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Auto-Tuning Dedispersion for Many-Core Accelerators

Auto-Tuning Dedispersion for Many-Core Accelerators

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International symposium on Parallel and Distributed processing (IPDPS)

作者： Alessio Sclocco Henri E. Bal Jason Hessels Joeri van Leeuwen Rob V. van Nieuwpoort Faculty of Sciences Vrije Universiteit Amsterdam Amsterdam Netherlands ASTRON Netherlands Institute for Radio Astronomy Dwingeloo Netherlands

ISBN: (纸本)9781479938018

Dedispersion is a basic algorithm to reconstruct impulsive astrophysical signals. It is used in high sampling-rate radio astronomy to counteract temporal smearing by intervening interstellar medium. To counteract this smearing, the received signal train must be dedispersed for thousands of trial distances, after which the transformed signals are further analyzed. This process is expensive on both computing and data handling. This challenge is exacerbated in future, and even some current, radio telescopes which routinely produce hundreds of such data streams in parallel. There, the compute requirements for dedispersion are high (petascale), while the data intensity is extreme. Yet, the dedispersion algorithm remains a basic component of every radio telescope, and a fundamental step in searching the sky for radio pulsars and other transient astrophysical objects. In this paper, we study the parallelization of the dedispersion algorithm on many-core accelerators, including GPUs from AMD and NVIDIA, and the Intel Xeon Phi. An important contribution is the computational analysis of the algorithm, from which we conclude that dedispersion is inherently memory-bound in any realistic scenario, in contrast to earlier reports. We also provide empirical proof that, even in unrealistic scenarios, hardware limitations keep the arithmetic intensity low, thus limiting performance. We exploit auto-tuning to adapt the algorithm, not only to different accelerators, but also to different observations, and even telescopes. Our experiments show how the algorithm is tuned automatically for different scenarios and how it exploits and highlights the underlying specificities of the hardware: in some observations, the tuner automatically optimizes device occupancy, while in others it optimizes memory bandwidth. We quantitatively analyze the problem space, and by comparing the results of optimal auto-tuned versions against the best performing fixed codes, we show the impact that auto-tuning ha

关键词： Artificial intelligence Delays Radio astronomy Equations Algorithm design and analysis Dispersion Extraterrestrial measurements

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DMTCP: Transparent checkpointing for cluster computations and the desktop

DMTCP: Transparent checkpointing for cluster computations an...

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International symposium on Parallel and Distributed processing (IPDPS)

作者： Jason Ansel Kapil Arya Gene Cooperman Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA College of Computer and Information Science Northeastern University Boston MA USA

DMTCP (distributed multithreaded checkpointing) is a transparent user-level checkpointing package for distributed applications. Checkpointing and restart is demonstrated for a wide range of over 20 well known applications, including MATLAB, Python, TightVNC, MPICH2, OpenMPI, and runCMS. RunCMS runs as a 680 MB image in memory that includes 540 dynamic libraries, and is used for the CMS experiment of the Large Hadron Collider at CERN. DMTCP transparently checkpoints general cluster computations consisting of many nodes, processes, and threads; as well as typical desktop applications. On 128 distributed cores (32 nodes), checkpoint and restart times are typically 2 seconds, with negligible run-time overhead. Typical checkpoint times are reduced to 0.2 seconds when using forked checkpointing. Experimental results show that checkpoint time remains nearly constant as the number of nodes increases on a medium-size cluster. DMTCP automatically accounts for fork, exec, ssh, mutexes/ semaphores, TCP/IP sockets, UNIX domain sockets, pipes, ptys (pseudo-terminals), terminal modes, ownership of controlling terminals, signal handlers, open file descriptors, shared open file descriptors, I/O (including the readline library), shared memory (via mmap), parent-child process relationships, pid virtualization, and other operating system artifacts. By emphasizing an unprivileged, user-space approach, compatibility is maintained across Linux kernels from 2.6.9 through the current 2.6.28. Since DMTCP is unprivileged and does not require special kernel modules or kernel patches, DMTCP can be incorporated and distributed as a checkpoint-restart module within some larger package.

