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Research on High-Dimensional Matrix Multiplication in Machine Learning for MEC

Research on High-Dimensional Matrix Multiplication in Machin...

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international conference on Computer and Communications (ICCC)

作者： Fan Ke Li Chen School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Machine learning (ML) algorithms have been widely deployed in mobile edge computing (MEC). Howerver, algorithms in MEC always demands high computational power to process complex models and large datasets in real-time. A critical component of this computational workload is matrix multiplication, a fundamental operation in many ML algorithms, particularly in deep learning. To meet the stringent latency and performance requirements of MEC applications, leveraging Graphics processing Unit (GPU) acceleration for matrix multiplication is essential. GPUs, with their massive parallel processing capabilities, can significantly enhance the efficiency and speed of matrix computations, enabling rapid inference and training at the edge. This acceleration not only reduces the latency but also offloads the central cloud, providing a more distributed and scalable solution for real-time ML applications at the edge. To this end, this paper first introduces the background of using matrix multiplication to accelerate model training speed in ML for MEC, clarifies the significance of accelerating matrix multiplication to ML and MEC, then reviews and experiments on existing schemes for accelerating matrix multiplication and compares and summarizes various schemes from the perspective of different sparsity of matrices on the GPU. Finally, we points out the possible future research directions and technical challenges, and provides the certain reference value for the research in related fields.

关键词： Training Deep learning Machine learning algorithms Multi-access edge computing Reviews Graphics processing units parallel processing Real-time systems Inference algorithms Sparse matrices

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A GPU Accelerated Hyperspectral 3D Convolutional Neural Network Classification at the Edge with Principal Component Analysis Preprocessing 14th

A GPU Accelerated Hyperspectral 3D Convolutional Neural Netw...

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14th international conference on parallel processing and Applied Mathematics, PPAM 2022

作者： De Lucia, Gianluca Lapegna, Marco Romano, Diego CNR Naples80131 Italy Department of Mathematics and Applications University of Naples Federico II Naples80126 Italy

ISBN: (纸本)9783031304446

The Edge Computing paradigm promises to transfer decision-making processes based on artificial intelligence algorithms to the edge of the network without the need to query servers far from the data collection point. Hyperspectral image classification is one of the application fields that can benefit most from the close relationship between Edge Computing and Artificial Intelligence. It consists of a framework of techniques and methodologies for collecting and processing images related to objects or scenes on the Earth’s surface, employing cameras or other sensors mounted on Unmanned Aerial Vehicles. However, the computing performance of the edge devices is not comparable with those of high-end servers, so specific approaches are required to consider the influence of the computing environment on the algorithm development methodology. In the present work, we propose a hybrid technique to make the Hyperspectral Image classification through Convolutional Neural Network affordable on low-power and high-performance sensor devices. We first use the Principal Component Analysis to filter insignificant wavelengths to reduce the dataset dimension;then, we use a process acceleration strategy to improve the performance by introducing a GPU-based form of parallelism. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Principal component analysis

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GPU Optimization of Biological Macromolecule Multi-tilt Electron Tomography Reconstruction Algorithm 1

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19th international conference on Advanced Intelligent Computing Technology and applications (ICIC)

作者： Fu, Zi-Ang Wan, Xiaohua Zhang, Fa Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Three-dimensional (3D) reconstruction in cryo-electron tomography (cryo-ET) plays an important role in studying in situ biological macromolecular structures at the nanometer level. Owing to limited tilt angle, 3D reconstruction of cryo-ET always suffers from a "missing wedge" problem which causes severe accuracy degradation. Multi-tilt reconstruction is an effective method to reduce artifacts and suppress the effect of the missing wedge. As the number of tilt series increases, large size data causes high computation and huge memory overhead. Limited by the memory, multi-tilt reconstruction cannot be performed in parallel on GPUs, especially when the image size reaches 1 K, 2 K, or even larger. To optimize large-scale multi-tilt reconstruction of cryo-ET, we propose a newGPU-based large-scale multi-tilt tomographic reconstruction algorithm (GMSIRT). Furthermore, we design a two-level data partition strategy in GM-SIRT to greatly reduce the memory required in the whole reconstructing process. Experimental results show that the performance of the GM-SIRT algorithm has been significantly improved compared with DM-SIRT, the distributed multi-tilt reconstruction algorithm on the CPU cluster. The acceleration ratio is over 300%, and the memory requirement only decreases to one-third of DM-SIRT when the image size reaches 2 K.

