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Cost-Effective Dynamic Alliance Pricing Mechanism Based on distributed Edge-Intelligence 21

Cost-Effective Dynamic Alliance Pricing Mechanism Based on D...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Cao, Zhihan Zheng, Xi Guo, Jianxiong Jia, Weijia Xu, Yang Wang, Tian Beijing Normal University Zhuhai China Fudan University Shanghai China Macquarie University Australia Hunan University Changsha China

ISBN: (纸本)9798350329223

In Internet of Thing (ToT) system based on edge intelligence, pay-for-use demand has become a consensus, and the pricing of ToT services has attracted the attention of academia and industry. The pricing method based on non-cooperative game allows edge service providers to compete fairly, effectively preventing edge nodes from malicious bidding. However, in this scenario, a task can only be assigned to one edge vendor. Because only one winner can profit each time, it is easy to create a large number of edge service providers who cannot profit for a long time due to insufficient incentives. To this end, a dynamic alliance pricing mechanism based on distributed edge-intelligence is proposed. Edge service providers can freely choose to form an edge dynamic alliance, which not only retains the independence of edge nodes, but also makes full use of the advantages of mutual cooperation between nodes. Tn order to prevent abnormal behaviors of edge nodes, we set up necessary restrictive rules. At the same time, we designed a privacy-enhanced joint pricing prediction algorithm to screen the alliance's candidate solutions to improve pricing efficiency and edge benefit. The experimental results show that, compared with the traditional game method, the performance of the mechanism we proposed improves the utilization rate of edge resources by 32.76%-61.37%. Meanwhile, the prediction accuracy was improved by 16.47%38.86%, and the average prediction time was reduced by 42.81 %-65.57%. © 2023 IEEE.

关键词： Profitability

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Hybrid Quantum-Classical computing in Federated Learning With Data Heterogeneity 35

Hybrid Quantum-Classical Computing in Federated Learning Wit...

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35th IEEE international symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Hisamori, Keita Chiang, Yi-Han Lin, Hai Ji, Yusheng Osaka Metropolitan University Department of Electrical and Electronic Systems Engineering Osaka Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo Japan

ISBN: (纸本)9798350362244

Federated learning (FL) has emerged as a promising technique to realize distributed machine learning (ML) in practice. FL enables multiple clients to collaboratively train a common ML model without the need to collect raw data from clients, which therefore has merit in the protection of data privacy. On the other hand, it is known that quantum computing excels in solving specific problems that are computationally prohibitive on classical computers due to its ability to harness quantum superposition and entanglement. However, current noisy intermediate-scale quantum (NISQ) computers have difficulties in dealing with many-qubit computation due to the lack of reliable error-correction schemes, which renders hybrid quantum-classical computing with few qubits a promising alternative. In this paper, we investigate how hybrid quantum-classical computing can be applied to FL while considering the data heterogeneity among clients. To this end, we propose the two-qubit quantum circuit-embedded convolutional neural network (2QCNN) for each client, which incorporates parallel two-qubit variational quantum circuits (VQCs) between the fully connected layers of the CNN. Simulation results show that 2QCNN outperforms the comparison schemes in terms of test accuracies. In addition, the impacts of the number and depth of parallel two-qubit VQCs on the performance of 2QCNN under various degrees of data heterogeneity are further evaluated. © 2024 IEEE.

关键词： Qubits

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Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer 36

Proteome-scale Deployment of Protein Structure Prediction Wo...

引用

36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Gao, Mu Coletti, Mark Davidson, Russell B. Prout, Ryan Abraham, Subil Hernandez, Benjamin Sedova, Ada Georgia Inst Technol Ctr Study Syst Biol Atlanta GA 30332 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN USA Oak Ridge Natl Lab Biosci Div Oak Ridge TN 37830 USA Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN USA

ISBN: (纸本)9781665497473

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic experiments, and with accelerators like GPUs and TPUs making inference using large models rapid, genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold v.2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 40,526 protein sequences, corresponding to four prokaryotic proteomes and one plant proteome, using under 4,400 total Summit node hours, equivalent to using the majority of the supercomputer for a little over one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.

