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FlexScience'22: 12th Workshop on AI and Scientific computing at Scale using Flexible computing Infrastructures 22

FlexScience'22: 12th Workshop on AI and Scientific Computing...

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31st international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Costan, Alexandru Nicolae, Bogdan Sato, Kento INSA Rennes IRISA Rennes France Argonne Natl Lab Lemont IL USA RIKEN Ctr Kobe Hyogo Japan

ISBN: (纸本)9781450391993

Scientific computing applications generate enormous datasets that are continuously increasing exponentially in both complexity and volume, making their analysis, archival, and sharing one of the grand challenges of modern big data analytics. Supported by the rise of artificial intelligence and deep learning, such enormous datasets are becoming valuable resources even beyond their original scope, opening new opportunities to learn patterns and extract new knowledge at large scale, potentially without human intervention. However, this leads to an increasing complexity of the workflows that combine traditional HPC simulations with big data analytics and AI applications. An initial wave that opened this direction was the shift from compute-intensive to data-intensive, which saw several ideas from big data analytics (in-situ processing, shipping computations close to data, complex and dynamic workflows) fused with the tightly coupled patterns addressed by the AI and the high performance computing ecosystems. In a quest to keep up with the complexity of the workflows, the design and operation of the infrastructures capable of running them efficiently at scale has evolved accordingly. Extreme heterogeneity at all levels (combinations of CPUs and accelerators, various types of memories and local storage and network links, parallel file systems and object stores, etc.) is now the norm. ideas pioneered by cloud and edge computing (aspects related to elasticity, multi-tenancy, geo-distributed processing, stream computing) are also beginning to be adopted in the HPC ecosystem (containerized workflows, on-demand jobs to complement batch jobs, streaming of experimental data from instruments directly to supercomputers, etc.). Thus, modern scientific applications need to be integrated into an entire Compute Continuum from the edge all the way to supercomputers and large data-centers using flexible infrastructures and middlewares. The 12th workshop on AI and Scientific computing at

关键词： computing continuum scientific computing cloud computing edge processing artificial intelligence

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C++ and Interoperability between Libraries: The GraphBLAS C++ Specification

C++ and Interoperability between Libraries: The GraphBLAS C+...

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2023 IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2023

作者： Brock, Benjamin McMillan, Scott Buluc, Aydin Mattson, Timothy G. Moreira, Jose E. Parallel Computing Lab Intel United States Software Engineering Institute Carnegie Mellon University United States Lawrence Berkeley National Laboratory Computational Research Department United States University of California EECS Department Berkeley United States IBM Thomas J. Watson Research Center United States

ISBN: (纸本)9798350311990

Interoperability between libraries is often hindered by incompatible data formats, which can necessitate creating new copies of data when transferring data back and forth between different libraries. This additional data movement incurs additional runtime costs, particularly for sparse applications, where the costs of data movement often dwarf compute costs. In this paper, we investigate interoperability in the context of the C++ GraphBLAS Specification, where C++ concepts allow GraphBLAS algorithms to accept any matrix type as long as it follows the matrix interface defined in the GraphBLAS matrix concept. We first develop non-owning, lazily evaluated adapted views for a number of external data structures, including two categories of graphs defined in the Northwest Graph Library (NWGraph) and traditional pointer-based CSR data structures. These adapted views fulfill the C++ GraphBLAS matrix concept, allowing them to be used inside GraphBLAS algorithms. We then evaluate the performance of these adapted views across two kernels, matrix reduction and sparse times dense matrix multiplication (SpMM), where the performance achieved using a single generic implementation with these views largely matches the performance achieved operating directly on the original data structures, with a slight performance loss in one case. We then propose a mechanism for automatically discovering the availability of these views, allowing algorithms to directly accept external data structures. We also discuss potential extensions to the C++ GraphBLAS specification that might eliminate the small performance dip observed for one of the views. © 2023 IEEE.

