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Social Recommendation via Graph Attentive Aggregation 22nd

Social Recommendation via Graph Attentive Aggregation

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22nd international conference on parallel and distributed computing, Applications and Technologies (PDCAT 2021)

作者： Liufu, Yuanwei Shen, Hong Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9783030967727;9783030967710

Recommender systems play an important role in helping users discover items of interest from a large resource collection in various online services. Although deep graph neural network-based collaborative filtering methods have achieved promising performance in recommender systems, they are still some weaknesses. Firstly, existing graph neural network methods only take user-item interactions into account neglecting direct user-user interactions which can be obtained from social networks. Secondly, they treat the observed data uniformly without considering fine-grained differences in importance or relevance in the user-item interactions. In this paper, we propose a novel graph neural network social graph attentive aggregation (SGA) which is suitable for parallel training to boost efficiency which is the common bottleneck for neural network deployed machine learning models. This model obtains user-user collaborative information from social networks and utilizes self-attention mechanism to model the differentiation of importance in the user-item interactions. We conduct experiments on two real-world datasets and the results demonstrate that our method is effective and can be trained in parallel efficiently.

关键词： Recommendation system Social recommendation Graph neural network parallel computing

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ICMLC 2023 - Proceedings of the 2023 15th international conference on Machine Learning and computing

ICMLC 2023 - Proceedings of the 2023 15th International Conf...

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15th international conference on Machine Learning and computing, ICMLC 2023

ISBN: (纸本)9781450398411

The proceedings contain 96 papers. The topics discussed include: simulation analysis of binary antenna radiation field mathematical model of instrument landing system;quantum algorithm for regularized spectral clustering;parallel simulation of lid-driven flow with internal square obstacle;multi-missile path planning algorithm based on reinforcement learning;maximizing network reliability in large scale infrastructure networks: a heat conduction model perspective;a pipeline and a graph-based model for paragraph extraction: take the insurance document as an example;an improved bare bone multi-objective particle swarm optimization algorithm for solving stochastic wind-solar-small hydro power dispatch problems;distant supervision relation extraction of combination bag with hierarchical attention;multi-factor quantitative investment tactic design based on support vector machine;unexpectedly useful: convergence bounds and real-world distributed learning;stochastic compositional kernel estimation for Gaussian process models;and ensemble two stage machine learning for network abnormal detection.

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Communication-aware Quantization for Deep Learning Inference parallelization on Chiplet-based Accelerators 29

Communication-aware Quantization for Deep Learning Inference...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Zou, Kaiwei Qu, Songyun Li, Wen Wang, Ying Li, Huawei Liu, Yongpan Tsinghua University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China Shanxi University School of Computer and Information Technology Taiyuan China

ISBN: (纸本)9798350330717

It has recently become trendy for neural network accelerators to scale from single-core to chiplet-based multichip architecture, as the growth of neural network depth and complexity are calling for the promotion of computation and memory capabilities. However, the unintended extensive inter-chip communication of chiplet-based accelerator may bottleneck the parallelism of deep learning inference, which is undesirable for many real-time applications and energy-efficient devices. Although it is imperative for novel schemes to be devised to alleviate this problem, related works are scarce. In this work, we present CampQ, a fine-grained communication-aware mixed-precision quantization method to accelerate inference parallelization by reducing the major inter-chiplet communication overhead. By leveraging the AutoML technique, CampQ is capable of determining different bit-width to activation groups according to thier transmission distances in on-package network. The experimental results show 1.4×-2.6× performance benefits and 29%-60% energy reduction over the 16-bit models for various neural networks and parallelism approaches. © 2023 IEEE.

关键词： AutoML chiplet deep learning inference inter-chip communication quantization

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BALA-CPD: BALanced and Asynchronous distributed Tensor Decomposition

BALA-CPD: BALanced and Asynchronous Distributed Tensor Decom...

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IEEE international conference on Cluster computing (CLUSTER)

作者： Miao, Zheng Li, Jiajia Calhoun, Jon C. Ge, Rong Hangzhou Dianzi Univ Hangzhou Peoples R China North Carolina State Univ Raleigh NC USA Clemson Univ Clemson SC 29631 USA

