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Towards Optimized Streaming Tensor Completion on multiple GPUs 24

Towards Optimized Streaming Tensor Completion on multiple GP...

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24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Hao, Jiwei Yang, Hailong Sun, Qingxiao Zhang, Huaitao Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing100191 China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi710049 China

ISBN: (纸本)9798350319934

Tensor completion is a prevailing method for predicting the unobserved or missing data in incomplete tensors. In many real-world scenarios, incomplete tensors can grow in multiple modes with streaming fashion. Such tensors are generally transformed into a dynamically changing tensor sequence known as Multi-Aspect Streaming Tensor (MAST) sequence. However, existing optimization techniques for a single static tensor completion cannot scale well to large streaming tensor sequences with dynamically growing properties. In this paper, we develop an efficient streaming tensor completion library cuSTC on multiple graphics processing units (GPUs). cuSTC adopts a novel TB-COO storage format with tiling and bit mapping data structure. In addition, cuSTC implements a two-level tensor partition scheme to accelerate the prevalent dynamic alternating least squares (ALS) algorithm for MAST sequence. Finally, cuSTC designs a warp-level reduction scheme to reduce atomic operations in tensor decomposition. We compare cuSTC with SPLATT tensor completion library and achieve 48.69×, 24.22×, 4.54 ×, 3.58×, 12.26 × performance speedup on five real-world datasets. © 2022 IEEE.

关键词： Tensors

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Voxelization of Moving Geometries on GPU 24

Voxelization of Moving Geometries on GPU

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作者： Shukla, Rishabh Arora, Nipun Banerjee, Dip Sankar Indian Institute of Technology Department of Mechanical Engineering Jodhpur India Indian Institute of Technology Department of Computer Science and Engineering. Jodhpur India

ISBN: (纸本)9798350319934

Voxelization is a standard representation technique for arbitrary geometries on Cartesian space. It is typically used as a pre-processor for any solver in computational fluid dynamics (CFD) problems. Voxelization typically is a computationally intensive and can consume significant amount of time on complex geometries on grids of high resolutions. In this paper, we propose a novel GPU acceleration method for both surface and solid voxelization aimed at CFD solvers dependent on the popular Lattice Boltzmann Method (LBM). Towards that, we design new mechanisms for data parallelism that can allow efficient SIMT execution on modern GPUs. In contrast to prior works, which typically extract parallelism in the space of the surface geometry, our method works at a finer grain where we extract parallelism in the space of the Cartesian fluid mesh. this leads to higher degree of parallelism which provides significant performance benefits. Our method is able to compute surface voxelization for a benchmark geometry (Stanford Bunny) in 16.8 ms at a grid size of 10243• Our method for solid voxelization on the same geometry takes 1546.3 ms. Our implementation is able to extract around 7.14 TFLOPS single precision (around 50% of peak) performance on a NVIDIA V100 GPU. © 2022 IEEE.

关键词： graphics processing unit

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39th computer graphics International conference on Advances in computer graphics, CGI 2022

39th Computer Graphics International Conference on Advances ...

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39th computer graphics International conference on Advances in computer graphics, CGI 2022

ISBN: (纸本)9783031234729

the proceedings contain 45 papers. the special focus in this conference is on Advances in computer graphics. the topics include: Research on Real-Time Forestry Pest Detection Based on Improved YOLOv5;power Line Detection Based on Feature Fusion Deep Learning Network;wider and Higher: Intensive Integration and Global Foreground Perception for Image Matting;authenticity Identification of Qi Baishi’s Shrimp Painting with Dynamic Token Enhanced Visual Transformer;an Optimized Material Point Method for Soil-Water Coupled Simulation;slimFliud-Net: Fast Fluid Simulation Using Admm Pruning;face Super-Resolution with Better Semantics and More Efficient Guidance;layout and Display of Network Graphs on a Sphere;joint Matrix Factorization and Structure Preserving for Domain Adaptation;graph Adversarial Network with Bottleneck Adapter Tuning for Sign Language Production;facial Landmarks Based Region-Level Data Augmentation for Gaze Estimation;preface;multi-granularity Feature Attention Fusion Network for Image-Text Sentiment Analysis;an Efficient Dense Depth Map Estimation Algorithm Using Direct Stereo Matching for Ultra-Wide-Angle Images;Ad-RMS: Adaptive Regional Motion Statistics for Feature Matching Filtering;visual Indoor Navigation Using Mobile Augmented Reality;cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo;reconstructing the Surface Mesh Representation for Single Neuron;WEmap: Weakness-Enhancement Mapping for 3D Reconstruction with Sparse Image Sequences;comparing Traditional Rendering Techniques to Deep Learning Based Super-Resolution in Fire and Smoke Animations;real-Time Light Field Path Tracing;crowd Simulation with Detailed Body Motion and Interaction;towards Rendering the Style of 20th Century Cartoon Line Art in 3D Real-Time;toward Efficient Image Denoising: A Lightweight Network with Retargeting Supervision Driven Knowledge Distillation;face Detection Algorithm in Classroom Scene Based on Deep Learning;GRVT: Toward Effectiv

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GSpTC: High-Performance Sparse Tensor Contraction on CPU-GPU Heterogeneous Systems 24

GSpTC: High-Performance Sparse Tensor Contraction on CPU-GPU...

