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22nd IEEE International Symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Tan, Yifan Mi, Zeyu Institute of Parallel and Distributed Systems Seiee Shanghai Jiao Tong University China

ISBN: (纸本)9798331509712

NVIDIA's H100 Confidential Computing (CC) counters the security hazards inherent in cloud AI workloads. It enforces data encryption to achieve data confidentiality, which leads to substantial throughput reductions as high as 93% in various AI workloads (such as TensorRT, PEFT and vLLM). Confronting this substantial overhead issue, we first delve into the underlying causes through meticulous analysis. This groundwork enables us to devise an innovative runtime system that operates seamlessly in the background, completely transparent to end-users. The cornerstone of our system lies in leveraging multiple encryption workers. Experiments demonstrate that our solution effectively reduces throughput drop to less than 28.1%. © 2024 IEEE.

关键词： Confidential Computing GPU H100 LLM

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FL-MISR: fast large-scale multi-image super-resolution for computed tomography based on multi-GPU acceleration

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2022年第2期19卷 331-344页

作者： Sun, Kaicong Tran, Trung-Hieu Guhathakurta, Jajnabalkya Simon, Sven Univ Stuttgart Inst Parallel & Distributed Syst D-70569 Stuttgart Germany

Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation. However, the large computational demand encumbers the deployment of MISR in practice. In this work, we propose a distributed optimization framework based on data parallelism for fast large-scale MISR using multi-GPU acceleration named FL-MISR. The scaled conjugate gradient (SCG) algorithm is applied to the distributed subfunctions and the local SCG variables are communicated to synchronize the convergence rate over multi-GPU systems towards a consistent convergence. Furthermore, an inner-outer border exchange scheme is performed to obviate the border effect between neighboring GPUs. The proposed FL-MISR is applied to the computed tomography (CT) system by super-resolving the projections acquired by subpixel detector shift. The SR reconstruction is performed on the fly during the CT acquisition such that no additional computation time is introduced. FL-MISR is extensively evaluated from different aspects and experimental results demonstrate that FL-MISR effectively improves the spatial resolution of CT systems in modulation transfer function (MTF) and visual perception. Comparing to a multi-core CPU implementation, FL-MISR achieves a more than 50x speedup on an off-the-shelf 4-GPU system.

关键词： Super resolution Computed tomography distributed optimization Data parallelism Multi-GPU Subpixel detector shift

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Parer: Boosting EROFS Image Creation With parallelism and Reproducibility 15

Parer: Boosting EROFS Image Creation With Parallelism and Re...

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15th Asia-Pacific Symposium on Internetware (Internetware)

作者： Zhao, Yifan Xin, Tong Dong, Mingkai Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China

ISBN: (纸本)9798400707056

The Enhanced Read-Only File System (EROFS) has become a popular choice for read-only file systems in a variety of settings, such as mobile devices and cloud services. As EROFS is increasingly applied to diverse contexts, the time-consuming process of creating EROFS images has emerged as a critical factor, often representing a bottleneck in application deployment. However, the process of parallelizing the creation of these images is fraught with challenges, owing to the constraints of fixed-size output compression, the requirement of reproducibility, and the added complexity of deduplication features. In this paper, we introduce Parer, a parallelized version of *** designed to ensure reproducibility. Parer utilizes a global order-preserving mechanism to assure consistent and reproducible outcomes and employs epoch-based de-duplication to mitigate dependencies during compression. Our evaluation indicates that Parer can reduce the time of creating an EROFS image by up to 79%, while only increasing the image's size by 3.42% on average. This work has gained recognition from the community and is being synchronized upstream.

关键词： Read-only File System EROFS File System Creation Storage System

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An intelligent mesh-smoothing method with graph neural networks

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Frontiers of Information Technology & Electronic Engineering 2025年第3期26卷 367-384页

作者： Zhichao WANG Xinhai CHEN Junjun YAN Jie LIU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Digitizing Software for Frontier Equipment National University of Defense TechnologyChangsha 410073China

In computational fluid dynamics(CFD),mesh-smoothing methods are widely used to refine the mesh quality for achieving high-precision numerical ***,optimization-based smoothing is used for high-quality mesh smoothing,but it incurs significant computational *** works have improved its smoothing efficiency by adopting supervised learning to learn smoothing methods from high-quality ***,they pose difficulties in smoothing the mesh nodes with varying degrees and require data augmentation to address the node input sequence ***,the required labeled high-quality meshes further limit the applicability of the proposed *** this paper,we present graph-based smoothing mesh net(GMSNet),a lightweight neural network model for intelligent mesh *** adopts graph neural networks(GNNs)to extract features of the node’s neighbors and outputs the optimal node *** smoothing,we also introduce a fault-tolerance mechanism to prevent GMSNet from generating negative volume *** a lightweight model,GMSNet can effectively smooth mesh nodes with varying degrees and remain unaffected by the order of input data.A novel loss function,MetricLoss,is developed to eliminate the need for high-quality meshes,which provides stable and rapid convergence during *** compare GMSNet with commonly used mesh-smoothing methods on two-dimensional(2D)triangle *** results show that GMSNet achieves outstanding mesh-smoothing performances with 5%of the model parameters compared to the previous model,but offers a speedup of 13.56 times over the optimization-based smoothing.

