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ISC High Performance 2025 Research Paper Proceedings (40th International Conference)

作者： Jie Ren Hatem Ltaief Sameh Abdulah David E. Keyes Division of Computer Electrical and Mathematical Sciences and Engineering (CEMSE) King Abdullah University of Science and Technology Jeddah Saudi Arabia

ISBN: (数字)9783982633619

This paper explores the performance optimization of out-of-core (OOC) Cholesky factorization on shared-memory systems equipped with multiple GPUs 1 1 Code release available at https://***/ecrc/ooc-cholesky. We employ fine-grained computational tasks to expose concurrency while creating opportunities to overlap data movement asynchronously with computations, especially when dealing with matrices that cannot fit in the GPU memory. We leverage the directed acyclic graph of the task-based Cholesky factorization and map it onto a static scheduler that promotes data reuse while supporting strategies for reducing data movement with the CPU host when the GPU memory is exhausted. The CPU-GPU interconnect may become the main performance bottleneck as the gap between the GPU execution rate and the traditional PCIe bandwidth continues to widen. While the surface-to-volume effect of compute-bound kernels partially mitigates the overhead of data motion, deploying mixed-precision (MxP) computations exacerbates the throughput discrepancy. Using static task scheduling, we evaluate the performance capabilities of the new ultra-fast NVIDIA chip interconnect technology, codenamed NVLink-C2C, that constitutes the backbone of the NVIDIA Grace Hopper Superchip (GH200), against a new four-precision (FP64/FP32/FP16/FP8) left-looking Cholesky factorization. We report the performance results of a benchmarking campaign on various NVIDIA GPU generations and interconnects. We highlight 20% performance superiority against cuSOLVER on a single GH200 with FP64, while hiding the cost of OOC task-based Cholesky factorization and we scale almost linearly on four GH200 superchips. With MxP enabled, our statically scheduled four-precision tile-based Cholesky factorization scores a 3X speedup against its FP64-only counterpart, delivering application-worthy FP64 accuracy when modeling a large-scale geospatial statistical application. Our results represent a significant advance in handling large dat

关键词： Technological innovation Accuracy Processor scheduling Memory management Graphics processing units Data transfer Throughput Spatial databases Scheduling Optimization

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用于二维半导体沟道晶体管的HfO_(2)/Sb_(2)O_(3)双层栅极介电层

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science Bulletin 2023年第22期68卷 2684-2686页

作者： Mario Lanza Materials Science and Engineering Program Physical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

The invention of the transistor 75 years ago by American scientists John Bardeen, William Shockley, and Walter Brattain at Bell Labs(New Jersey, USA), was the onset of a technological revolution that has changed the w... 详细信息

关键词： Shockley transistor 二维半导体

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DeeReCT-APA:Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 483-495页

作者： Zhongxiao Li Yisheng Li Bin Zhang Yu Li Yongkang Long Juexiao Zhou Xudong Zou Min Zhang Yuhui Hu Wei Chen Xin Gao King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionThuwal 23955-6900Saudi Arabia Department of Biology Southern University of Science and Technology(SUSTech)Shenzhen 518055China Cancer Science Institute of Singapore Singapore 117599Singapore

Alternative polyadenylation(APA)is a crucial step in post-transcriptional *** bioinformatic studies have mainly focused on the recognition of polyadenylation sites(PASs)in a given genomic sequence,which is a binary classification ***,computational methods for predicting the usage level of alternative PASs in the same gene have been ***,all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into *** address this,here we propose a deep learning architecture,Deep Regulatory Code and Tools for Alternative Polyadenylation(DeeReCT-APA),to quantitatively predict the usage of all alternative PASs of a given *** accommodate different genes with potentially different numbers of PASs,DeeReCT-APA treats the problem as a regression task with a variable-length *** on a convolutional neural network-long short-term memory(CNN-LSTM)architecture,DeeReCT-APA extracts sequence features with CNN layers,uses bidirectional LSTM to explicitly model the interactions among competing PASs,and outputs percentage scores representing the usage levels of all PASs of a *** addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene,we show that our method consistently outperforms other existing methods on three different tasks for which they are trained:pairwise comparison task,highest usage prediction task,and ranking ***,we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA *** code and data are available at https://***/lzx325/DeeReCT-APA-repo.