关键词： Checkpointing Kernel Packaging Libraries Sockets MATLAB Collision mitigation Large Hadron Collider Yarn Runtime

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Quantum Annealing based SVM Outperforms Classical SVM in Cloud Feature Remote Sensing image Classification

Quantum Annealing based SVM Outperforms Classical SVM in Clo...

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Remote Sensing symposium (InGARSS), 2020 ieee India Geoscience and

作者： Indranil Misra S. Manthira Moorthi Debajyoti Dhar Signal and Image Processing Area Space Applications Centre Indian Space Research Organisation (ISRO) Ahmedabad India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The increasing complexity and volume of remote sensing data necessitate advanced computational techniques for efficient and accurate image classification. This study explores the efficacy of Quantum Annealing based Support Vector Machines (QASVM) in comparison to classical Support Vector Machines (SVM) for classifying cloud features in remote sensing images. Quantum annealing, leveraging principles of quantum mechanics, promises to solve optimization problems more efficiently and accurately than classical methods, particularly in high-dimensional data spaces typical of remote sensing applications. Our experimental framework involved the classification of cloud features using a comprehensive dataset of Indian Nano Satellite (INS-2B) Nano-MX satellite imagery. The QA-SVM is implemented on a D-Wave quantum annealer based on the Adiabatic Quantum Computation (AQC) model, while the classical SVM utilizes a traditional computing setup. Performance metrics, including overall accuracy and F1 score, are evaluated. The results demonstrated that QA-SVM achieved higher overall accuracy and F1 score compared to its classical counterpart on the Nano-MX test image. It is found that the overall accuracy of QA-SVM is increased by 6.7% while the F1 score is increased by 4.5% with respect to classical SVM. This performance gain is attributed to the quantum annealer’s ability to explore a vast solution space more effectively, avoiding local minima that often hinder classical SVMs.

关键词： Support vector machines Annealing Accuracy Satellites Quantum mechanics Performance gain Quantum annealing Satellite images Remote sensing image classification

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Ultrasonic Imaging and Flaw Detection with Optimized Convolutional Transformer Neural Networks

Ultrasonic Imaging and Flaw Detection with Optimized Convolu...

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ieee symposium (IUS) Ultrasonics

作者： Xin Zhang Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago Illinois U.S.A.

Flaw detection using the ultrasonic imaging technique has been widely used for Structural Health Monitoring (SHM). Ultrasonic testing has the advantages of one-sided measurement, high penetration depth, and inspection accuracy. Artificial intelligence (AI) such as Deep Learning (DL) methods can automate the inspection process with high reliability for imaging and SHM. Neural Networks (NNs) and DL methods can detect flaws using ultrasonic images with high accuracy but suffer extensive computational costs for training and deployment. In this study, we propose a lightweight transformer NN, UFDCTNet: Ultrasonic Flaw Detection Convolutional Transformer Neural Network (TNN), optimized with data-efficient Convolutional NN (CNN) for flaw detection using ultrasonic imaging. TNN utilizes the self-attention network architecture that learns global representations and allows high parallelism in computation resulting in reduced training time. CNNs learn local representations with fewer parameters because of inherent spatial inductive bias. This UFDCTNet utilizes the advantages of CNNs to learn spatially local representations resulting in fewer model parameters for fast training. For performance analysis, we trained data-efficient TNN and CNN using ultrasonic images to detect flaws. To examine training results, NNs are trained with the USimgAIST dataset consisting of 7000 experimental B-scan images representing without-flaw and with-flaw cases. A pulsed laser ultrasonic scanning system was used to collect these B-scan images from 17 stainless steel specimen plates with various types of flaws and some plates without any damage.

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Efficient Vision Transformer Deployment on Google Coral Through Knowledge Distillation

Efficient Vision Transformer Deployment on Google Coral Thro...

