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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Tal Kadosh Niranjan Hasabnis Timothy Mattson Yuval Pinter Gal Oren Department of Computer Science Ben-Gurion University Israel Israel Atomic Energy Commission Intel Labs United States Scientific Computing Center Nuclear Research Center - Negev Israel Department of Computer Science Technion - Israel Institute of Technology Israel

In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-core CPUs. OpenMP stands out as a popular choice due to its simplicity and portability, offering a directive-driven approach for shared-memory parallel programming. Despite its wide adoption, however, there is a lack of comprehensive data on the actual usage of OpenMP constructs, hindering unbiased insights into its popularity and evolution. This paper presents a statistical analysis of OpenMP usage and adoption trends based on a novel and extensive database, HPCORPUS, compiled from GitHub repositories containing C, C++, and Fortran code. The results reveal that OpenMP is the dominant parallel programming model, accounting for 45% of all analyzed parallel APIs. Furthermore, it has demonstrated steady and continuous growth in popularity over the past decade. Analyzing specific OpenMP constructs, the study provides in-depth insights into their usage patterns and preferences across the three languages. Notably, we found that while OpenMP has a strong “common core” of constructs in common usage (while the rest of the API is less used), there are new adoption trends as well, such as simd and target directives for accelerated computing and task for irregular parallelism. Overall, this study sheds light on OpenMP's significance in HPC applications and provides valuable data for researchers and practitioners. It showcases OpenMP's versatility, evolving adoption, and relevance in contemporary parallel programming, underlining its continued role in HPC applications and beyond. These statistical insights are essential for making informed decisions about parallelization strategies and provide a foundation for further advancements in parallel programming models and techniques. HPCORPUS, as well as the analysis scripts and raw results, are available at: https://***/scientific-computing-Lab-NRCN/HP

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Quantifying OpenMP: Statistical Insights into Usage and Adoption

arXiv

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arXiv 2023年

作者： Kadosh, Tal Hasabnis, Niranjan Mattson, Timothy Pinter, Yuval Oren, Gal Department of Computer Science Ben-Gurion University Israel Israel Atomic Energy Commission Intel Labs United States Scientific Computing Center Nuclear Research Center Negev Israel Department of Computer Science Technion - Israel Institute of Technology Israel

In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-core CPUs. OpenMP stands out as a popular choice due to its simplicity and portability, offering a directive-driven approach for shared-memory parallel programming. Despite its wide adoption, however, there is a lack of comprehensive data on the actual usage of OpenMP constructs, hindering unbiased insights into its popularity and evolution. This paper presents a statistical analysis of OpenMP usage and adoption trends based on a novel and extensive database, HPCORPUS, compiled from GitHub repositories containing C, C++, and Fortran code. The results reveal that OpenMP is the dominant parallel programming model, accounting for 45% of all analyzed parallel APIs. Furthermore, it has demonstrated steady and continuous growth in popularity over the past decade. Analyzing specific OpenMP constructs, the study provides in-depth insights into their usage patterns and preferences across the three languages. Notably, we found that while OpenMP has a strong "common core" of constructs in common usage (while the rest of the API is less used), there are new adoption trends as well, such as simd and target directives for accelerated computing and task for irregular parallelism. Overall, this study sheds light on OpenMP's significance in HPC applications and provides valuable data for researchers and practitioners. It showcases OpenMP's versatility, evolving adoption, and relevance in contemporary parallel programming, underlining its continued role in HPC applications and beyond. These statistical insights are essential for making informed decisions about parallelization strategies and provide a foundation for further advancements in parallel programming models and techniques. HPCORPUS, as well as the analysis scripts and raw results, are available at: https://***/scientific-computing-Lab-NRCN/HP

关键词： Parallel programming

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Alleviating Deduplication-oriented Fragmentation of SSDs by Considering File Hotness and Popularity

Alleviating Deduplication-oriented Fragmentation of SSDs by ...

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International SoC Design Conference, ISOCC

作者： Lee Chang Chien-Chung Ho Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Department of Computer Science and Information Engineering National Cheng Kung University High Performance and Scientific Computing Center National Taiwan University Mohamed bin Zayed University of Artificial Intelligence

Data deduplication is a highly efficient method for data storage, providing significant benefits in terms of storage utilization and cost reduction in large-scale storage systems. This research introduces a novel approach to dynamic chunk allocation in deduplication systems, aiming to minimize fragmentation and enhance read performance by leveraging supported SSD commands. The write strategy is intelligently determined based on the popularity of chunks within each die, optimizing overall system performance. Additionally, we introduce a read/write unit based on the plane level to harness the internal parallelism of SSDs. Through extensive simulations, our proposed design demonstrates an average performance improvement of 18.8%, highlighting our approach’s effectiveness and impact.