关键词： Data structures

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Novel Resource Allocation Algorithm of Edge computing Based on Deep Reinforcement Learning Mechanism 19

Novel Resource Allocation Algorithm of Edge Computing Based ...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Zhang, Degan Fan, Hongrui Zhang, Jie Tianjin Univ Technol Tianjin Key Lab Intelligent Comp & Novel Software Key Lab Comp Vis & Syst Minist Educ Tianjin Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781665435741

Edge computing is a computing paradigm that can bring practical value to most modern enterprises. When it is integrated into the Internet of Things system, it can improve the QoS of mobile applications, and can realize real-time management of the generated big data.5G promotes the development of the edge computing paradigm further, and mobile users can obtain low-latency and high-speed access QoS. In this paper, we study the resource allocation method of edge servers in the MEC environment. We are absorbed in solving the problem of allocation of limited resources in the MEC system, such as computing resources and available bandwidth, with the goal of maximizing the average resource utilization and task processing capacity of edge servers in the MEC system, while considering some differential processing for delay-sensitive applications, describe this resource allocation problem as a Finite-state Markov Decision Process (FMDP), and consider the continuity of the user state, and design a new Edge computing Resource Allocation Algorithm based on Deep Deterministic Policy Gradient (ECRAA-DDPG)to find the optimal strategy for resource allocation. Finally, a large number of experiments are used to prove the performance of the new algorithm, and the experimental results show that the method can make the optimal decision in a real environment.

关键词： computing paradigm MEC resource allocation deep reinforcement learning

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Wireless Multihop Transmissions with Concurrent Forwarding without Collisions by Capture Effect

Wireless Multihop Transmissions with Concurrent Forwarding w...

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Shintaro Okazaki Hiroaki Higaki Department of Robotics and Mechatronics Tokyo Denki University Tokyo Japan

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

In wireless multihop transmissions of data messages, concurrent data message forwarding by 2-hop neighbor intermediate wireless nodes are prohibited due to collision avoidance between these data messages. However, depending on the distances between neighbor intermediate wireless nodes, no collision concurrent data message forwarding by 2-hop neighbor intermediate wireless nodes is realized due to the capture effects. Therefore, with more partial sequences of intermediate wireless nodes allowing the concurrent data message forwarding, shorter end-to-end transmission delay and higher throughput of data messages are expected. This paper proposes a more concurrent data message transmission method with collision avoidance in consideration of the capture effects and a routing protocol detecting a wireless multihop forwarding route allowing the concurrent data message transmissions. Results of simulation experiments suggest the improvement of end-to-end throughput of data messages due to the concurrent data message.

关键词： Wireless communication parallel processing Throughput Ad hoc networks Routing protocols Delays Collision avoidance

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Performance Analysis of Image Classification between Edge and Cloud computing

Performance Analysis of Image Classification between Edge an...

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Natthasak Vechprasit Annop Monsakul Pramuk Boonsieng Faculty of Information Technology Thai-Nichi Institute of Technology Bangkok Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Image classification has become a major application in the AI era for connecting the physical and digital worlds. However, it requires intensive graphic processing power. IoT and Edge computing have become popular approaches for distributing and offloading the workload from the cloud to the edge. Many edge devices are powered by an energy-efficient processor that can't execute intensive workloads, but some may be able to. In this paper, we studied the overview of image classification implementation on edge and cloud computing and analyzed the performance to reveal the opportunity to implement a proper system architecture. The edge and cloud computing environments studied in this paper are a smartphone and a cloud GPU instance with sample applications to simulate real-world scenarios. The performance is based on the datasets and the processing environment comprising three factors: ML runtime, hardware, and network. Resulting in six factors: inference time, end-to-end execution time (including network delays), accuracy, confidence score, resource usage, and data transfer.

关键词： Cloud computing Accuracy Image edge detection Graphics processing units Feature extraction Hardware Data models Delays Image classification Testing

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Workload Prediction in Edge computing based on Graph Neural Network 19

Workload Prediction in Edge Computing based on Graph Neural ...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Miao, WeiWei Zeng, Zeng Zhang, Mingxuan Quan, Siping Zhang, Zhen Li, Shihao Zhang, Li Sun, Qi State Grid Jiangsu Elect Power CO LTD Informat & Telecommun Branch Nanjing Peoples R China State Grid Taizhou Power Supply Co Nanjing Peoples R China

ISBN: (纸本)9781665435741

In a typical edge computing paradigm, multiple edge servers are located near the end users to provide augmented computation and bandwidth. As the resources of the edge servers are limited, precisely predicting the workload of different edge servers can be of great importance in efficiently utilizing the edges. However, due to the dynamics of end users, the workload in the edge servers have lots of spikes. In this paper, we consider a deep learning method to predict the resource usage of edge servers. The main idea is to use graph neural network (GNN) to capture the interconnected topology of the edge servers. The edge servers that are close in proximity often have similar resource load patterns. The GNN is then concatenated with a LSTM layer to output the prediction value. The algorithm is evaluated in a real edge computing data set. The results show that our algorithm have high accuracy in predicting the resource usage. In addition, it outperforms other SOTA algorithms significantly.