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East-West Design & Test symposium (EWDTS)

作者： Vazgen Melikyan Tigran Khachatryan Davit Galstyan Eduard Harutyunyan National Polytechnic University of Armenia Synopsys Armenia CJSC Yerevan Armenia Synopsys Armenia Educational Department National Polytechnic University of Armenia Yerevan Armenia

ISBN: (数字)9798331515768

ISBN: (纸本)9798331515775

With the development of AI, the models are becoming more complex and resource intensive. There is a need to create smaller but at the same time satisfactory performance models that will run on resource-constrained devices. This paper explores enhancing the efficiency of the MobileNetV2 architecture using the power of the Vision Transformer through knowledge distillation. The goal was to leverage the complex insights of the Vision Transformer to improve the capabilities of MobileNetV2. As a result, the refined model outperformed its original version by about 5% (from 81.7% to 86.5%). The model was then placed on the Coral Dev Board edge device and due to the weight adjustments, there was a slight decrease in accuracy to 84.3%. Despite this, it reduced its processing latency by more than half, from 0.0076 seconds to 0.0037 seconds, proving a valuable approach for efficient and real-time edge computing applications.

关键词： Performance evaluation Computer vision Accuracy Quantization (signal) computational modeling Transformers Real-time systems Internet Artificial intelligence Edge computing

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Benchmarking End-to-End Behavioural Cloning on Video Games

Benchmarking End-to-End Behavioural Cloning on Video Games

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ieee symposium on computational intelligence and Games, CIG

作者： Anssi Kanervisto Joonas Pussinen Ville Hautamäki School of Computing University of Eastern Finland Joensuu Finland

ISBN: (数字)9781728145334

ISBN: (纸本)9781728145341

Behavioural cloning, where a computer is taught to perform a task based on demonstrations, has been successfully applied to various video games and robotics tasks, with and without reinforcement learning. This also includes end-to-end approaches, where a computer plays a video game like humans do: by looking at the image displayed on the screen, and sending keystrokes to the game. As a general approach to playing video games, this has many inviting properties: no need for specialized modifications to the game, no lengthy training sessions and the ability to re-use the same tools across different games. However, related work includes game-specific engineering to achieve the results. We take a step towards a general approach and study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010), by using human demonstrations as training data. Our results show that these agents cannot match humans in raw performance but do learn basic dynamics and rules. We also demonstrate how the quality of the data matters, and how recording data from humans is subject to a state-action mismatch, due to human reflexes.

关键词： Games Cloning Training Task analysis Delays Tools signal resolution

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Enhanced Interval Approach for encoding words into interval type-2 fuzzy sets and convergence of the word FOUs

Enhanced Interval Approach for encoding words into interval ...

引用

ieee International Conference on Fuzzy Systems (FUZZ-ieee)

作者： Simon Coupland Jerry M. Mendel Dongrui Wu Centre of Computational Intelligence De Montfort University Leicester UK Signal and Image Processing Institute Ming Hsieh Department of Electrical Engineering University of Southern California Los Angeles CA USA Institute of Creative Technologies and the Signal Analysis and Interpretation Laboratory Viterbi School of Engineering University of Southern California Los Angeles CA USA

The Interval Approach (IA) [4] is a method for synthesizing an interval type-2 fuzzy set (IT2 FS) model for a word from data that are collected from a group of subjects. A key assumption made by the IA is: each person's data interval is random and uniformly distributed. This means, of course, that the IT2 FS model for the word is random. Consequently, one can question whether or not the IT2 FS model for the word converges in a stochastic sense. This paper focuses on this question. As a part of our study, we have had to modify some steps of the IA, the resulting being an Enhanced IA (EIA). The paper shows by means of some simulations, that the IT2 FS word models that are obtained from the EIA are converging in a mean-square sense. This provides substantial credence for using the EIA to obtain T2 FS word models.

关键词： Convergence Frequency selective surfaces Mathematical model Stochastic processes Fuzzy sets Probability distribution Context

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TrafficEd: Deployment and Management System of Edge AI Cameras

TrafficEd: Deployment and Management System of Edge AI Camer...