关键词： high-performance computing protein structure prediction deep learning proteomics workflow management software

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Cross-subject Drowsiness Recognition Model Based on the Multi-scale parallel Convolution of EEG Channels 21

Cross-subject Drowsiness Recognition Model Based on the Mult...

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作者： Li, Songquan Zhu, Rongbo Yuan, Jun Wan, Shaohua Huazhong Agricultural University College of Informatics Wuhan430070 China Huazhong Agricultural University Shenzhen Institute of Nutrition and Health Wuhan430070 China Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory Ministry of Agriculture Shenzhen518000 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518000 China

ISBN: (纸本)9798350329223

Cross-subject electroencephalogram (EEG) drowsiness recognition is currently one of the most efficient methods. However, the traditional cross-subject approaches overlook the correlation between channel sub-features and effectively utilize the potential connection between frequency features and drowsiness, making it difficult to extract shared features from differentiated subjects. This paper proposes a novel deep learning model called CPCNN to address the challenging of cross-subject drowsiness recognition without calibration and improve accuracy. CPCNN obtains delta frequency band features related to drowsiness from sub-features in the channel dimension, effectively combining the advantages of delta frequency bands in EEG with the richness of channel features. To capture the common features of drowsiness, a channel slicing module is proposed, which divides the spatial and channel features extracted by the deep separable convolution module into multiple sub-features along the channel dimension. In addition, to further improve cross-subject recognition performance, a multi-scale parallel group convolution is designed to capture four key frequency sub-feature groups within the delta frequency band. The experimental results show that CPCNN boosts the recognition performance, significantly outperforms all strong baselines. On balanced and unbalanced public datasets, the average accuracy of CPCNN is 81.49% and 78.26%, respectively, surpassing the excellent baseline method by 3.14% and 0.56%. © 2023 IEEE.

关键词： Electroencephalography

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

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Efficient Large Scale Reverse-time Migration Imaging Computation based on distributed Spark Cluster with GPUs 21

Efficient Large Scale Reverse-time Migration Imaging Computa...

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作者： Wangzhang, Suhui Yang, Ruizhang Gu, Rong Li, Bo Liu, Dingjin Wang, Zhaokang Nanjing University State Key Laboratory for Novel Software Technology China Sinopec Geophysical Research Institute China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798350329223

The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. Thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee. © 2023 IEEE.

关键词： Graphics processing unit

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Coded parallelism for distributed Deep Learning

Coded Parallelism for Distributed Deep Learning

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2023 IEEE international symposium on Information Theory, ISIT 2023

作者： Ji, Songting Zhang, Zhaoyang Yang, Zhaohui Jin, Richeng Yang, Qianqian College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China Zhejiang University International Joint Innovation Center Haining314400 China

ISBN: (纸本)9781665475549

With the rapid development of deep learning, the parameters of modern neural network models, especially in the field of Natural Language Processing (NLP) are extremely huge. When the parameters of the model are larger even than the storage memory of a single device, it is necessary to split the original big learning model into different parts with each part assigned to one device, thus realizing joint model training over different devices (i.e., distributed training). In this paper, we aim to introduce the advanced coding scheme into the distributed parallel framework, which leads to the perfect combination of coding and the underlying calculation of neural networks. The proposed scheme is not only able to avoid the impact of poor computing power or low bandwidth and even dropped devices (stragglers) on system performance but also reduce the communication load between different devices, thereby greatly improving the performance of distributed parallel systems. © 2023 IEEE.

