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Grouping Synchronous to Eliminate Stragglers with Edge Computing in distributed Deep Learning 19

Grouping Synchronous to Eliminate Stragglers with Edge Compu...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Gui, Zhiyi Yang, Xiang Yang, Hao Li, Wei Zhang, Lei Qi, Qi Wang, Jingyu Sun, Haifeng Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Huawei Beijing Peoples R China

ISBN: (纸本)9781665435741

With the development of artificial intelligence(AI) applications, a large number of data are generated from mobile or IoT devices at the edge of the network. Deep learning tasks are executed to obtain effective information in the user data. However, the edge nodes are heterogeneous and the network bandwidth is limited in this case, which will cause general distributed deep learning to be inefficient. In this paper, we propose Group Synchronous parallel (GSP), which uses a density-based algorithm to group edge nodes with similar training speeds together. In order to eliminate stragglers, group parameter servers are responsible for coordinating communication of nodes in the group with Stale Synchronous parallel and aggregating the gradients of these nodes. And a global parameter server is responsible for aggregating the gradients from the group parameter servers to update the global model. To save network bandwidth, we further propose Grouping Dynamic Sparsification (GDS). It adjusts the gradient sparsification rate of nodes dynamically based on GSP so as to differentiates the communication volume and makes the training speed of all nodes tend to be the same. We evaluate GSP and GDS's performance on LeNet-5, ResNet, VGG, and Seq2Seq with Attention. The experimental results show that GSP speedups the training by 45% similar to 120% with 16 nodes. GDS on top of GSP can make up for some test accuracy loss, up to 0.82% for LeNet-5.

关键词： distributed deep training gradient compression parameter server gradient sparsification

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The GraphBLAS 3.0 Project

The GraphBLAS 3.0 Project

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

作者： Raye Kimmerer Timothy G. Mattson Scott McMillan Benjamin Brock Erik Welch Michel Pelletier José E. Moreira JuliaLab Massachusetts Institute of Technology Cambridge MA Human Learning Group Ocean Park WA Software Engineering Institute Carnegie Mellon University Parallel Computing Lab Intel Santa Clara CA NVIDIA Corporation Austin TX Graphegon Portland OR IBM Thomas J. Watson Research Center Yorktown Heights NY

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The GraphBLAS C API is mature with an updated specification (version 2.1) and a compliant implementation (SuiteSparse GraphBLAS). We are now focused on GraphBLAS 3.0; the next major GraphBLAS revision. Potential changes include: (1) a separate math spec to make new language bindings easier to write, (2) better support for user-defined types, rank promotion, and enhanced non-blocking execution, (3) expanded scope of GraphBLAS to address a wider range of applications, and (4) support for complex heterogeneous and distributed systems.

关键词： distributed processing Conferences Oral communication Libraries

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TFIDF Text Keyword Mining Method Based on Hadoop distributed Platform Under Massive Data

TFIDF Text Keyword Mining Method Based on Hadoop Distributed...

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Image processing and Computer applications (ICIPCA), IEEE international Conference on

作者： Zheng Dai Xinping Zhao Bingtao Cui Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Keyword extraction is an important part of text mining. Keywords extracted from text can explore the theme of the whole text and be further applied to text recommendation or text search. Under the wave of big data, the traditional text keyword extraction algorithm frequently appears in the low efficiency of the training and testing process on a single machine. Based on this, this paper proposes the TFIDF text classification algorithm based on the distributed platform of Hadoop, and gives the specific process of the algorithm implementation. The parallel TFIDF text classification algorithm considering the position of words in the document is implemented by MapReduce programming model, and compared with the traditional serial algorithm, the experiment is carried out in single machine and cluster mode. Experiments show that the proposed method improves the performance of the algorithm significantly, and can realize efficient information extraction at high speed in the face of massive data.

关键词： Training Text mining Image segmentation Accuracy Text categorization Clustering algorithms Programming Information retrieval Classification algorithms Testing

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MediNER: Understanding Diabetes Management Strategies Based on Social Media Discourse 19

MediNER: Understanding Diabetes Management Strategies Based ...

引用

19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Oyebode, Oladapo Orji, Rita Dalhousie Univ Fac Comp Sci Halifax NS Canada

ISBN: (纸本)9781665435741

Over the years, researchers have leveraged patients' discourse on social media to inform clinical and digital interventions. We contribute to these research efforts by mining and analyzing diabetes-related public posts on two social networks (online forums) with the aim of identifying management strategies adopted by diabetes patients and suggesting appropriate interventions. We develop a medical named entity recognition framework, MediNER, to identify named entities related to diabetes management and classify them into Food, Medication, Therapeutic Procedure, and Supplement. Our analysis shows that food-related strategies are most prevalent among both Type 1 and Type 2 diabetes patients, followed by medication-related strategies. Strategies involving supplements (such as vitamins) are the least utilized. We also investigate for gender differences in the strategies employed by diabetes patients. Finally, we offer design recommendations for digital interventions aimed at diabetes self-management based on our findings.