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作者： Xiao, Guoqing Yin, Chuanghui Chen, Yuedan Duan, Mingxing Li, Kenli College of Computer Science and Electronic Engineering Hunan University Changsha410082 China National Supercomputing Center in Changsha Changsha410082 China Shenzhen Research Institute Hunan University Shenzhen518000 China

ISBN: (纸本)9798350319934

Many fields of scientific simulation, such as chem-istry and condensed matter physics, are increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this work, we centre around binary sparse tensor contraction (SpTC) which has the challenges of index matching and accumulation. To address and alleviate the above difficulties, we present GSpTC, an efficient element-wise SpTC framework on CPU-GPU heterogeneous systems. GSpTC first introduces a fine-grained index partition based on element-wise tensor con-traction, which facilitates efficient use of the parallel computing power of GPUs. In particular, GSpTC leverages multi-threading parallelism on GPUs for the contraction phase and merge phase, which greatly accelerates the computation phase in sparse tensor contraction. through evaluation of five datasets, we find that the proposed GSpTC achieves an average performance improvement of 74.32% compared to the previous state-of-the-art Sparta framework. © 2022 IEEE.

关键词： graphics processing unit

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EgpuIP: An Embedded GPU Accelerated Library for Image Processing 24

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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作者： Wang, Luhan Jia, Haipeng Zhang, Yunquan Li, Kun Wei, Cunyang Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research China

ISBN: (纸本)9798350319934

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. the image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm;2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm;3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm;4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to 19×88×, and 3× speedup respectively. © 2022 IEEE.

关键词： graphics processing unit

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Accelerating small matrix multiplications by adaptive batching strategy on GPU 24

Accelerating small matrix multiplications by adaptive batchi...

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作者： Zhang, Yaqing Wang, Yaobin Mo, Zhangbin Zhou, Yong Sun, Tao Xu, Guang Xing, Chaojun Yang, Liang School of Computer Science and Technology Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education Southwest University of Science and Technology Mianyang China Huawei Technologies Co.Ltd Shenzhen China Loongson Technology Corporation Limited Peking China

ISBN: (纸本)9798350319934

General matrix multiplication (GEMM) is a key operator in a wide range of fields such as machine learning, scientific computing, and signal processing. In practice, the matrix sizes are usually too small to make full use of GPU resources in many applications. To this end, previous work has attempted to batch small GEMMs by designing a CUDA core to process multiple GEMMs simultaneously. It divides the GEMM into multiple tiles, and then process a tile in a thread block. However, the approach only processes one tile per thread block and allocates the number of thread blocks based on the size of the largest matrix in the batch. It will make some thread blocks and threads idle. In this paper, we propose a batching strategy to batch small GEMMs with the consideration of several factors, including tile number, block number, and block size. We process multiple tiles in a thread block. Different tiles may need different block size and block number, we select the optimal scheme adaptively according to the hardware resources occupancy. Our proposed strategy achieves the performance improvement of batched GEMM by improving GPU occupancy. the experimental results show that our batching strategy can achieve about 1.7X speedup on average over the MAGMA and about 1.2X performance improvement on average over the state-of-the-art L-gemm. © 2022 IEEE.

关键词： graphics processing unit

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DTSpMV: An Adaptive SpMV Framework for Graph Analysis on GPUs 24

DTSpMV: An Adaptive SpMV Framework for Graph Analysis on GPU...

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作者： Xiao, Guoqing Zhou, Tao Chen, Yuedan Hu, Yikun Li, Kenli College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China National Supercomputing Center in Changsha Hunan Changsha410082 China Shenzhen Research Institute Hunan University Shenzhen518000 China

ISBN: (纸本)9798350319934

Sparse Matrix and Vector multiplication (SpMV) is one of the core algorithms in various large-scale scientific computing and real-world applications. With the rapid development of AI and big data, the input vector in SpMV becomes more and more sparse in many application fields. Especially in some graph analysis calculations, the sparsity of the input vector will change with the running of the program. At this time, the optimal SpMV kernel may be different, and a single SpMV kernel can no longer meet the acceleration requirements. In this paper, we propose a decision tree-based adaptive SpMV framework, named DTSpMV, that can automatically select appropriate SpMV kernels based on different input data in iterations of graph computation. Based on the analysis of computational patterns and serial and parallel algorithms, we encapsulate six SpMV cores within the proposed framework. We explore machine learning-based kernel selectors in terms of both accuracy and runtime overhead. Experimental results on NVIDIA Tesla P100 GPU show that our adaptive framework achieves the arithmetic average performance improvement of 60% compared to the state-of-the-art SpMV kernel benchmark, and the average ratio of the runtime prediction overhead of the framework to the total computing overhead is 1.9%. © 2022 IEEE.