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Improved imaging by invex regularizers with global optima guarantees 22

Improved imaging by invex regularizers with global optima gu...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Samuel Pinilla Tingting Mu Neil Bourne Jeyan Thiyagalingam Scientific Computing Department Science and Technology Facilities Council Harwell UK and University of Manchester at Harwell UK Computer Science University of Manchester UK University of Manchester at Harwell UK Scientific Computing Department Science and Technology Facilities Council Harwell UK

ISBN: (纸本)9781713871088

Image reconstruction enhanced by regularizers, e.g., to enforce sparsity, low rank or smoothness priors on images, has many successful applications in vision tasks such as computer photography, biomedical and spectral imaging. It has been well accepted that non-convex regularizers normally perform better than convex ones in terms of the reconstruction quality. But their convergence analysis is only established to a critical point, rather than the global optima. To mitigate the loss of guarantees for global optima, we propose to apply the concept of invexity and provide the first list of proved invex regularizers for improving image reconstruction. Moreover, we establish convergence guarantees to global optima for various advanced image reconstruction techniques after being improved by such invex regularization. To the best of our knowledge, this is the first practical work applying invex regularization to improve imaging with global optima guarantees. To demonstrate the effectiveness of invex regularization, numerical experiments are conducted for various imaging tasks using benchmark datasets.

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Correction: Web service location‑allocation using discrete NSGA‑II with matrix based genetic operations and a repair mechanism

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Journal of Ambient Intelligence and Humanized computing 2023年第1期15卷 797-798页

作者： Verma, Shanu Pant, Millie Snasel, Vaclav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee India Centre for Artificial Intelligence and Data Science Indian Institute of Technology Roorkee Roorkee India Department of Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic

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Vision Language Transformation for Medical Image Captioning: Comparison of Four Pretrained CNN Networks

Vision Language Transformation for Medical Image Captioning:...

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International Conference on computer Theory and Applications (ICCTA)

作者： Salma Elgayar Safwat Hamad El-Sayed M. El-Horbaty Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Business Analytics Department School of Economic and Business Administration (SEBA)، Saint Mary’s College of California Moraga CA USA Scientific Computing Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

Accurately interpreting medical images is crucial for effective clinical diagnoses, and the manual description of these images poses significant challenges. Medical image captioning bridges the gap between visual data and natural language, enabling the extraction of critical information from diverse medical datasets. This study introduces an innovative vision-language transformer approach that evaluates the performance of four powerful pre-trained convolutional neural networks (CNNs): InceptionV3, DenseNet121, ResNet50, and VGG16. By augmenting these networks with additional layers to enhance their trainability for feature extraction, we subsequently input the extracted features into a transformer model for caption generation. Our experiments utilized the Benchmark Radiography Captions (RGC) dataset, which encompasses various medical imaging modalities, including X-rays, MRIs, CT scans, and ultrasound images. To our knowledge, this is the first application of the RGC dataset for medical image captioning. We systematically compare the performance of the CNNs as feature extractors, assessing how well their output facilitates the transformer model's captioning capabilities. The source code for our approach is publicly available, promoting further research in the field of medical image captioning.

关键词： Radiography Visualization Ultrasonic imaging Source coding X-rays Feature extraction Transformers Convolutional neural networks Medical diagnostic imaging Residual neural networks

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Portability and Scalability of OpenMP Offloading on State-of-the-art Accelerators

arXiv

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arXiv 2023年

作者： Fridman, Yehonatan Tamir, Guy Oren, Gal Department of Computer Science Ben-Gurion University of the Negev Israel Department of Physics Nuclear Research Center Negev Israel Scientific Computing Center Nuclear Research Center Negev Israel Intel Corporation United States Department of Computer Science Technion - Israel Institute of Technology

Over the last decade, most of the increase in computing power has been gained by advances in accelerated many-core architectures, mainly in the form of GPGPUs. While accelerators achieve phenomenal performances in various computing tasks, their utilization requires code adaptations and transformations. Thus, OpenMP, the most common standard for multi-threading in scientific computing applications, introduced offloading capabilities between host (CPUs) and accelerators since v4.0, with increasing support in the successive v4.5, v5.0, v5.1, and the latest v5.2 versions. Recently, two state-of-the-art GPUs - the Intel Ponte Vecchio Max 1100 and the NVIDIA A100 GPUs - were released to the market, with the oneAPI and NVHPC compilers for offloading, correspondingly. In this work, we present early performance results of OpenMP offloading capabilities to these devices while specifically analyzing the portability of advanced directives (using SOLLVE's OMPVV test suite) and the scalability of the hardware in representative scientific mini-app (the LULESH benchmark). Our results show that the coverage for version 4.5 is nearly complete in both latest NVHPC and oneAPI tools. However, we observed a lack of support in versions 5.0, 5.1, and 5.2, which is particularly noticeable when using NVHPC. From the performance perspective, we found that the PVC1100 and A100 are relatively comparable on the LULESH benchmark. While the A100 is slightly better due to faster memory bandwidth, the PVC1100 reaches the next problem size (4003) scalably due to the larger memory size. The results are available at: https://***/scientific-computing-Lab-NRCN/Accel-OpenMP-Portability-Scalability. © 2023, CC BY.

