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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10259 条记录，以下是21-30 订阅

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AdapCK: Optimizing I/O for Checkpointing on Large-Scale high performance Computing Systems 30th

AdapCK: Optimizing I/O for Checkpointing on Large-Scale High...

引用

30th European conference on Parallel and Distributed Processing (Euro-Par)

作者： Jia, Jie Liu, Yi Liu, Yanke Chen, Yifan Lin, Fang Beihang Univ Sino German Joint Software Inst Beijing 100191 Peoples R China

ISBN: (纸本)9783031695827;9783031695834

With the scaling-up of high-performance computing (HPC) systems, the resilience has become an important challenge. As a widely used resilience technique for HPC systems, checkpointing saves checkpoints of the system during the execution of parallel programs, and in case of failure, recovers the execution of the program from the most recent checkpoint. However, large-scale parallel programs often produce thousands of processes, and result in large-volume simultaneous data-writings on each checkpoint, which impacts the storage as well as the parallel file systems of HPC. To tackle this problem, this paper proposes AdapCK, an I/O-optimization scheme for checkpointing on large-scale HPC systems. AdapCK consists of two main parts: a load-balancing mechanism used for balancing workloads across low-level storage volumes on checkpointing, and a throughput-aware checkpoint-data writing mechanism used for reducing I/O contentions and increasing utilization of I/O-bandwidth. Experiment results show that the AdapCK can reduce the checkpoint time by more than 30%, up to 54.5%.

关键词： Checkpoint Fault tolerance high-performance Computing Parallel file system DMTCP

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An Efficient Energy Consumption Prediction Framework for high performance Computing Cluster Jobs 5

An Efficient Energy Consumption Prediction Framework for Hig...

引用

5th International conference on computer Engineering and Application (ICCEA)

作者： Lou, Yantao Wang, Jibin Feng, Shoupeng Yu, Xian Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Qilu Univ TechnolShandong Prov Key Lab Comp Netw Minist EducShandong Comp Sci CtrNatl Supercomp Jinan Peoples R China Shandong Intelligent Publ Secur Technol Innovat C Dept Publ Secur Shandong Prov Jinan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Energy consumption prediction is a crucial approach to enhance the operational efficiency of HPC (high performance Computing) clusters. Existing predicting methods for the energy consumption mainly rely on time series mode, focusing on the instantaneous power during job execution. However, in practical applications, due to the large scale of HPC clusters, the prediction models need to process a vast amount of data, often resulting in poor performance. Moreover, it's challenging to provide effective predictions for jobs with significant power fluctuations. Thus we propose a more efficient energy consumption prediction framework for HPC cluster jobs, achieving two main objectives: On one hand, it uses a data encoding module to pre-encode the sampled data of energy consumption related indicators, simplifying the data that the time series prediction model needs to process and improving prediction efficiency. On the other hand, the prediction target is set to the power distribution range in a future period of time, rather than the instantaneous power at some specific time points, which improves the usability of the prediction results. Test results show that the proposed prediction framework can achieve more markable performance improvement while ensuring the effectiveness of the predictions.

关键词： component Efficient Framework Power Prediction high performance Computing Time Series Data

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Heap Memory Vulnerability Detection and Protection in Embedded Real-Time Operating Systems 24

Heap Memory Vulnerability Detection and Protection in Embedd...

引用

8th International conference on Electronic Information Technology and computer Engineering, EITCE 2024

作者： Zhang, Jinyuan Yao, Rui Zhang, Yang Beijing Institute of Computer Technology and Application Beijing China

ISBN: (纸本)9798400710094

In application software development, memory defects are difficult to detect. Traditional memory defect detection tools generally face issues of high performance overhead and excessive memory consumption, which limits their applicability in embedded operating systems. To address this, a method called EHD (Embedded Heap Detect) is proposed. This method combines memory defect detection technology with the memory management mechanisms of embedded operating systems to effectively and quickly detect heap memory vulnerabilities, selecting appropriate times for detection to reduce the number of detections and lower performance overhead. © 2024 Copyright held by the owner/author(s).

关键词： Dynamic Detection

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HDMNet: A Hierarchical Matching Network with Double Attention for Large-scale Outdoor LiDAR Point Cloud Registration

HDMNet: A Hierarchical Matching Network with Double Attentio...