关键词： Graphics processing unit

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Analysis of parallel Graph applications

Analysis of Parallel Graph Applications

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international conference on parallel and distributed Systems (ICPADS)

作者： Funda Atik Serif Yesil Hamza Ouarnoughi Smail Niar Ozcan Ozturk Computer Science and Engineering Department Washington University in St. Louis Doimukh MI USA NVidia Urbana IL USA Department of Computer Science Université Polytechnique Hauts-de-France Valenciennes France LAMIH/CNRS Université Polytechnique Hauts-de-France Valenciennes France Computer Science and Engineering Program Electronics Engineering Program Sabanci University Istanbul Turkey

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Despite the increasing computing power of shared memory systems with high core counts, parallel graph processing frameworks cannot exploit it effectively. The reason behind this is the inherent challenges in parallel graph algorithms, which are efficient management of dynamically created tasks and irregular data access patterns. In this paper, we categorize several popular design choices into three design dimensions: (i) execution mode, (ii) data access pattern, and (iii) work activation. We provide their high-level parallel implementations and analyze various implementations of three representative iterative graph algorithms by considering these design dimensions. To gain a better understanding of design choices, we examine their impacts on performance, communication, scalability, and work efficiency. We also investigate the communication characteristics of the design choices on two state-of-the-art shared-memory platforms by performing micro-architectural analysis. Our microarchitectural analysis reveals that a topology-driven, pull-based model gives up to $20 x$ better performance.

关键词： Analytical models Microarchitecture Scalability Heuristic algorithms Benchmark testing Throughput Iterative algorithms System-on-chip

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Scaling Up Video-to-Audio Conversion: A High-Performance Architecture Approach

Scaling Up Video-to-Audio Conversion: A High-Performance Arc...

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2023 international conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

作者： Vayadande, Kuldeep Pujari, Aishwarya Verma, Vivek Yeole, Abhishek Zawar, Sahil Jamadar, Zoya Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350306927

The vast array of programming techniques for audio and video files, large volumes of data, and different other essential components are all demonstrated by the quickly developing industry of audio and video publication. The digital publication company found it incredibly difficult to swiftly and effectively convert to a single coding format when faced with vast and varied data. This research suggests a distributed online audio and video format change stage with a B/S structure in response to demand and the current circumstance. It is constructed using the open source and cost-free FFMPEG format conversion tool in conjunction with the development architecture of React, Python, and JavaScript. The effective audio and video format change system created in this work is develop on the Java programming language. Its "Front end display system,""core scheduling backend server,"and "conversion backend server"are all built on Java. This paper presents a comprehensive study of the key technologies and design tenets utilised to develop platform architecture. The usage of a platform for batch audio and video format change may considerably increase the conversion effectiveness of audio and video files while lowering the difficulty of the process as compared to a standard audio and video conversion scheme. The central idea in this study has a number of real-world applications and has been successfully applied to the processing of huge file batches. The process of extracting audio from a video clip and saving it as a distinct audio file is known as video to audio conversion. People prefer to listen to audio content on the go, which is driving up demand for video to audio conversion in today's multimedia world. Technology improvements have made it feasible to convert video to audio more quickly and effectively while maintaining the audio's quality. Users may easily and quickly extract audio from videos, thanks to the speedy conversion process, so they can listen to their preferred p

关键词： Video signal processing

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Evaluating Active-learning Based Performance Prediction of parallel applications

Evaluating Active-learning Based Performance Prediction of P...