关键词： graphics processing unit

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Low-Latency Immersive 6D Televisualization with Spherical Rendering 20

Low-Latency Immersive 6D Televisualization with Spherical Re...

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20th IEEE-RAS International conference on Humanoid Robots (HUMANOIDS)

作者： Schwarz, Max Behnke, Sven Univ Bonn Autonomous Intelligent Syst Grp Bonn Germany

ISBN: (纸本)9781728193724

We present a method for real-time stereo scene capture and remote VR visualization that allows a human operator to freely move their head and thus intuitively control their perspective during teleoperation. the stereo camera is mounted on a 6D robotic arm, which follows the operator's head pose. Existing VR teleoperation systems either induce high latencies on head movements, leading to motion sickness, or use scene reconstruction methods to allow re-rendering of the scene from different perspectives, which cannot handle dynamic scenes effectively. Instead, we present a decoupled approach which renders captured camera images as spheres, assuming constant distance. this allows very fast re-rendering on head pose changes while keeping the resulting temporary distortions during head translations small. We present qualitative examples, quantitative results in the form of lab experiments and a small user study, showing that our method outperforms other visualization methods.

关键词： Visualization Head Robot vision systems Humanoid robots Reconstruction algorithms Cameras Rendering (computer graphics)

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Influence of Voltage Deviation and Asymmetry on Transitional Processes in Asynchronous Electric Drive 20

Influence of Voltage Deviation and Asymmetry on Transitional...

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20th IEEE International conference on Modern Electrical and Energy Systems, MEES 2021

作者： Savchenko, Vitaliy Synyavskiy, Oleksandr Dudnyk, Alla Nesvidomin, Andriy National University of Life and Environmental Sciences of Ukraine Department of Electroenergetics Electrotechnics and Electromechanics Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Department of Automatics and Robototechnical Systems Kyiv Ukraine National University of Life and Environmental Sciences of Ukraine Department of Descriptive Geometry Computer Graphics and Design Kyiv Ukraine

ISBN: (纸本)9781665423663

Investigations of electromagnetic and electromechanical transients in an asynchronous electric drive with voltage deviation and asymmetry have been carried out. Analytical expressions are obtained that describe the transients in an asynchronous electric drive with a shock load at voltage deviation and *** the voltage decreases and asymmetries, the electromechanical constant of time increases, and as the voltage increases, electromechanical constant decreases. As a result, in drives with a small electromechanical time constant, increasing the voltage can cause torques and speed to fluctuate, while reducing and asymmetry can eliminate *** electric drives with a large moment of inertia, the transient process is aperiodic. When the voltage is higher than the nominal one, the duration of the transient process decreases, and when the voltage decreases and asymmetries, duration of the transient process *** decrease and asymmetry of the voltage causes a decrease in the steady angular speed compared to the nominal, and an increase in voltage - angular speed increase. © 2021 IEEE.

关键词： Electric drives

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Hybrid conference Experiences in the ARENA 20

Hybrid Conference Experiences in the ARENA

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20th IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Pereira, Nuno Rowe, Anthony Farb, Michael W. Liang, Ivan Lu, Edward Riebling, Eric Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665412988

We propose supporting hybrid conference experiences using the Augmented Reality Edge Network Architecture (ARENA). ARENA is a platform based on web technologies that simplifies the creation of collaborative mixed reality for standard Web Browsers (Chrome, Firefox) in VR, Headset AR/VR Browsers (Magic Leap, Hololens, Oculus Quest 2), and mobile AR (WebXR Viewer for iOS, Chrome with experimental flags for Android, and our own custom WebXR fork for iOS). We use a 3D scan of the conference venue as the backdrop environment for remote users and a model to stage various AR interactions for in-person users. Remote participants can use VR in a browser or a VR headset to navigate the scene. In-person participants can use AR headsets or mobile AR through WebXR browsers to see and hear remote users. ARENA can scale up to hundreds of users in the same scene and provides audio and video with spatial sound that can more closely capture real-world interactions.

关键词： Computing methodologies computer graphics graphics systems and interfaces Mixed / augmented reality Information Interfaces and Presentation [Miscellaneous]

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