关键词： Deep learning

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Modeling Matrix Engines for Portability and Performance 36

Modeling Matrix Engines for Portability and Performance

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Tukanov, Nicholai Srinivasaraghavan, Rajalakshmi Moreira, Jose E. Low, Tze Meng Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA IBM Syst Austin TX USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781665481069

Matrix engines, also known as matrix-multiplication accelerators, capable of computing on 2D matrices of various data types are traditionally found only on GPUs. However, they are increasingly being introduced into CPU architectures to support AI/ML computations. Unlike traditional SIMD functional units, these accelerators require both the input and output data to be packed into a specific 2D-data layout that is often dependent on the input and output data types. Due to the large variety of supported data types and architectures, a common abstraction is required to unify these seemingly disparate accelerators and more efficiently produce high-performance code. In this paper, we show that the hardware characteristics of a vast array of different matrix engines can be unified using a single analytical model that casts matrix engines as an accumulation of multiple outer-products (also known as rank-k updates). This allows us to easily and quickly develop high-performance kernels using matrix engines for different architectures. We demonstrate our matrix engine model and its portability by applying it to two distinct architectures. Using our model, we show that high-performance computational kernels and packing routines required for high-performance dense linear algebra libraries can be easily designed. Furthermore, we show that the performance attained by our implementations is around 90-99% (80-95% on large problems) of the theoretical peak throughput of the matrix engines.

关键词： MMA VNNI Matrix Engine High Performance computing Dense Linear Algebra

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parallel Minimum Spanning Tree Algorithms via Lattice Linear Predicate Detection 36

Parallel Minimum Spanning Tree Algorithms via Lattice Linear...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Alves, David R. Garg, Vijay K. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)9781665497473

We show that the problem of computing the minimum spanning tree can be formulated as special case of detecting Lattice Linear Predicate (LLP). In general, formulating problems as LLP presents two main advantages: 1) Different problems are formulated under a single, general framework, which defines the problem in terms of simple local predicates that must hold for the all the elements of a lattice, making the problem (and the solution) compact and easy to understand. 2) improvements on one set of problems can be transferable to other sets of problems;3) since the problems are stated as a set of local predicates, which can be often tested with little or no synchronization it is often the case that new opportunities for parallelism present themselves. In this paper we introduce two parallel algorithms LLP-Prim and LLP-Boruvka that improve on the non-LLP counterparts in several ways. LLP-Prim reduces the number of heap operations required by Prim by allowing edges to be selected without entering the heap thus allowing for parallelism. LLP-Boruvka improves on Boruvka by reducing synchronization and thus once more improving parallelism opportunities. Our experimental evaluation shows that LLP-Prim is faster than Prim's algorithm in both single threaded and multithreaded scenarios and that it provides a good tradeoff between parallelism and efficiency at low core counts. For higher core count scenarios we show how LLP-Boruvka improves on an efficient implementation of a parallel version of Boruvka.

关键词： Minimum Spanning Tree parallel Algorithms

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Heterogeneous GPU and FPGA computing: a VexCL case-study 36

Heterogeneous GPU and FPGA computing: a VexCL case-study

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Laan, Tristan Varbanescu, Ana-Lucia Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665497473

FPGA-based accelerators are capturing the interest of the HPC domain, primarily due to their superior energy-efficiency compared to more common accelerators, like GPUs. However, enabling HPC codes to use FPGA-based accelerators (efficiently) remains a difficult task. One interesting, fast-track solution to this problem is to extend the domain-specific, high-level languages, libraries, or APIs that already support other accelerators (e.g., GPUs) to target FPGAs. In this work we demonstrate the added value of such an approach by adding FPGA support to VexCL, a vector expression template library for OpenCL/CUDA. To this end, we use the VexCL-generated OpenCL code as intermediate representation, while creating code-skeletons to implement the FPGA code and all necessary data links between the host and accelerator. We further support five generic optimizations for the FPGA code. We demonstrate our approach on two use-cases, an affine transformation and an SpMV calculation, showcasing the performance and energy consumption of the resulting FPGA versions. We further demonstrate that the FPGA code can outperform the VexCL-generated GPU version. To illustrate the integration of GPU and FPGA code, we also demonstrate the performance of an VexCL SpMV application using a heterogeneous GPU+FPGA system. Our results indicate that, indeed, the integration of the two accelerators is seamless. Performance-wise, however, the heterogeneous version does not outperform the FPGA-only one.

关键词： Heterogeneous computing FPGA plus GPU task parallelism data parallelism OpenCL VexCL Xilinx

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