关键词： Polyadenylation Gene regulation Sequence analysis Deep learning Bioinformatics

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Proposed Biometric Security System Based on Deep Learning and Chaos Algorithms

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computers, Materials & Continua 2023年第2期74卷 3515-3537页

作者： Iman Almomani Walid El-Shafai Aala AlKhayer Albandari Alsumayt Sumayh S.Aljameel Khalid Alissa Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School of Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Applied CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Computer Science Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Nowadays,there is tremendous growth in biometric authentication and cybersecurity ***,the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private ***,designing and implementing robust security algorithms for users’biometrics is still a hot research area to be *** work presents a powerful biometric security system(BSS)to protect different biometric modalities such as faces,iris,and *** proposed BSSmodel is based on hybridizing auto-encoder(AE)network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and ensures their *** employed AE network is unsupervised deep learning(DL)structure used in the proposed BSS model to extract main biometric *** obtained features are utilized to generate two random chaos *** first random chaos matrix is used to permute the pixels of biometric *** contrast,the second random matrix is used to further cipher and confuse the resulting permuted biometric pixels using a two-dimensional(2D)chaotic logisticmap(CLM)*** assess the efficiency of the proposed BSS,(1)different standardized color and grayscale images of the examined fingerprint,faces,and iris biometrics were used(2)comprehensive security and recognition evaluation metrics were *** assessment results have proven the authentication and robustness superiority of the proposed BSSmodel compared to other existing *** example,the proposed BSS succeeds in getting a high area under the receiver operating characteristic(AROC)value that reached 99.97%and low rates of 0.00137,0.00148,and 3516 CMC,2023,vol.74,no.20.00157 for equal error rate(EER),false reject rate(FRR),and a false accept rate(FAR),respectively.

关键词： Biometric security deep learning AE network 2D CLM cybersecurity and authentication applications feature extraction unsupervised learning

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Sound Waveguiding by Spinning: An Avenue toward Unidirectional Acoustic Spinning Fibers

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Physical Review Applied 2023年第4期19卷 L041002-L041002页

作者： Mohamed Farhat Pai-Yen Chen Ying Wu Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electrical and Computer Engineering University of Illinois at Chicago Chicago Illinois 60607 USA Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

Waveguiding in general and acoustic waveguiding in particular are possible under the condition of a transverse “discontinuity” or modulation in the refractive index. We propose a radically different approach that relies on imposing spinning on a column of air, leading to highly modified acoustic refractive indices for specific azimuthal modes. This result may be leveraged to realize not only the airborne acoustic counterpart of an optical fiber, i.e., an acoustic spinning fiber, but also a nonreciprocal unidirectional waveguiding mechanism, reminiscent of an “acoustic Zeeman effect.” The concept is demonstrated in the realm of acoustics, but may be applicable to other wave systems, e.g., in elastodynamics.

关键词： Acoustic wave phenomena Non-reciprocal transmission Waveguides Zeeman effect 1-dimensional systems Acoustic metamaterials Sound wave techniques

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Environmental Temperature Monitoring System Featuring Printed Temperature Sensor and a Flexible, Printed WiFi Antenna

Environmental Temperature Monitoring System Featuring Printe...

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Microwave, Antennas & Circuits (ICMAC), International Conference on

作者： Rana muhammad Bilal Firas Fatani Mohammad Vaseem Sakandar Rauf Atif Shamim Department of Electrical and Computer Engineering CEMSE Division King Abdullah University of Science and Technology (KAUST) Kingdom of Saudi Arabia

ISBN: (数字)9798331518424

ISBN: (纸本)9798331518431

With the increasing need for scalable, energy-efficient sensing solutions in smart cities and buildings, traditional temperature monitoring systems face limitations due to higher manufacturing costs and rigid designs. Printed fabrication offers a flexible and economical alternative that enhances potential for volume deployment, ubiquitous integration and the increased spatial resolution of monitoring. In this work, we present a novel environmental temperature monitoring system that leverages printed fabrication for antenna and sensor for such applications. A high measured sensitivity (i.e. 3.1% Temperature coefficient of resistance) is shown for the designed printed temperature sensor. A measured 93MHz bandwidth coverage is shown for the designed printed, flexible dual-patch array WiFi antenna.

关键词： Temperature sensors Temperature measurement Antenna measurements Fabrication Smart cities Sensors electrical resistance measurement Monitoring Wireless fidelity Antenna arrays

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Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

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Intelligent Automation & Soft Computing 2023年第4期36卷 169-181页

作者： Muhammad Irfan Khalil Ullah Fazal Muhammad Salman Khan Faisal Althobiani Muhammad Usman Mohammed Alshareef Shadi Alghaffari Saifur Rahman Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Software Engineering University of MalakandDir LowerPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG *** an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare *** this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modiﬁed version of mel fre-quency cepstral coefﬁcients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ﬁcient,hurst exponent,and root mean *** state-of-the-art classiﬁers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted ***-based analyses of these classiﬁers have also been considered based on total classi-ﬁcation accuracy,prediction speed(observations/sec),and processing *** proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG *** is used as our main classiﬁer for the detection of noisy channels which gives a total classiﬁcation accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

关键词： Machine learning surface electromyography support vector machine classiﬁcation features

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A Wearable ECG System with Printed Electrodes for Heart Health Monitoring and Diagnosis

A Wearable ECG System with Printed Electrodes for Heart Heal...