关键词： social media diabetes natural language processing named entity recognition application framework intervention design

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LARA: Latency-Aware Resource Allocator for Stream processing applications

LARA: Latency-Aware Resource Allocator for Stream Processing...

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Euromicro Conference on parallel, distributed and Network-Based processing

作者： Priscilla Benedetti Giuseppe Coviello Kunal Rao Srimat Chakradhar NEC Laboratories America Inc. Princeton NJ USA

One of the key metrics of interest for stream processing applications is “latency”, which indicates the total time it takes for the application to process and generate insights from streaming input data. For mission-critical video analytics applications like surveillance and monitoring, it is of paramount importance to report an incident as soon as it occurs so that necessary actions can be taken right away. Stream processing applications are typically developed as a chain of microser-vices and are deployed on container orchestration platforms like Kubernetes. Allocation of system resources like “cpu” and “memory” to individual application microservices has direct impact on “latency”. Kubernetes does provide ways to allocate these resources e.g. through fixed resource allocation or through vertical pod autoscaler (VPA), however there is no straight-forward way in Kubernetes to prioritize “latency” for an end-to-end application pipeline. In this paper, we present LARA, which is specifically designed to improve “latency” of stream processing application pipelines. LARA uses a regression-based technique for resource allocation to individual microservices. We implement four real-world video analytics application pipelines i.e. license plate recognition, face recognition, human attributes detection and pose detection, and show that compared to fixed allocation, LARA is able to reduce latency by up to 2.8X and is consistently better than VPA. While reducing latency, LARA is also able to deliver over 2X throughput compared to fixed allocation and is almost always better than VPA.

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Joint Request Offloading and Resource Allocation for Energy Efficient D2D Enabled Multi-type Inference Services 21

Joint Request Offloading and Resource Allocation for Energy ...

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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： Zeng, Ziyu Huang, Jiale Wu, Jiaxin Wu, Jigang Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China

ISBN: (纸本)9798350329223

Tremendous research efforts have devoted in providing multi-type inference services for users by deploying heterogeneous models in edge servers (ESs). But, resource contention in ES can degrade the quality-of-service (QoS) of users. To obtain the services with QoS guarantee, users can leverage device-to-device (D2D) collaboration to share the models and resources in mobile devices (MDs) with each other. However, it brings non-trivial energy consumption to MDs, and thus dramatically decreases their battery life. To fill this gap, this paper formulates an energy efficiency optimization problem for D2D enabled multi-type inference services. The goal is to minimize the total energy consumption of MDs, by jointly optimizing the decisions for request offloading and resource allocation under the constraints of resources and QoS. The formulated problem is NP-hard, as it is a mixed integer nonlinear programming problem. To solve the problem, this paper transforms it into an equivalent master request offloading (RO) problem with a low-complexity resource allocation (RA) subproblem. A two-level optimization algorithm problem is proposed for the transformed problem. In the outer level, a low-complexity heuristic algorithm is proposed to iteratively seek the optimal request offloading decision for RO problem. In the inner level, a subgradient based algorithm is proposed to derive the optimal resource allocation decision for RA subproblem. Experimental results show that, the proposed algorithms achieve the best performance, in terms of energy efficiency for all cases, in comparison to the baseline algorithms. © 2023 IEEE.

关键词： Resource allocation

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HiHiSpMV: Sparse Matrix Vector Multiplication with Hierarchical Row Reductions on FPGAs with High Bandwidth Memory 32

HiHiSpMV: Sparse Matrix Vector Multiplication with Hierarchi...

引用

32nd IEEE Annual international symposium on Field-Programmable Custom Computing Machines, FCCM 2024

作者： Tareen, Abdul Rehman Meyer, Marius Plessl, Christian Kenter, Tobias Paderborn University Paderborn Center for Parallel Computing Department of Computer Science Warburger Str. 100 Paderborn33098 Germany

ISBN: (纸本)9798350372434

The multiplication of a sparse matrix with a dense vector is a vital operation in linear algebra, with applications in numerous contexts. After earlier research on FPGA acceleration of this operation had shown the potential to achieve a high bandwidth efficiency, this workload has received renewed attention with the introduction of high bandwidth memory on FPGA platforms. However, previous designs fell short of scaling to the full bandwidth potential of current FPGA platforms with high bandwidth memory. In this work, we present HiHiSpMV with a novel design approach around hierarchical accumulation, which allows us to overcome several limitations of the related work. Our design is completely implemented with high-level synthesis and compiled with Vitis, and instantiates 16 independent compute units, each processing up to 16 matrix elements per clock cycle. The reduction of these elements is performed without any latency or resource overhead and the subsequent accumulation uses the well-established shift-register design pattern. Due to the independent nature of compute units, our design can connect to all of the 32 high bandwidth memory pseudo-channels in 512-bit interface mode on an Alveo U280 FPGA board. In our tests, we reach up to 86% of the theoretical available bandwidth of this FPGA platform, enabling a computational throughput of up to 98 GFLOPS. This is about 1.5 × faster than the peak throughput of the best related work in that regard, Serpens. On average, the throughput of HiHiSpMV is even 2.7× higher than Serpens. © 2024 IEEE.

