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检索条件"主题词=distributed Computing"

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Demystifying Data Management for Large Language Models 24

Demystifying Data Management for Large Language Models

引用

ACM International Conference on Management of Data (SIGMOD)

作者： Miao, Xupeng Jia, Zhihao Cui, Bin Carnegie Mellon Univ Pittsburgh PA 15213 USA Peking Univ Beijing Peoples R China

ISBN: (纸本)9798400704222

Navigating the intricacies of data management in the era of Large Language Models (LLMs) presents both challenges and opportunities for database and data management communities. In this tutorial, we offer a comprehensive exploration into the vital role of data management across the development and deployment phases of advanced LLMs. We provide an in-depth survey of existing techniques of managing knowledge and parameter data during the whole LLM lifecycle, emphasizing the balance between efficiency and effectiveness. This tutorial stands to offer participants valuable insights into the best practices and contemporary challenges in data management for LLMs, equipping them with the knowledge to navigate and contribute to this rapidly evolving field.

关键词： Large Language Model Pre-training Fine-tuning Inference Data Management Database distributed computing Knowledge Data

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distributed Computations with Layered Resolution 11

Distributed Computations with Layered Resolution

引用

11th IEEE International Conference on Cloud Networking (CloudNet)

作者： Esfahanizadeh, Homa Cohen, Alejandro Medard, Muriel Shamai (Shitz), Shlomo MIT RLE Cambridge MA 02139 USA Technion Fac Elect & Comp Engn Haifa Israel

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

Modern computationally-heavy applications are often time-sensitive, demanding distributed strategies to accelerate them. On the other hand, distributed computing suffers from the bottleneck of slow workers in practice. distributed coded computing is an attractive solution that adds redundancy such that a subset of distributed computations suffices to obtain the final result. However, the final result is still either obtained within a desired time or not, and for the latter, the resources that are spent are wasted. In this paper, we introduce the novel concept of layered-resolution distributed coded computations such that lower resolutions of the final result are obtained from collective results of the workers - at an earlier stage than the final result. This innovation makes it possible to have more effective deadline-based systems, since even if a computational job is terminated because of timing, an approximated version of the final result can be released. Based on our theoretical and empirical results, the average execution delay for the first resolution is notably smaller than the one for the final resolution. Moreover, the probability of meeting a deadline is one for the first resolution in a setting where the final resolution exceeds the deadline almost all the time, reducing the success rate of the systems with no layering.

关键词： Technological innovation Conferences Redundancy Delays distributed computing

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Hyperion: A Generic and distributed Mobile Offloading Framework on OpenCL 20

Hyperion: A Generic and Distributed Mobile Offloading Framew...

引用

20th ACM Conference on Embedded Networked Sensor Systems (SenSys)

作者： Fu, Ziyan Ren, Ju Liu, Yunxin Cao, Ting Zhang, Deyu Zhou, Yuezhi Zhang, Yaoxue Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing Peoples R China Microsoft Res Beijing Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Zhongguancun Lab Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9781450398862

Despite the significant development of mobile device SoCs, they are still inefficient in computing computation-intensive workloads, such as high-resolution image processing and AR/VR applications. Offloading offers a promising way to leverage cloud or edge servers for acceleration, but existing offloading is limited to specific tasks or specific hardware/software platforms, resulting in significant engineering overhead. To address this problem, we focus on the underlying layer of these applications (i.e., OpenCL) and propose Hyperion, a generic and distributed mobile offloading framework built on OpenCL. To achieve high-performance distributed execution for Hyperion, we first take a deep insight into the OpenCL data structures and design regularity-aware kernel analyzer to analyze the data dependency of work-groups and identify the essential data to offload. Then, context-aware execution time predictor is proposed to estimate the computing time of a given partitioned kernel workload that is highly impacted by many runtime factors. These techniques are integrated into pipeline-enabled and network-adaptive scheduler to make scheduling decisions, which coordinates the kernel partition and workload scheduling to form pipeline processing between data transmission and distributed execution with flexible adaptability to network dynamics. Extensive experimental results demonstrate that Hyperion achieves superior performance with an average 3.80x speedup compared with the best baseline and flexible adaptation to dynamic network conditions and available computing resources.

关键词： Mobile offloading edge computing distributed computing OpenCL online scheduling

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Structural Health Monitoring of Civil Infrastructures Using Smart Sensor Networks

Structural Health Monitoring of Civil Infrastructures Using ...