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IEEE SENSORS

作者： Sakandar Rauf Rana Muhammad Bilal Mohammad Vaseem Atif Shamim Electrical and Computer Engineering CEMSE King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

The increasing prevalence of cardiovascular diseases worldwide has triggered the need for developing wearable Electrocardiogram (ECG) systems for routine use in daily life. Here, we report the development of a miniaturized wireless ECG monitoring system that is easy to use and low cost. Our innovative wearable ECG system comprises screen-printed ECG electrodes connected using snap connectors to an ECG readout. The screen-printed ECG electrodes comprises of silver nanowires as conductive layer, ECG electrode gel and a silicone adhesive printed on a flexible plastic substrate. The effect of the distance between the two printed electrodes in the form of an ECG strip (5 cm and 8 cm centre-to-centre) is tested by collecting ECG data from volunteers using our custom wearable ECG readout, operated through a generic app. The printed ECG electrodes demonstrated performance similar to commercial ECG electrodes, as confirmed by a cardiologist during a blinded review of the ECG data. Future developments involve testing the system in large populations (adults, kids, infants, and neonates) and validation with gold-standard ECG systems and electrodes.

关键词： Electrodes Wireless communication Pediatrics Wireless sensor networks Electrocardiography Nanowires Biomedical monitoring Monitoring Wearable sensors Testing

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HBC Transmission Policies for Energy Harvesting Empowered ECG Nodes

HBC Transmission Policies for Energy Harvesting Empowered EC...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Abeer Alamoudi Abdulkadir Celik Ahmed M. Eltawil Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal KSA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Internet of Bodies is a body-centric network that connects smart devices to enable real-time monitoring of physiological data, including early detection of cardiovascular diseases through electrocardiograms (ECG) monitoring. In contrast to power-intensive RF transceivers in many ECG wearables, Human Body Communication (HBC) offers an energy-efficient (EE) and secure alternative, utilizing the body as a communication channel. This paper explores the functionality of an IoB sensor node designed for ECG monitoring, leveraging HBC and incorporating energy harvesting techniques to ensure sustained power. It introduces a scheme that utilizes ECG signal characteristics to identify sensing and transmission time durations, and it formulates an optimization problem within a Markov Decision Process framework. Given the battery and computational power constraint of IoB nodes, we propose lightweight and EE policies. Simulations confirm their benefits over myopic policies in terms of maximizing node lifetime and optimizing energy utilization, making them a promising solution for IoB systems operating under dynamic energy arrival rates.

关键词： Radio frequency Electrocardiography Real-time systems Transceivers Stability analysis Sensors Energy harvesting Wearable devices Smart devices Monitoring

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Steering Customized AI Architectures for HPC Scientific Applications 38th

Steering Customized AI Architectures for HPC Scientific Appl...

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38th International Conference on High Performance Computing, ISC High Performance 2023

作者： Ltaief, Hatem Hong, Yuxi Dabah, Adel Alomairy, Rabab Abdulah, Sameh Goreczny, Chris Gepner, Pawel Ravasi, Matteo Gratadour, Damien Keyes, David Division of Computer Electrical and Mathematical Sciences and Engineering Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Jeddah23955 Saudi Arabia Division of Physical Sciences and Engineering Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Jeddah23955 Saudi Arabia Graphcore Gdańsk Poland Warsaw University of Technology Warsaw Poland Paris Observatory Paris France

ISBN: (纸本)9783031320408

AI hardware technologies have revolutionized computational science. While they have been mostly used to accelerate deep learning training and inference models for machine learning, HPC scientific applications do not seem to directly benefit from these specific hardware features unless AI-based components are introduced into their simulation workflows, for instance, as a replacement of their numerical solvers. This paper proposes to take another direction in an attempt to democratize customized AI architectures for HPC scientific computing. The main idea consists in demonstrating how legacy applications can leverage these AI engines after a necessary algorithmic redesign. It is critical that the resulting software implementations map onto the underlying memory-austere hardware architectures to extract the expected performance. To facilitate this process, we promote the matricization technique for restructuring codes (1) by exploiting data sparsity via algebraic compression and (2) by expressing the critical computational phases in terms of tile low-rank matrix-vector multiplications (TLR-MVM) and batch matrix-matrix multiplications (batch GEMM). Algebraic compression enables to reduce memory footprint and to fit into small local cache/memory, while batch execution ensures high occupancy. We highlight how we can steer the Graphcore AI-focused Wafer-on-Wafer Intelligence Processing Units (IPUs) to deliver high performance for both operations. We conduct a performance benchmarking campaign of these two matrix operations that account for most of the elapsed times of four real applications in computational astronomy, seismic imaging, wireless communications, and climate/weather predictions. We report bandwidth and execution rates with speedup factors up to 150X/14X/25X/40X, respectively, on IPUs compared to other systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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