引用

IEEE/CVF Winter conference on Applications of computer Vision (WACV)

作者： Xue, Weiyi Lu, Fan Chen, Guang Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Outdoor LiDAR point clouds are typically large-scale and complexly distributed. To achieve efficient and accurate registration, emphasizing the similarity among local regions and prioritizing global local-to-local matching is of utmost importance, subsequent to which accuracy can be enhanced through cost-effective fine registration. In this paper, a novel hierarchical neural network with double attention named HDMNet is proposed for large-scale outdoor LiDAR point cloud registration. Specifically, A novel feature consistency enhanced double-soft matching network is introduced to achieve two-stage matching with high flexibility while enlarging the receptive field with high efficiency in a patch-to-patch manner, which significantly improves the registration performance. Moreover, in order to further utilize the sparse matching information from deeper layer, we develop a novel trainable embedding mask to incorporate the confidence scores of correspondences obtained from pose estimation of deeper layer, eliminating additional computations. The high-confidence keypoints in the sparser point cloud of the deeper layer correspond to a high-confidence spatial neighborhood region in shallower layer, which will receive more attention, while the features of non-key regions will be masked. Extensive experiments are conducted on two large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HDMNet.

关键词： 3D computer vision Algorithms

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Soter: Analytical Tensor-Architecture Modeling and Automatic Tensor program Tuning for Spatial Accelerators 51

Soter: Analytical Tensor-Architecture Modeling and Automatic...

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ACM/IEEE 51st Annual International Symposium on computer Architecture (ISCA)

作者： Wang, Fuyu Shen, Minghua Ding, Yufei Xiao, Nong Sun Yat Sen Univ Guangzhou Peoples R China Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9798350326598;9798350326581

Spatial accelerator is a specialized hardware to provide noticeable performance speedup for tensor computations. It also brings a challenge to map tensor computations on spatial accelerators. Auto-tuning compiler is one of the most promising directions for tensor mapping. However, existing auto-tuning compilers suffer from either numerous invalid and inefficient programs or inaccurate evaluation of incomplete programs, leading to sub-optimal performance. In this paper, we propose Soter, a novel auto-tuning tensor compilation framework for spatial accelerators. The key is to perform exploration in a both valid and efficient program design space and perform optimization according to accurate evaluation of complete programs. First, we design an analytical model to generate a high-quality program design space, which excludes invalid and inefficient programs. Second, we design an automatic program tuner to efficiently explore the program space and avoid evaluating incomplete programs. Finally, we coordinate the model and the tuner to further improve the quality of program space. The program space is identified by the model and is updated during the exploration of tuner. On average, Soter achieves 2.1x to 3.5x speedup over the state-of-the-art tensor compilers. Moreover, Soter shows better scalability for larger-scale tensor computations and spatial architectures.

关键词： code generation Spatial accelerators tensor computations

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RTMO: Towards high-performance One-Stage Real-Time Multi-Person Pose Estimation

RTMO: Towards High-Performance One-Stage Real-Time Multi-Per...

引用

IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Lu, Peng Jiang, Tao Li, Yining Li, Xiangtai Chen, Kai Yang, Wenming Tsinghua Shenzhen Int Grad Sch Shenzhen Guangdong Peoples R China Shanghai AI Lab Shanghai Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350353013;9798350353006

Real-time multi-person pose estimation presents significant challenges in balancing speed and precision. While two-stage top-down methods slow down as the number of people in the image increases, existing one-stage methods often fail to simultaneously deliver high accuracy and real-time performance. This paper introduces RTMO, a one-stage pose estimation framework that seamlessly integrates coordinate classification by representing keypoints using dual 1-D heatmaps within the YOLO architecture, achieving accuracy comparable to top-down methods while maintaining high speed. We propose a dynamic coordinate classifier and a tailored loss function for heatmap learning, specifically designed to address the incompatibilities between coordinate classification and dense prediction models. RTMO outperforms state-of-the-art one-stage pose estimators, achieving 1.1% higher AP on COCO while operating about 9 times faster with the same backbone. Our largest model, RTMO-l, attains 74.8% AP on COCO val2017 and 141 FPS on a single V100 GPU, demonstrating its efficiency and accuracy. The code and models are available at https://***/open-mmlab/mmpose/tree/main/projects/rtmo.

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Inspection of I/O Operations from System Call Traces using Directly-Follows-Graph

Inspection of I/O Operations from System Call Traces using D...