关键词： Edge computing Workload Prediction Graph Neural Networks

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Research on Power Big Data Fusion Method Based on Graph Convolutional Neural Network

Research on Power Big Data Fusion Method Based on Graph Conv...

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Runyang Ji State Grid Nantong Power Supply Company Nantong Jiangsu

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

In recent years, in order to accelerate the construction of new power systems, power companies have carried out a series of information system construction, generating a massive amount of power related business data, which brings problems such as low query efficiency and inaccurate results to the real-time query performance of the system. Therefore, this article proposes a power big data fusion method based on graph convolutional neural networks, using association rule algorithms to horizontally connect various fragmented basic wide tables, indicators, and labels related to the perspective of power business, and aggregate them to form a logical business view. The association integrates more data information of power business objects, providing data support for power big data analysis, mining, and inference.

关键词： Companies Big Data parallel processing Real-time systems Inference algorithms Power systems Association rule learning Convolutional neural networks Business Information systems

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Adversarial Bidding Profiles for Mechanism Design

Adversarial Bidding Profiles for Mechanism Design

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parallel computing and distributed Systems (PCDS), international symposium on

作者： Senlong Xu Hao Wu School of Information Science and Engineering Yunnan University Kunming China

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

This study proposes a novel neural network architecture, CRegretNet, for designing revenue-maximizing mechanisms in multi-item auctions. CRegretNet incorporates concepts from exchangeable matrix mechanisms and permutation-equivariant neural networks to ensure fairness and scalability. By minimizing bidders' regret, CRegretNet encourages truthful bidding, which leads to efficient auction outcomes. We evaluate the performance of CRegretNet against multiple strong baselines in multiple experiments. In unknown optimal multi-item auctions, CRegretNet achieves the best performance. In known optimal settings, it demonstrates near-optimal performance. Moreover, CRegretNet exhibits good generalization capabilities, demonstrating its adaptability to varying auction sizes and scenarios. The results suggest that CRegretNet is a promising approach for designing efficient and fair auction mechanisms in practice.

关键词： Deep learning Scalability Neural networks Mechanism design Computer architecture parallel processing Feature extraction Optimization

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Digital In-Memory computing to Accelerate Deep Learning Inference on the Edge

Digital In-Memory Computing to Accelerate Deep Learning Infe...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Stefania Perri Cristian Zambelli Daniele Ielmini Cristina Silvano Università della Calabria Italy Università degli Studi di Ferrara Italy Politecnico di Milano Italy

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Deploying Deep Learning (DL) models on edge devices presents several challenges due to the limited set of processing and memory resources, and the bandwidth constraints while ensuring performance and energy requirements. In-memory computing (IMC) represents an efficient way to accelerate the inference of data-intensive DL tasks on the edge. Recently, several analog, digital, and mixed digital-analog memory technologies emerged as promising solutions for IMC. Among them, digital SRAM IMC exhibits a deterministic behavior and compatibility with advanced technology scaling rules making it a viable path for integration with hardware accelerators. This work focuses on discussing the potentially powerful aspects of digital IMC (DIMC) on edge System-on-Chip (SoC) devices. The limitations and open challenges of DIMC are also discussed.

关键词： Deep learning Performance evaluation Computational modeling Random access memory In-memory computing Transformers Energy efficiency

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An efficient hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm 19

An efficient hybrid MPI/OpenMP parallelization of the asynch...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Qiu, Qinnan Lei, Yongmei Wang, Dongxia Wang, Guozheng Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China

ISBN: (纸本)9781665435741

Alternating direction method of multipliers (ADMM) is an efficient algorithm to solve large- scale machine learning problems in a distributed environment. To make full use of the hierarchical memory model in modern highperformance computing systems, this paper implements a hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm (AH-ADMM). The AH-ADMM algorithm updates local variables in parallel by OpenMP threads and exchanges information between MPI processes, which relieves memory and communication pressure by replacing multiprocessing with multi- threading. Furthermore, for the SVM problem, the AH-ADMMalgorithm speeds up the calculation of sub- problems through an efficient parallel optimization strategy. This paper effectively combines the features of both algorithm design and programming model. Experiments on the Ziqiang4000 high-performance cluster demonstrate that the AH- ADMM algorithm scales better and run faster than the existing distributed ADMM algorithms implemented by pure MPI. The AH-ADMM can reduce the communication overhead by up to 91.8% and increase the convergence rate by up to 36x. For large datasets, the AH-ADMM can scale well on the cluster which over 129 cores.

关键词： distributed ADMM algorithm asynchronous communication hybrid parallel programming model MPI OpenMP

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