引用

Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

作者： Nour, Marc Abdel Giordano, Pier Francesco Limongelli, Maria Pina Politecn Milan Piazza Leonardo Da Vinci 32 I-20133 Milan Italy Politecn Milan Dept Architecture Built Environm & Construct Engn Piazza Leonardo Da Vinci 32 I-20133 Milan Italy Lund Univ Fac Engn Div Struct Engn John Ericssons Vag 1 SE-22100 Lund Sweden

ISBN: (数字)9781510672055

ISBN: (纸本)9781510672055;9781510672048

Structural Health Monitoring (SHM) plays a vital role in maintaining the integrity of structures by providing continuous information about damage or anomalies. Vibration-based SHM, which focuses on the dynamic behavior of structures, offers insights into structural conditions through changes in dynamic properties. Among SHM approaches, damage localization is crucial for pinpointing the geometric location of damage. This paper proposes a method for damage localization using Short Time Fourier Transform and a Statistical Interpolation Damage Index. The proposed methodology is applied to a numerical case study involving a finite element beam model and to the S101 benchmark bridge, in Austria, demonstrating its efficacy in damage localization. The study also introduces a multi-level clustering approach to perform damage localization using smart decentralized sensor networks, able to reduce the volume of transmitted data and thereby the energy requirements. Results show promising outcomes in accurately identifying damage locations while minimizing data transmission.

关键词： structural health monitoring decentralized sensor network distributed computing damage localization interpolation error

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Pervasive Chatbots: Investigating Chatbot Interventions for Multi-Device Applications 32

Pervasive Chatbots: Investigating Chatbot Interventions for ...

引用

32nd ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP)

作者： Olapade, Mayowa Hasanli, Tarlan Ottun, Abdul-Rasheed Akintola, Adeyinka Liyanage, Mohan Flores, Huber Univ Tartu Tartu Estonia

ISBN: (纸本)9798400704338

The inherent social characteristics of humans make them prone to adopting distributed and collaborative applications easily. Although fundamental methods and technologies have been defined and developed over the years to construct these applications, their adoption in practice is uncommon because end-users may be puzzled about how to use them without much hassle. Indeed, commonly, these applications require a certain level of technical expertise and awareness to use them correctly. Fortunately, AI-chatbot interventions are envisioned to assist and support various human tasks. In this paper, we contribute pervasive chatbots as a solution that fosters a more transparent and user-friendly interconnection of devices in distributed and collaborative environments. Through two rigorous user studies, firstly, we quantify the perception of users toward distributed and collaborative applications (N = 56 participants). Secondly, we analyze the benefits of adopting pervasive chatbots when compared with the chatbot reference model designed for assistance and recommendations (N = 24 participants). Our results suggest that pervasive chatbots can significantly enhance the practicability of distributed and collaborative applications, reducing the time and effort needed for collaboration with surrounding devices by 57%. With this information, we then provide design and development implications to integrate pervasive chatbot interventions in distributed and collaborative environments. Moreover, challenges and opportunities are also provided to highlight the remaining issues that need to be addressed to realize the full vision of pervasive chatbots for any multi-device application. Our work paves the way towards the proliferation of sophisticated and highly decentralized computing environments that are easily interconnected.

关键词： Decentralized infrastructures distributed computing collaborative computing opportunistic networks

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Long Sequence Inference and Memory Optimization in FastFold 24

Long Sequence Inference and Memory Optimization in FastFold

引用

International Conference on Computer and Multimedia Technology (ICCMT)

作者： Liang, Qingfeng Wang, Zheqi Zhu, Wenwen Liu, Dong Cao, Wudi Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450000 Peoples R China Dawning Informat Ind Beijing Co Ltd Qingdao 266000 Peoples R China Dawning Informat Ind Beijing Co Ltd Nanjing 210000 Peoples R China Dawning Informat Ind Beijing Co Ltd Xian 710000 Peoples R China

ISBN: (纸本)9798400718267

AlphaFold2 has successfully solved the protein structure prediction problem that has perplexed the field of biology for 50 years, significantly reducing the prediction cost compared to traditional experimental methods. AlphaFold2 exhibits high memory demands. FastFold, a PyTorch implementation of AlphaFold2, introduced methods like Dynamic Axis Parallelism to achieve parallelism. FastFold reduces the inference time, but there is still room for memory optimization. This study explores various memory optimization techniques built upon the FastFold to reduce the model's memory requirements. The optimization methods encompass strategies such as memory fragment management, memory offloading and chunk-based computation. The optimized model maintains excellent inference capability and accuracy on GPUs with limited memory, and its maximum inference length is doubled. Using 4 GPU-like accelerators(16GB) enables the inference of proteins up to a maximum length of 3072, whereas using 8 accelerators extends the inferable length to 3511.

关键词： FastFold distributed computing deep learning memory optimization

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Codeless PDC Modules for Early computing Curriculum

Codeless PDC Modules for Early Computing Curriculum

引用

1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Bourke, Chris Firestone, Justin Univ Nebraska Sch Comp Lincoln NE 68588 USA

ISBN: (纸本)9798350364613;9798350364606

PDC at UM, is a series of "codeless" modules consisting of visualizations, simulations, and demonstrations which introduce Parallel and distributed computing (PDC) concepts in early computing courses. These materials are codeless because they do not require students to write or understand code. Instead, students read a short introduction to a PDC concept and Then engage with a web-based visualization and/or (code-based) demonstration reinforcing the concept. The codeless nature of these modules makes them suitable for computing and non computing majors. To test the effectiveness of our modules we introduced them into two CSI courses and designed and administered a pre/posttest. Our results show statistically significant results: those who engaged with our modules substantially improved their knowledge and understanding of PDC concepts. Our modules also improved student attitudes, confidence and self-efficacy with respect to PDC topics. We also provide some qualitative observations of our study and identify common misconceptions students have about PDC.