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IEEE international conference on e-Science and Grid Computing

作者： Shivam Aggarwal Preeti Malakar Oracle India Pvt. Ltd. India Indian Institute of Technology Kanpur India

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Many scientific applications use Message Passing Interface for distributed-memory parallelism. These applications simulate complex physical phenomena and are routinely executed on supercomputers and high performance compute clusters. The applications may run for several hours or days and there may be long queue waiting times on the supercomputers. Often the application developers are unable to correctly estimate the runtime of their application for a new configuration and thus may overestimate the runtime requirements. This may increase the queue wait times along with sub-optimal job scheduling decisions. Thus accurate prediction of the performance of an application (performance modeling) is helpful. We have developed statistical models for performance prediction of MPI applications based on active learning algorithms and are able to achieve low Median Absolute Percentage Error (MdAPE) in many cases. We used five scientific applications and benchmarks – HPCG, LULESH, miniAMR, miniMD, and miniFE. We predict the execution times of these applications using various data sizes on up to 512 cores of the PARAM Sanganak supercomputer at IIT Kanpur. The statistical models gave MdAPE of 10.6, 3.5, 8.21, 12.62, and 32.25% for HPCG, LULESH, miniAMR, miniMD, and miniFE respectively.

关键词： Runtime Accuracy Message passing Predictive models parallel processing Benchmark testing Prediction algorithms

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Cost-aware & Fault-tolerant Geo-distributed Edge Computing for Low-latency Stream processing 7

Cost-aware & Fault-tolerant Geo-distributed Edge Computing f...

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7th IEEE international conference on Collaboration and Internet Computing (CIC)

作者： Xu, Jinlai Palanisamy, Balaji Univ Pittsburgh Sch Comp & Informat Pittsburgh PA 15213 USA

ISBN: (纸本)9781665416252

The number of Internet-of-Things (IoT) devices is rapidly increasing with the growth of IoT applications in various domains. As IoT applications have a strong demand for low latency and high throughput computing, stream processing using edge computing resources is a promising approach to support low latency processing of large-scale data. Edge-based stream processing extends the capability of cloud-based stream processing by processing the data streams near the edge of the network. In this vision paper, we discuss a distributed stream processing framework that optimizes the performance of stream processing applications through a careful allocation of geo-distributed computing and network resources available in edge computing environments. The framework includes key optimizations in both the platform layer and the infrastructure layer. While the platform layer is responsible for converting the user program into a stream processing physical plan and optimizing the physical plan and operator placement, the infrastructure layer is responsible for provisioning geo-distributed resources to the platform layer. The framework optimizes the performance of stream query processing at the platform layer through its careful consideration of data locality and resource constraints during physical plan generation and operator placement and by incorporating resilience to deal with failures. The framework also includes techniques to dynamically determine the level of parallelism to adapt to changing workload conditions. At the infrastructure layer, the framework includes a novel model for allocating computing resources in edge and geo-distributed cloud computing environments by carefully considering latency and cost. End users benefit from the platform through reduced cost and improved user experience in terms of response time and latency.

关键词： Internet of things

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Impact of CUDA and OpenCL on parallel and distributed Computing 8

Impact of CUDA and OpenCL on Parallel and Distributed Comput...

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8th international conference on Electrical and Electronics Engineering (ICEEE)

作者： Abu Asaduzzaman Trent, Alec Osborne, S. Aldershof, C. Sibai, Fadi N. Wichita State Univ Elect Engn & Comp Sci Dept Wichita KS 67260 USA Prince Mohammad Bin Fahd Univ Dept Comp Engn Al Khobar Saudi Arabia