关键词： Unstructured mesh Mesh smoothing Graph neural network Optimization-based smoothing

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Merak: An Efficient distributed DNN Training Framework With Automated 3D parallelism for Giant Foundation Models

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IEEE TRANSACTIONS ON parallel and distributed SYSTEMS 2023年第5期34卷 1466-1478页

作者： Lai, Zhiquan Li, Shengwei Tang, Xudong Ge, Keshi Liu, Weijie Duan, Yabo Qiao, Linbo Li, Dongsheng Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc Changsha 410003 Hunan Peoples R China

Foundation models are in the process of becoming the dominant deep learning technology. Pretraining a foundation model is always time-consuming due to the large scale of both the model parameter and training dataset. Besides being computing-intensive, the pretraining process is extremely memory- and communication-intensive. These challenges make it necessary to apply 3D parallelism, which integrates data parallelism, pipeline model parallelism, and tensor model parallelism, to achieve high training efficiency. However, current 3D parallelism frameworks still encounter two issues: i) they are not transparent to model developers, requiring manual model modification to parallelize training, and ii) their utilization of computation resources, GPU memory, and network bandwidth is insufficient. We propose Merak, an automated 3D parallelism deep learning training framework with high resource utilization. Merak automatically deploys 3D parallelism with an automatic model partitioner, which includes a graph-sharding algorithm and proxy node-based model graph. Merak also offers a non-intrusive API to scale out foundation model training with minimal code modification. In addition, we design a high-performance 3D parallel runtime engine that employs several techniques to exploit available training resources, including a shifted critical path pipeline schedule that increases computation utilization, stage-aware recomputation that makes use of idle worker memory, and sub-pipelined tensor model parallelism that overlaps communication and computation. Experiments on 64 GPUs demonstrate Merak's capability to speed up training performance over state-of-the-art 3D parallelism frameworks of models with 1.5, 2.5, 8.3, and 20 billion parameters by up to 1.42, 1.39, 1.43, and 1.61x, respectively.

关键词： Training parallel processing Three-dimensional displays Solid modeling Computational modeling Pipelines Data models Deep learning distributed systems foundation model training

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Exploring Quantization Techniques for Large-Scale Language Models: Methods, Challenges and Future Directions 24

Exploring Quantization Techniques for Large-Scale Language M...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Shen, Ao Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China

ISBN: (纸本)9798400718137

Breakthroughs in natural language processing (NLP) by large-scale language models (LLMs) have led to superior performance in multilingual tasks such as translation, summarization, and Q&A. However, the size and complexity of these models raise challenges in terms of computational requirements, memory usage, and energy consumption. Quantization strategies, as a type of model compression technique, have gained attention for their advantages in reducing model size and accelerating inference speed. In this paper, we review the rapid development of quantization techniques for LLMs, and systematically explore methods such as post-training quantization (PTQ), quantization-aware fine-tuning (QAF), and quantization-aware training (QAT), which provide a comprehensive solution to the resource-intensive problem of LLMs from training to post-deployment. We also analyze state-of-the-art benchmarks and datasets to evaluate the effectiveness of quantitative methods in terms of performance retention and computational efficiency. The purpose of this review is to provide researchers with a snapshot of the latest progress in LLM quantization techniques, helping them to quickly grasp the dynamics of the field and understand the key techniques and challenges, so that they can more efficiently devote themselves to this evolving research area. © 2024 Copyright held by the owner/author(s).

关键词： Natural language processing systems

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NSC-YOLOv8: A Small Target Detection Method for UAV-Acquired Images Based on Self-Adaptive Embedding

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ELECTRONICS 2025年第8期14卷 1548-1548页

作者： Chen, Dongmin Chen, Danyang Zhong, Cheng Zhan, Feng Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Key Lab Parallel Distributed & Intelligent Comp Gu Nanning 530004 Peoples R China

Existing drone image processing algorithms for small target detection in Unmanned Aerial Vehicle (UAV) aerial images struggle with challenges like missed detection of small objects, information loss from downsampling, loss of low-dimensional features, and information drop of contextual features. In order to alleviate the four problems just mentioned, we propose a self-adaptive small target detection method, NSC-YOLOv8, based on the YOLOv8 model. First, we introduce a small target detection head that enhances the model's ability to fuse shallow and deep features, effectively handling low-pixel targets. Second, a Non-lossy Downsampling Block (NDB) is introduced into the backbone, which optimizes the detection accuracy of small targets in large scenes through dimensional transformation. In addition, we introduce a Self-Adaptive Embedding Block (SAEB) based on low-dimensional information, which enhances the comprehensive performance of the model by expanding the local sensing field to enhance the focus on important contextual information. Finally, we design a Content-Aware Resampling Block (CARB), which is able to enhance the model's ability to recognize small targets by resampling low-dimensional features. Experiments on the VisDrone2019-DET dataset show that NSC-YOLOv8s improves target detection accuracy over YOLOv8s, with an 11.7% increase in mAP@0.5. Additionally, removing a large detection head and adjusting the bottom-up layers reduces NSC-YOLOv8s' parameters by 1.2 M compared to YOLOv8s. Therefore, NSC-YOLOv8 shows better performance in small target detection for UAV imagery.