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Tensor decomposition is widely used in machine learning, recommendation systems, and social networks. Large real-world tensors require parallel algorithms running on distributed memory systems. parallel algorithms suffer two major performance bottlenecks: load imbalance and communication cost, which are difficult to overcome due to the inherent tradeoff among the multiple types of computations and communications, especially for irregular sparse tensors. Previous work predominately focuses on balancing the load within the tensor-related computation, resulting in imbalance for multiple matrix-only computations and increased communication costs. It also extensively uses collective communication operations and bulk-synchronous computations by interleaving stages of global communication and stages of local computation, failing to hide the communication cost. In this paper, we present a novel algorithm BALA-CPD, which achieves the best overall workload balance, and effectively overlaps communication and computation for the popular distributed Canonical Polyadic Decomposition (CPD) algorithms. BALA-CPD uses a workload and data partition scheme that prioritizes the load balance for all the matrix-only computations and all the communications. When necessary, BALA-CPD adjusts to mitigate the load imbalance for the tensor-related computation. Departing from the bulk-synchronous approaches, BALA-CPD breaks down computation and communication in consecutive stages, and masks the communication costs by a combination of one-sided asynchronous communication and a fine-grained interleaving of communication and computation. We implement BALA-CPD and evaluate it on a 64-node cluster with 1280 processors. Experimental results show BALA-CPD is scalable and outperforms the state-of-the-art distributed implementations by up to 1.8x on 1280 processors.

关键词： sparse tensor tensor decomposition CPD asynchronous algorithm

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distributed and parallel Sparse computing for Very Large Graph Neural networks

Distributed and Parallel Sparse Computing for Very Large Gra...

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2022 IEEE international conference on Big Data, Big Data 2022

作者： Petit, Quentin R. Li, Chong Emad, Nahid Huawei Paris Research Center Université Paris-Saclay Boulogne-Billancourt France Huawei Paris Research Center Distributed and Parallel Software Lab Boulogne-Billancourt France Université Versailles Saint-Quentin Maison de la Simulation LI-PaRAD Saclay France

ISBN: (纸本)9781665480451

Deep learning (DL) requires high-performance processing on big data. Graph Neural networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph's sparse data structures is one of the key *** first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore. © 2022 IEEE.

关键词： Matrix algebra

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AccDP: Accelerated Data-parallel distributed DNN Training for Modern GPU-Based HPC Clusters 29

AccDP: Accelerated Data-Parallel Distributed DNN Training fo...

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29th Annual IEEE international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Alnaasan, Nawras Jain, Arpan Shafi, Aamir Subramoni, Hari Panda, Dhabaleswar K. Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781665494236

Deep Learning (DL) has become a prominent machine learning technique due to the availability of efficient computational resources in the form of Graphics Processing Units (GPUs), large-scale datasets and a variety of models. The newer generation of GPUs are being designed with special emphasis on optimizing performance for DL applications. Also, the availability of easy-to-use DL frameworks-like PyTorch and TensorFlowhas enhanced productivity of domain experts to work on their custom DL applications from diverse domains. However, existing Deep Neural Network (DNN) training approaches may not fully utilize the newly emerging powerful GPUs like the NVIDIA A100-this is the primary issue that we address in this paper. Our motivating analyses show that the GPU utilization on NVIDIA A100 can be as low as 43% using traditional DNN training approaches for small-to-medium DL models and input data size. This paper proposes AccDP-a data-parallel distributed DNN training approach-to accelerate GPU-based DL applications. AccDP exploits the Message Passing Interface (MPI) communication library coupled with the NVIDIA's Multi-Process Service (MPS) to increase the amount of work assigned to parallel GPUs resulting in higher utilization of compute resources. We evaluate our proposed design on different small-to-medium DL models and input sizes on the state-of-the-art HPC clusters. By injecting more parallelism into DNN training using our approach, the evaluation shows up to 58% improvement in training performance on a single GPU and up to 62% on 16 GPUs compared to regular DNN training. Furthermore, we conduct an in-depth characterization to determine the impact of several DNN training factors and best practices-including the batch size and the number of data loading workers- to optimally utilize GPU devices. To the best of our knowledge, this is the first work that explores the use of MPS and MPI to maximize the utilization of GPUs in distributed DNN training.

关键词： Deep Neural networks Graphics Processing Units Multi-Process Service MVAPICH2

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Network Parameter Control in Cellular networks through Graph-Based Multi-Agent Constrained Reinforcement Learning 19

Network Parameter Control in Cellular Networks through Graph...

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19th IEEE international conference on Automation Science and Engineering, CASE 2023

作者： Forsberg, Albin Larsson Nikou, Alexandros Feljan, Aneta Vulgarakis Tumova, Jana Stockholm Sweden Kth Division of Robotics Perception and Learning Department of Electrical Engineering and Computer Science Stockholm Sweden Digital Futures United States

ISBN: (纸本)9798350320695

Cellular networks are growing in complexity at increasing speed and the geographical locations in which they are deployed in are getting denser. Traditional control methods fall short in providing a scalable and dynamic way of adapting the network to new conditions. distributed multiagent reinforcement learning successfully addresses scalability problems seen in centralized approaches. The question of achieving learning with constraint satisfaction in distributed systems is still left unanswered in the state-of-the-art. In this work, we aim to perform distributed multi-agent constrained reinforcement learning in order to learn a policy online while satisfying imposed constraints. We use a coordination graph to model the interactions between agents and decompose the global value function. A conservative safety critic is trained in parallel to evaluate the safety of proposed actions. Our method allows for separate training of both the critic and the value network independently of each other, and hence offers flexibility in how and when to train the different models. The results are compared to a baseline using no safety critic. Simulations show that the agents succeed in learning a policy that can satisfy the constraints, while still maximizing the objective. © 2023 IEEE.