关键词： Shift registers

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distributed V2V Computing Offloading Method Based on Delay and Fairness Awareness 19

Distributed V2V Computing Offloading Method Based on Delay a...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Yang, Guoliang Xiong, Mengqi Feng, Guangsheng Liu, Yuzheng Huang, Yun Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China China Aerosp Sci & Ind Corp Res Inst 2 Beijing Peoples R China

ISBN: (纸本)9781665435741

The roadside Basic unit (RSU) can relieve the heavy computing and analysis pressure required by vehicles'computing tasks. Due to the mobility of vehicles and limited computing resources, it is difficult to deal extensive vehicle tasks in time. Therefore, we consider the computation task offloading problem between the vehicles, as well as the dynamic topology caused by the vehicle movement and the randomness of task arrival, and propose an online offloading method based on fairness awareness, which can be ensured tolerable delays and the requirement of the quality of experience (QoE) between vehicles by introducing delay-aware and price-constrained virtual queues. By Lyapunov optimization technology, the objective function is transformed into a drift-plus-penalty profit minimum bound problem, achieveing all the offloading decisions by the realtime resource requirements and topology state. Finally, extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

关键词： Mobile computing offloading vehicle-to-vehicle communication delay tolerance fairness Lyapunov optimization

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Accelerating Message Passing Operation of GDL-Based Constraint Optimization Algorithms Using Multiprocessing 19

Accelerating Message Passing Operation of GDL-Based Constrai...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Zaoad, Syeed Abrar Tanjim, Tauhid Hasan, Mir Mamun-Or-Rashid, Md Almansour, Ibrahem Abdullah Khan, Md Mosaddek Univ Dhaka Dept Comp Sci & Engn Dhaka 1000 Bangladesh Austin Peay State Univ Dept Comp Sci & Informat Technol Clarksville TN 37040 USA King Abdulaziz City Sci & Technol Dept Comp Sci & Informat Technol Riyadh Saudi Arabia

ISBN: (纸本)9781665435741

This paper develops a new message passing protocol that can be used to speed up the inference process of Generalized Distributive Law (GDL) based constraint optimization algorithms. In particular, we parallelize the inference process of the GDL-based constraint optimization algorithms which have been widely used to solve Constraint Optimization Problems (COPs) in different real world applications, such as wire routing, image analysis, computer-aided gas pipeline operation, and numerical optimization. It is worth noting that the proposed new parallel approach can be applied to accelerate any GDL-based message passing algorithms, such as Max-Sum, Max-Product or Sum-Product. In this work, we make use of the available Central processing Unit (CPU) cores of a system to minimize the time it requires to execute a given algorithm. Consequently, this reduced computation time will accelerate the GDL-based COP algorithms which can be used in larger systems. However, the main challenge is to maintain the quality of the solution while minimizing the completion time. Our proposed protocol specifically takes this trade-off into account, and our empirical results depict that it is able to produce the same solution quality while accelerating the inference process 2-10 times faster compared to the state-of-theart.

关键词： COP Generalized distributive law parallel computing Speeding up

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A Performance Prediction Model for Structured Grid Based applications in HPC Environments

A Performance Prediction Model for Structured Grid Based App...

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international symposium on parallel and distributed Computing

作者： Md Bulbul Sharif Thomas Hines Sheikh Ghafoor Mario Morales-Hernández Tigstu Dullo Alfred Kalyanapu Computer Science Tennessee Technological University Cookeville TN USA Computational Science and Engineering Oak Ridge National Laboratory Oak Ridge TN USA Civil and Environmental Engineering Tennessee Technological University Cookeville TN USA

Predicting the performance of parallel applications at scale is a challenging problem. We have developed a performance prediction model for structured grid-based scientific applications for High Performance Computing systems. Our model can capture the system complexity and consider computation and communication attributes of application performance on HPC architectures. We have also proposed a methodology for obtaining the realistic value of the model parameters by small-scale sample runs of an application on the target system. We have used our model to predict the performance of an actual application (2D Flood Simulation) and a synthetic application (Game of Life) on the Summit supercomputer at Oak Ridge National Laboratory and Stampede2 at Texas Advanced Computing Center. The experimental results indicate that our model’s predictive performance is acceptable, and our model was able to predict the performance of these two applications on Summit and Stampede2 with more than 90% accuracy.

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