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ieee symposium on Network Operations and Management

作者： Guan-Wen Chen Yi-Hsiu Lin Tsì-Uí İk Department of Computer Science College of Computer Science National Yang Ming Chiao Tung University Hsinchu City Taiwan

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Artificial intelligence (AI) cameras are edge devices with embedded graphics processing units that can run lightweight deep learning models. In traffic management applications, traffic flow and traffic incidents can be detected from roadside images with the use of AI cameras, and only detected high-level information is sent to the server to minimize the use of network bandwidth and server resources. However, because edge devices are computationally limited, models should be optimized before they are deployed to these AI cameras. In addition, environment-related parameters must be configured appropriately after model deployment. Thus, an AI camera management system is required. Consequently, in this study, we designed a deployment and management system for AI cameras; this system can perform model optimization and parameter configuration with ease. The main functions of this system involve 1) automatic modeling and code transfer, 2) the remote deployment of deep learning models, 3) the remote configuration of relevant applications, and 4) the presentation of analytical results on a graphical user interface. The performance of the developed system was investigated by using it to deploy traffic analysis models and visualize analysis results. The experimental results indicate that this system achieved all of its design goals.

关键词： Deep learning Analytical models computational modeling image edge detection Cameras Turning Software

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A Noval Feature via Color Quantisation for Fake Audio Detection

A Noval Feature via Color Quantisation for Fake Audio Detect...

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International symposium on Chinese Spoken Language processing

作者： Zhiyong Wang Xiaopeng Wang Yuankun Xie Ruibo Fu Zhengqi Wen Jianhua Tao Yukun Liu Guanjun Li Xin Qi Yi Lu Xuefei Liu Yongwei Li Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Information and Communication Engineering Communication University of China Beijing National Research Center for Information Science and Technology Tsinghua University Department of Automation Tsinghua University

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

In the field of deepfake detection, previous studies focus on using reconstruction or mask and prediction methods to train pre-trained models, which are then transferred to fake audio detection training where the encoder is used to extract features, such as wav2vec2.0 and Masked Auto Encoder. These methods have proven that using real audio for reconstruction pre-training can better help the model distinguish fake audio. However, the disadvantage lies in poor interpretability, meaning it is hard to intuitively present the differences between deepfake and real audio. This paper proposes a noval feature extraction method via color quantisation which constrains the reconstruction to use a limited number of colors for the spectral image-like input. The proposed method ensures reconstructed input differs from the original, which allows for intuitive observation of the focus areas in the spectral reconstruction. Experiments conducted on the ASVspoof2019 dataset demonstrate that the proposed method achieves better classification performance compared to using the original spectral as input and pretraining the recolor network can also benefit the fake audio detection.

关键词： Training Deepfakes Quantization (signal) image color analysis Predictive models Feature extraction image reconstruction

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Identification of the nonlinear dynamic model of sailplanes involving state estimation and image processing for actuator signal computation

Identification of the nonlinear dynamic model of sailplanes ...

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International symposium on Intelligent Systems and Informatics (SISY)

作者： Loránd Lukács Béla Lantos Department of Control Engineering and Information Technology Budapest University of Technology and Economics Budapest Hungary

The primary scope of the paper lies on the identification of an aircraft's nonlinear dynamic model. It is assumed that the aircraft has no inbuilt navigational system, nor any sensors mounted on its control surfaces. The flight of the airplane is influenced by the control column and pedals manipulated by the pilot whose positions can only visually be observed. This situation can often occur in the first phase of control system development of airplanes. Hence, for the time of data logging, an external sensory system (GPS, IMU) and a camera system were deployed on the airplane supporting the collection of flight data for state estimation and model identification. An earlier paper discussed the computation of the actuator signals thus the paper deals mainly with the state estimation and model identification. State estimation is based on two-level Extended Kalman Filters with additional correction in an external loop. System identification is based on the dynamical equations of rigid body with additional weighted nonlinear terms for 3D forces and torques. Wind effects are taken into consideration. From the inertial parameters only the mass is known. Dominating nonlinear functions in the force and torque model are selected by using hypotheses tests. The results are presented for a real sailplane using flight data.

关键词： Mathematical model Atmospheric modeling Aircraft navigation computational modeling Aircraft State estimation Equations

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