ISBN: (纸本)9781665440714

Along with high performance computer systems, the Application Programming Interface (API) used is crucial to develop efficient solutions for modern parallel and distributed computing. Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL) are two popular APIs that allow General Purpose Graphics processing Unit (GPGPU, GPU for short) to accelerate processing in applications where they are supported. This paper presents a comparative study of OpenCL and CUDA and their impact on parallel and distributed computing. Mandelbrot set (represents complex numbers) generation, Marching Squares algorithm (represents embarrassingly parallelism), and Bitonic Sorting algorithm (represents distributed computing) are implemented using OpenCL (version 2.x) and CUDA (version 9.x) and run on a Linux-based High Performance Computing (HPC) system. The HPC system uses an Intel i7-9700k processor and an Nvidia GTX 1070 GPU card. Experimental results from 25 different tests using the Mandelbrot Set generation, the Marching Squares algorithm, and the Bitonic Sorting algorithm are analyzed. According to the experimental results, CUDA performs better than OpenCL (up to 7.34x speedup). However, in most cases, OpenCL performs at an acceptable rate (CUDA speedup is less than 2x).

关键词： CUDA high performance computing OpenCL parallel and distributed computing performance analysis

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MLPs: Efficient Training of MiniGo on Large-scale Heterogeneous Computing System

MLPs: Efficient Training of MiniGo on Large-scale Heterogene...

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international conference on parallel and distributed Systems (ICPADS)

作者： Peng Qiao Zhouyu He Rongchun Li Jingfei Jiang Yong Dou Dongsheng Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

Deep Reinforcement Learning has been successfully applied in various applications and achieved impressive performance compared with previous traditional methods but suffers from high computation cost and long training time. MLPerf takes deep reinforcement learning as one of the benchmark tracks and provides a single node training version of MiniGo as a reference. A key challenge is to achieve efficient MiniGo training on a large-scale computing system. According to the training computation pattern in MiniGo and the characteristics of our large-scale heterogeneous computing system, we propose a MultiLevel parallel strategy, MLPs, including task-level parallelism between nodes, CPU-DSP heterogeneous parallelism, and DSP multi-core parallelism. The proposed method reduces the overall execution time from 43 hours to 16 hours while scaling the node size from 1067 to 4139. The scaling efficiency is 69.1%. According to our fitting method, the scaling efficiency is 46.5% when scaling to 8235 nodes. The experimental results show that the proposed method achieves the efficient training of MiniGo on the largescale heterogeneous computing system.

关键词： Training Deep learning Neural networks Memory management Reinforcement learning parallel processing Programming

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ReFrAEN: a Reconfigurable Vibration Analysis Framework for Constrained Sensor Nodes 17

ReFrAEN: a Reconfigurable Vibration Analysis Framework for C...

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17th Annual IEEE international conference on distributed Computing in Sensor Systems (IEEE DCOSS)

作者： Thangarajan, Ashok Samraj Yang, Fan Joosen, Wouter Michiels, Sam Hughes, Danny Katholieke Univ Leuven Imec DistriNet Leuven Belgium

ISBN: (纸本)9781665439299

Vibration monitoring uses data gathered from accelerometers to study kinetic phenomena in applications such as: structural health monitoring and predictive maintenance. The Internet of Things (IoT) has the potential to greatly expand the range and scope of vibration monitoring applications by delivering long-life wireless sensors that can be cost-effectively embedded in hard to reach places such as;within machines, infrastructure or the built environment. However, achieving this vision is difficult due to the stringent resource constraints of contemporary IoT devices and networks. This has led the research community to develop a creative range of application-specific near-sensor processing firmware. However, systematic support for generic vibration monitoring on resource-poor IoT networks remains an open problem. We tackle this challenge by introducing ReFrAEN, a software framework that efficiently enables a wide range of vibration monitoring applications on IoT networks. ReFrAEN achieves this through a deeply configurable combination of compression techniques and data processing algorithms. These features allow end-users to effectively trade-off between resource consumption and data resolution in order to meet battery life constraints while preserving sufficient data quality to support the target application. Our evaluation shows that ReFrAEN is capable of identifying bearing faults, while dramatically improving battery lifetime and reducing latency in comparison to prior approaches.

关键词： IoT Near Sensor processing Vibration Monitoring

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