关键词： small target detection YOLOv8 UAV aerial images

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Serialization/Deserialization-free State Transfer in Serverless Workflows 24

Serialization/Deserialization-free State Transfer in Serverl...

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19th European Conference on Computer Systems, EuroSys 2024

作者： Lu, Fangming Wei, Xingda Huang, Zhuobin Chen, Rong Wu, Minyu Chen, Haibo Institute of Parallel and Distributed Systems Seiee Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China University of Electronic Science and Technology of China China Institute of Parallel and Distributed Systems Germany

ISBN: (纸本)9798400704376

Serialization and deserialization play a dominant role in the state transfer time of serverless workflows, leading to substantial performance penalties during workflow execution. We identify the key reason as a lack of ability to efficiently access the (remote) memory of another function. We propose RMMap, an OS primitive for remote memory map. It allows a serverless function to directly access the memory of another function, even if it is located remotely. RMMap is the first to completely eliminates serialization and deserialization when transferring states between any pairs of functions in (unmodified) serverless workflows. To make remote memory map efficient and feasible, we co-design it with fast networking (RDMA), OS, language runtime, and serverless platform. Evaluations using real-world serverless workloads show that integrating RMMap with Knative reduces the serverless workflow execution time on Knative by up to 2.6 × and improves resource utilizations by 86.3%. © 2024 ACM.

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Few-Shot Object Detection for Remote Sensing Images via Pseudo-Sample Generation and Feature Enhancement

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4477-4477页

作者： Huang, Zhaoguo Chen, Danyang Zhong, Cheng Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Key Lab Parallel Distributed & lntelligent Comp Gu Nanning 530004 Peoples R China

Few-shot object detection (FSOD) based on fine-tuning is essential for analyzing optical remote sensing images. However, existing methods mainly focus on natural images and overlook the scale variations in remote sensing images, leading to feature confusion among foreground instances of different classes. Additionally, since only a subset of instances are labeled in FSOD training data, the model might mistakenly treat unlabeled instances as background, leading to confusion between foreground features and background features, particularly those of novel classes. The preceding phenomenon indicates that severe feature confusion in remote sensing FSOD hampers the ability of the model to accurately classify and localize instances. To address these issues, this paper proposes a two-stage FSOD framework based on transfer learning via pseudo-sample generation and feature enhancement (PSGFE), including pseudo-sample generation module (PSGM) and feature enhancement module (FEM). The former reduces the feature confusion between foreground and background by generating pseudo-samples for unannotated background areas. The latter dynamically captures and enhances multi-scale features on the region of interest (ROI), and extracts unique core information for each class to eliminate the feature confusion among foreground instances of different classes. Our method has been validated on the optical remote sensing datasets DIOR and RSOD. It demonstrates superior performance compared to existing methods.

关键词： few-shot object detection remote sensing images pseudo-sample generation feature enhancement

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Enhancing Data Trustworthiness in Explorative Analysis: An Interactive Approach for Data Quality Monitoring

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SN Computer Science 2024年第5期5卷 465页

作者： Behringer, Michael Hirmer, Pascal Villanueva, Alejandro Rapp, Jannis Mitschang, Bernhard Institute for Parallel and Distributed Systems University of Stuttgart Universitätsstr. 38 Stuttgart 70569 Germany

The volume of data to be analyzed has increased tremendously in recent years. In order to extract knowledge from this data, domain experts gain new insights with the help of graphical analysis tools for explorative analyses. Here, the reliability and trustworthiness of an exploratory analysis is determined by the quality of the underlying data. Existing approaches require manual testing to ensure data quality which is often neglected. This research aims to introduce a novel interactive approach for seamlessly integrating data quality considerations into the process of explorative data analysis conducted by domain experts. We derive requirements, conduct an extensive literature review, and develop an approach that efficiently combines stakeholders’ strengths, allowing unobtrusive data quality integration in interactive analysis. Our approach enhances trustworthiness due to unobtrusive monitoring of data quality within the context of explorative data analysis. Domain experts gain insights more reliably, bridging the gap between technical requirements and domain expertise. In conclusion, our research presents a promising solution for improving the reliability and trustworthiness of explorative data analysis, especially for domain experts who may lack technical knowledge. By seamlessly integrating data quality into the analytical process, we empower domain experts to extract valuable insights from the ever-increasing volume of data, thereby advancing the field of data-driven decision-making. © The Author(s) 2024.

关键词： Data Quality Data Mashups Explorative Data Analysis Human-in-the-Loop

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