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2024 Workshops of the International conference for high performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Sankaran, Aravind Zhukov, Ilya Frings, Wolfgang Bientinesi, Paolo Forschungszentrum Jülich Jülich Supercomputing Center Germany Umeå Universitet Department of Computer Science Sweden

ISBN: (纸本)9798350355543

We aim to identify the differences in Input/Output (I/O) behavior between multiple user programs through the inspection of system calls (i.e., requests made to the operating system). A typical program issues a large number of I/O requests to the operating system, thereby making the process of inspection challenging. In this paper, we address this challenge by presenting a methodology to synthesize I/O system call traces into a specific type of directed graph, known as the Directly-Follows-Graph (DFG). Based on the DFG, we present a technique to compare the traces from multiple programs or different configurations of the same program, such that it is possible to identify the differences in the I/O behavior. We apply our methodology to the IOR benchmark, and compare the contentions for file accesses when the benchmark is run with different options for file output and software interface. © 2024 IEEE.

关键词： Directly-Follows Graph high-performance Computing Input/Output performance Analysis Process Mining strace

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SwitchFlow: Optimizing HPC Workflow performance with Heterogeneous Serverless Frameworks 30

SwitchFlow: Optimizing HPC Workflow Performance with Heterog...

引用

30th IEEE International conference on Parallel and Distributed Systems, ICPADS 2024

作者： Chen, Hao Dong, Yucong Wen, Xin Xu, Zichen Nanchang University School of Mathematics and Computer Science Jiangxi China Jiangxi Academy of Water Science and Engineering Jiangxi China

ISBN: (纸本)9798331515966

high-performance serverless computing has garnered significant attention. Researchers have developed numerous optimization strategies for serverless frameworks to fully leverage the benefits of serverless computing. However, they often overlook the architectural heterogeneity of various serverless frameworks, leading to performance discrepancies. We designs SwitchFlow by analyzing and characterizing the performance of different serverless frameworks. The core idea is to utilize the time series characteristics of scientific workflows to predict upcoming functions, thereby switching them to the optimal framework for execution. Furthermore, SwitchFlow's unique design concept enables integration with existing optimization strategies. Experimental results demonstrate that SwitchFlow can effectively reduce overall workflow execution time, optimize resource efficiency, and enhance service availability compared to running on a single serverless framework. © 2024 IEEE.

关键词： high performance Computing Serverless Computing

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iToF-flow-based high Frame Rate Depth Imaging

iToF-flow-based High Frame Rate Depth Imaging

引用

IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Meng, Yu Xue, Zhou Chang, Xu Hu, Xuemei Yue, Tao Nanjing Univ Nanjing Peoples R China Li Auto Beijing Peoples R China Bytedance Inc Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

iToF is a prevalent, cost-effective technology for 3D perception. While its reliance on multi-measurement commonly leads to reduced performance in dynamic environments. Based on the analysis of the physical iToF imaging process, we propose the iToF flow, composed of crossmode transformation and uni-mode photometric correction, to model the variation of measurements caused by different measurement modes and 3D motion, respectively. We propose a local linear transform (LLT) based cross-mode transfer module (LCTM) for mode-varying and pixel shift compensation of cross-mode flow, and uni-mode photometric correct module (UPCM) for estimating the depth-wise motion caused photometric residual of uni-mode flow. The iToF flow-based depth extraction network is proposed which could facilitate the estimation of the 4-phase measurements at each individual time for high framerate and accurate depth estimation. Extensive experiments, including both simulation and real-world experiments, are conducted to demonstrate the effectiveness of the proposed methods. Compared with the SOTA method, our approach reduces the computation time by 75% while improving the performance by 38%. The code and database are available at https://***/ComputationalPerceptionLab/iToF_flow.

关键词： 3D motion Depth Imaging iToF Motion Compensation

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high performance Algorithms for the Unrelated Parallel Machines Scheduling Problem with a Common Server and Job-Sequence Dependent Setup Times 9th

High Performance Algorithms for the Unrelated Parallel Machi...

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9th International conference on Metaheuristics and Nature Inspired Computing (META)

作者： Hadhbi, Youssouf Deroussi, Laurent Grangeon, Nathalie Norre, Sylvie Blanchon, Christophe Univ Clermont Auvergne CNRS Clermont Auvergne INP Mines St EtienneLIMOS F-63000 Clermont Ferrand France Inoprod F-63800 Cournon Dauvergne France

ISBN: (纸本)9783031692567;9783031692574

In this paper we study a variant of the non-preemptive unrelated parallel machines scheduling problem with sequence-dependent setup times and machine eligibility restrictions. We first formulate the problem as a mixed integer linear program (MILP), and further devise a branch-and-cut (B&C) algorithm for solving the problem. Due to the NP hardness of the problem, we propose a metaheuristic based on an iterated local search (ILS) algorithm. Using this, we provide several matheuristics for solving the problem. The proposed approaches are compared using different families of instances.

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