关键词： Scalability

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Towards Autonomous Shooting Rover via Situation Aware Visual-Perception and Dynamic Action Execution

Towards Autonomous Shooting Rover via Situation Aware Visual...

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IEEE/SICE International Symposium on System Integration

作者： Shreenaga Chikoti Shubham Yukkta Seelam Aditya Jain Nishi Mehta Sidhartha Watsa Ayush Ranjan L. Behera Department of Mathematics and Scientific Computing Indian Institute of Technology Kanpur Department of Material Science and Engineering Indian Institute of Technology Kanpur Department of Electrical Engineering Indian Institute of Technology Kanpur Department of Computer Science and Engineering Indian Institute of Technology Kanpur Department of Mechanical Engineering Indian Institute of Technology Kanpur

In this system paper, we present our approach to the DJI Robomaster AI Challenge, 2022 where our team ERA-IITK was ranked 3 rd worldwide among 83 teams. We primarily focus on system hardware design and algorithmic components to achieve full autonomy. We first detail the hardware infrastructure, including intricate details on sensors and communication setup. Then we comprehensively describe our algorithmic pipeline and a state machine based on them to realize an autonomous system. Our major novelty is the high-speed and accurate visual perception module for target detection, and high-speed self-localization to maximize the likelihood of a target being shot. Our research endeavor in this paper can serve as a foundation for more complex systems and holds immense potential for various real-world applications.

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REFROM: Responsive, Energy-Efficient Frame Rendering for Mobile Devices

REFROM: Responsive, Energy-Efficient Frame Rendering for Mob...

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International Symposium on Low Power Electronics and Design (ISLPED)

作者： Tsung-Yen Hsu Yi-Shen Chen Yun-Chih Chen Yuan-Hao Chang Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Institute of Information Science Academia Sinica Mohamed bin Zayed University of Artificial Intelligence High Performance and Scientific Computing Center National Taiwan University

The increasing demand for high-quality graphics on mobile devices necessitates a high frame rate for display refresh. However, current process scheduling and memory management policies fail to consider the computation demands of frame rendering because they are optimized for saving energy and resource utilization. This leads to unresponsive displays for mobile users due to rendering delays. Accurately estimating computation demands is challenging for the mobile operating system, particularly under memory pressure, without display-specific semantics from user space. Moreover, the complexity of frame rendering makes it infeasible to schedule them with real-time policies. To address these issues, we propose a new framework called REFROM that utilizes a history-based frame time estimator to analyze frame time samples from UI threads and predict the computation requirements of upcoming frames. Experimental results demonstrate that REFROM reduces the number of delayed frames by up to 40% and improves up to 4% energy efficiency compared to the existing approaches.

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A Digital 3D TCAM Accelerator for the Inference Phase of Random Forest 23

A Digital 3D TCAM Accelerator for the Inference Phase of Ran...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Chieh-Lin Tsai Chun-Feng Wu Yuan-Hao Chang Han-Wen Hu Yung-Chun Lee Hsiang-Pang Li Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Department of Computer Science National Yang Ming Chiao Tung University Institute of Information Science Academia Sinica Taiwan Macronix International Co. Ltd and Department of Electrical Engineering National Tsing Hua University Macronix International Co. Ltd Department of Computer Science and Information Engineering National Taiwan University and Mohamed Bin Zayed University of Artificial Intelligence and NTU High Performance and Scientific Computing Center National Taiwan University

ISBN: (纸本)9798350323481

Random forest is a popular ensemble machine-learning algorithm for classification and regression tasks. However, the irregular tree shapes and non-deterministic memory access patterns make it hard for the current von Neumann architecture to handle random forest efficiently. This paper proposes a digital 3D TCAM-based accelerator for the random forest, adopting the idea of processing-in-memory (PIM) to reduce data movement. By utilizing this accelerator, we propose a TCAM-based approach to provide real-time inference with low energy consumption, making it suitable for edge or embedded environments. In the experiments, the proposed approach achieves an average of 3.13 times higher throughput with 22 times more energy saving than the GPU approach.

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