关键词： Graphic methods

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Adaptive Gradient Data Partition and Route Selection for distributed DNN Training 20th

Adaptive Gradient Data Partition and Route Selection for D...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Chai, Bo Tan, Xiaobin Yuan, Shenzhi Jia, Guangge Meng, Qiushi Wang, Weifeng Zhu, Shiyin University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China H3C Technologies Co. Limited Beijing China

ISBN: (纸本)9789819628636

As the scale of distributed training for Deep Neural Network (DNN) increases, communication has become a critical performance bottleneck in data center networks. In-Network Aggregation (INA) can accelerate aggregating gradients process, the critical communication bottleneck in distributed DNN training. Due to the limited processing capacity of network switch, existing aggregation approaches have turned to a combination of INA and Parameter Server (PS) methods. However, these approaches employ a fixed partitioning of the gradient data, leading to suboptimal performance. This paper proposes GPRS, an adaptive gradient aggregation framework for distributed DNN training, which adaptively combines the line-rate processing of INA and large memory capacity of PS. We present an adaptive gradient data partition scheme, which divides the gradient data into INA and PS aggregation. We propose a heuristic algorithm that determines the aggregation route selection by assigning priority to the available network paths. To validate our approach, we build a real-world experimental testbed and implement the proposed framework. Experimental results show that it can overcome the limitations of traditional INA-only methods and reduce communication time by 15% ∼ 52% for DNN training of varying scales. © IFIP international Federation for Information Processing 2025.

关键词： Data aggregation

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Convolutional graph neural network training scalability for molecular docking 31

Convolutional graph neural network training scalability for ...

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31st Euromicro international conference on parallel, distributed and Network-Based Processing, PDP 2023

作者： Crampon, Kevin Giorkallos, Alexis Baud, Stephanie Steffenel, Luiz Angelo Atos Se Center for Excellence in Advanced Computing Echirolles France Université de Reims Champagne-Ardenne Cnrs MEDyC Reims France Université de Reims Champagne-Ardenne Reims France

ISBN: (纸本)9798350337631

Deep learning use is growing in many numerical simulation fields, and drug discovery does not escape this trend. Indeed, before proceeding with in vitro and then in vivo experiments, drug discovery now relies on in silico techniques such as molecular docking to narrow the number of experiments and identify the best candidates. This method explores the receptor surface and the ligand's conformational space, providing numerous ligand-receptor poses. All these poses are then scored and ranked by a scoring function allowing to predict the best poses among all, then compare different ligands regarding a given receptor or different targets regarding a given ligand. Since the 2010s, numerous deep learning methods have been used to tackle this problem. Nowadays, there are two significant trends in deep learning for molecular docking: (i) the augmentation of available structural data and (ii) the use of a new kind of neural network: the graph convolutional neural networks (GCNs). In this paper, we propose the study of training scalability of a GCN-a molecular complex scoring function-on an increasing number of GPUs and with a variety of batch sizes. After a hyperparameter analysis, we achieve an 80% reduction in the training time, but this improvement sometimes involves a performance metrics degradation that the final users must ponder. © 2023 IEEE.

关键词： Ligands

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PS-Based Heterogeneous computing Framework

PS-Based Heterogeneous Computing Framework

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Next Generation Data-driven networks (NGDN), international conference on

作者： Xi Guo Ming He Shouhao Qin Dexuan Wang Chenjun Li Research Institute China United Network Communications Corporation Beijing China GuoChuang Software Co. Ltd. Hefei China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

In the current heterogeneous computing environment, different types of accelerated computing resources such as GPU(Graphics Processing Unit) and NPU(Neural Processing Unit) coexist in the cluster. However, because these resources use different communication protocols and architectures, heterogeneous computing resources cannot effectively communicate and collaborate. To solve this problem, we propose a heterogeneous distributed computing framework based on the Parameter Server(PS) architecture and achieve heterogeneous parallel computing capabilities across hardware devices. This allows users to uniformly use all available heterogeneous computing resources in the cluster to perform large-scale data parallel deep learning training tasks. We have conducted experiments and evaluations on this framework in multiple scenarios, and the results show that the NPU and GPU computing power in a heterogeneous computing cluster can be used simultaneously to complete a same computing task without affecting accuracy, while ensuring high training efficiency. Overall, this framework provides an effective solution for high-performance distributed computing in heterogeneous computing environments.

关键词： Training Deep learning Performance evaluation Protocols Processor scheduling Graphics processing units Computer architecture Heterogeneous networks distributed computing Artificial intelligence

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