fast-growing compute capability and moderate-growing memory bandwidth, but their improvement of network bandwidth/latency is limited. In this paper, we propose a new communication-avoiding algorithmic model based on asynchronous communications which, unlike BSP, records and handles multiple iterative states together. the basic idea is to let computation run in small regular time steps while communications over longer dynamic time steps. Computation keeps checking inaccuracies so that the intervals between communications are small in volatile scenarios but longer when dynamics is smooth. this helps reduce the number of data exchanges via network communication and hence improve the overall performance when communication is the bottleneck. We test MD simulation of condensed covalent materials on the Sunway TaihuLight. For best time-to-solution, the general-purpose supercomputer Sunway TaihuLight performs 11.8K steps/s for a system with 2.1 million silicon atoms and 5.1 K steps/s for 50.4 million silicon atoms. this time-to-solution performance is close to those of state-of-art hardware solution. A software solution using general-purpose supercomputers makes the technology more accessible to the general scientific users.

关键词： BSP parallel-in-time Molecular dynamics

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Cross-shard Transaction processing in Sharding Blockchains 20th

Cross-shard Transaction Processing in Sharding Blockchains

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Liu, Yizhong Liu, Jianwei Yin, Jiayuan Li, Geng Yu, Hui Wu, Qianhong Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9783030602482;9783030602475

Sharding blockchains could improve the transaction throughput and achieve scalibility, making the application fields of the blockchain technology more extensive. Cross-shard transactions account for a large fraction of transactions in a sharding blockchain, so the processing method of cross-shard transactions is of vital importance to the system efficiency. In this paper, we focus on the study of cross-shard transaction processing methods. Firstly, a summary of cross-shard transaction processing methods for sharding blockchains is given. Secondly, we propose RSTBP, which is built on the basis of a two phase commit protocol. In RSTBP, an input shard runs an intra-shard consensus algorithm, i.e., a Byzantine fault tolerance (BFT) algorithm, to process multiple inputs of different transactions simultaneously. For each input, a corresponding proof of availability is generated and sent to the relevant shards. Compared with previous schemes, the number of BFT calls is reduced by hundreds of times when processing the same number of transactions. thirdly, RSTSBP is designed by making some modifications to RSTBP. the proofs of availability are constructed according to different shards. the Merkel tree structure is different from that of RSTBP to cut down message complexity of the proofs. Both of the two schemes are proved to satisfy the consistency, liveness and responsiveness properties, and improve the cross-shard transaction processing efficiency.

关键词： Sharding blockchains Byzantine fault tolerance Cross-shard transactions Scalability Responsiveness

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Distributed and parallel Ensemble Classification for Big Data Based on Kullback-Leibler Random Sample Partition 20th

Distributed and Parallel Ensemble Classification for Big Dat...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Wei, Chenghao Zhang, Jiyong Valiullin, Timur Cao, Weipeng Wang, Qiang Long, Hao Shenzhen Univ Big Data Inst Coll Comp Sci & Software Engn Shenzhen 518000 Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 311305 Peoples R China Southern Univ Sci & Technol SUSTech Acad Adv Interdisciplinary Studies Shenzhen 518055 Peoples R China

ISBN: (纸本)9783030602451;9783030602444

In this article, we use a Kullback-Leibler random sample partition data model to generate a set of disjoint data blocks, where each block is a good representation of the entire data set. Every random sample partition (RSP) block has a sample distribution function similar to the entire data set. To obtain the statistical measure between them, Kernel Density Estimation (KDE) with a dual-tree recursion data structure is firstly applied to fast estimate the probability density of each block. then, based on the Kullback-Leibler (KL) divergence measure, we can obtain the statistical similarity between a randomly selected RSP data block and other RSP data blocks. We rank the RSP data blocks according to their divergence values in descending order and choose the first ten for an ensemble classification learning. the classification models are established in parallel for the selected RSP data blocks and the final ensemble classification model is obtained by the weighted voting ensemble strategy. the experiments were conducted by building XGboost model based on those ten blocks in parallel, and we incrementally ensemble them according to their KL values. the testing classification results show that our method can increase the generalization capability of the ensemble classification model. It could reduce the model building time in parallel computation environment by using less than 15% of the entire data, which could also solve the memory constraints of big data analysis.

关键词： Big data analysis Approximate computing Random sample partition Ensemble classification parallel distributed computing

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