关键词： parallel computing distributed computing computer science education

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Fine-grained Dynamic Load Balancing in Spatial Join byWork Stealing on distributed Memory 22

Fine-grained Dynamic Load Balancing in Spatial Join byWork S...

引用

30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Yang, Jie Puri, Satish Zhou, Hui Marquette Univ Dept Comp Sci Milwaukee WI 53233 USA Argonne Natl Lab Math & Comp Sci Dept Lemont IL USA

ISBN: (纸本)9781450395298

Spatial join is an important operation for combining spatial data. Parallelization is essential for improving spatial join performance. However, load imbalance due to data skew limits the scalability of parallel spatial join. There are many work sharing techniques to address this problem in a parallel environment. One of the techniques is to use data and space partitioning and then scheduling the partitions among threads/processes with the goal of minimizing workload differences across threads/processes. However, load imbalance still exists due to differences in join costs of different pairs of input geometries in the partitions. For the load imbalance problem, we have designed a work stealing spatial join system (WSSJ-DM) on a distributed memory environment. Work stealing is an approach for dynamic load balancing in which an idle processor steals computational tasks from other processors [5]. This is the first work that uses work stealing concept (instead of work sharing) to parallelize spatial join computation on a large compute cluster. We have evaluated the scalability of the system on shared and distributed memory. Our experimental evaluation shows that work stealing is an effective strategy. We compared WSSJ-DM with work sharing implementations of spatial join on a high performance computing environment using partitioned and un-partitioned datasets. Static and dynamic load balancing approaches were used for comparison. We study the effect of memory affinity in work stealing operations involved in spatial join on a multi-core processor. WSSJ-DM performed spatial join using ST_Intersection on Lakes (8.4M polygons) and Parks (10M polygons) in 30 seconds using 35 compute nodes on a cluster (1260 CPU cores). A work sharing Master-Worker implementation took 160 seconds in contrast.

关键词： High Performance computing distributed computing NUMA MPI

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A Framework for Network Self-evolving Based on distributed Swarm Intelligence 22nd

A Framework for Network Self-evolving Based on Distributed S...

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22nd Annual International Conference on Computational Science (ICCS)

作者： Tian, Changbo Zhang, Yongzheng Yin, Tao Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Chinese Asset Cybersecur Technol Co Ltd Beijing 100041 Peoples R China

ISBN: (纸本)9783031087547;9783031087530

More and more users are attracted by P2P networks characterized by decentralization, autonomy and anonymity. The management and optimization of P2P networks have become the important research contents. This paper presents a framework for network self-evolving problem based on distributed swarm intelligence, which is achieved by the collaboration of different nodes. Each node, as an independent agent, only has the information of its local topology. Through the consensus method, each node searches for an evolving structure to evolve its local topology. The self-evolving of each node's local topology makes the whole topology converge to the optimal topology model. In the experiments, two simulated examples under different network topologies illustrate the feasibility of our approach.

关键词： Self-evolving network Swarm intelligence distributed optimization distributed computing distributed consensus

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Efficient Communication for Decentralized Federated Learning: An Energy Disaggregation Case Study 15

Efficient Communication for Decentralized Federated Learning...

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IEEE 15th International Symposium on Power Electronics for distributed Generation Systems (PEDG)

作者： Zhang, Yusen Gao, Feng Zhou, Kangjia Shandong Univ Sch Control Sci & Engn Jinan Peoples R China Shandong Univ Sch Elect Engn Jinan Peoples R China

ISBN: (纸本)9798350361018;9798350361001

The terminal devices in the energy system face limitations in communication resources, storage space, computational power, and data security, making it challenging to train and deploy computationally intensive artificial intelligence models. Therefore, employing communication-efficient federated learning to train lightweight neural network models is a suitable solution. In this paper, we propose decentralized federated learning with efficient communication achieved by cyclically broadcasting weight parameters from each device during the model training process. We explore the proposed decentralized federated learning framework using energy disaggregation as a case study. Furthermore, grouped convolution is introduced to establish lightweight models, reducing the computational and storage costs during both the training process and model deployment. We conducted extensive experiments on publicly available datasets to validate that the proposed decentralized federated learning approach exhibits significant communication efficiency and minimal impact on the performance of the global model.

关键词： federated learning artificial intelligence decentralized federated learning distributed computing energy disaggregation

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