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An Adaptive Teacher-Student Framework for Real-time Video Inference in Multi-User Heterogeneous MEC Networks 29

An Adaptive Teacher-Student Framework for Real-time Video In...

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

作者： Lu, Yuan Ge, Shuxin Wang, Weixu Zhou, Xiaobo Qiu, Tie Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China

ISBN: (纸本)9798350330717

Teacher-student learning has emerged as a promising framework for real-time video inference on mobile devices in multi-access edge computing (MEC) networks, where heavyweight teacher models are deployed on edge servers, and lightweight student models distilled from teacher models are deployed on mobile devices. To deal with data drift and maintain the inference accuracy, the student model has to be updated periodically with the help of the teacher model through a training process. Different training configurations, such as training epochs and frozen layers, lead to different accuracy improvements with different resource requirements. However, in multi-user heterogeneous MEC networks, due to resource heterogeneity and limited computing resources of edge servers, it is quite challenging to update all the student models simultaneously to achieve high inference accuracy. To address this problem, in this paper, we propose an adaptive teacher-student framework in multi-user heterogeneous MEC networks. The key idea is to adaptively make optimal updating decisions (i.e., offloading decision and configuration selection decision) for each user, where the available resources of edge servers and network conditions are taken into account. First, we model the teacher-student collaborative video inference problem as an optimization problem with the aim of maximizing the average inference accuracy. Then, we propose an evolutionary deep reinforcement learning algorithm, CEM-MASAC, to solve this problem. Finally, trace-driven simulations employing real-world bandwidth traces demonstrate the superiority of our algorithm compared to the baseline methods. © 2023 IEEE.

关键词： deep reinforcement learning model updating multi-access edge computing teacher-student learning video inference

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PDC Course Development and Assessment Process for the betterment of Teaching-Learning Process 29

PDC Course Development and Assessment Process for the better...

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

作者： Bayyapu, Neelima Manipal Acad Higher Educ MAHE Manipal Inst Technol MIT Dept Comp Sci & Engn Manipal 576104 Karnataka India

ISBN: (纸本)9798350333886

Outcome-based education is being adapted in most of the engineering colleges across India. This process is being accredited by a national body called National Board of Accreditation (NBA) in India. This accreditation process involves a continuous process of defining Program Outcomes (POs) and Course Outcomes (COs) and these are evaluated to check the OBE outcome level. This course evaluation includes course content, evaluation methods used for students' evaluation, pedagogy of teaching, Bloom's taxonomy mapping of learning levels, feedback from stakeholders and results and outcome analysis. If the required level is not attained, the COs and POs are reviewed and accordingly curriculum is revised. There are a lot of challenges being faced by faculty in this process of creating and assessing the curriculum as per NBA standards. These challenges are unique and non-trivial for PDC/HPC (parallel and distributed computing/High-Performance computing) courses. The preparation of OBE-based PDC/HPC course involves a lot of stakeholders and brainstorming over multiple sessions. Many universities are adopting PDC/HPC courses more recently across the world. With an intention to create a pointer for developing, delivering and reviewing a PDC/HPC course, this paper presents the course development process for the benefit of various stakeholders and specially PDC/HPC educators and institutions. This research paper presents the undergraduate teaching experience of parallel computing (PC) course with a critical evaluation based on course Outcomes (COs) and Bloom's taxonomy mapping of learning levels. This paper compiles a list of challenges faced by PDC/HPC educators and stakeholders focusing on the Indian education scenario. It lists the activities and the suggestions that can be applied to address these challenges suitably. The research and teaching experience-based discussions and strategies proposed in this paper help other PDC/HPC educators and stakeholders in the CS (Computer Sci

关键词： Undergraduate Education parallel and distributed computing (PDC) Education High-Performance computing (HPC) Education Computer Science Community Indian Education Bloom's Taxonomy Learning Levels Course Outcomes parallel computing Course

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Near-Linear Scaling Data parallel Training with Overlapping-Aware Gradient Compression 29

Near-Linear Scaling Data Parallel Training with Overlapping-...

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

作者： Meng, Lin Sun, Yuzhong Li, Weimin Chinese Academy of Sciences Institute of Computing Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350330717

Existing Data parallel (DP) trainings for deep neural networks (DNNs) often experience limited scalability in speedup due to substantial communication overheads. While Overlapping technique can mitigate such problem by paralleling communication and computation in DP, its effectiveness is constrained by the high communication-to-computation ratios (CCR) of DP training tasks. Gradient compression (GC) is a promising technique to obtain lower CCR by reducing communication volume directly. However, it is challenging to obtain real performance improvement by applying GC into Overlapping because of (1) severe performance penalties in traditional GCs caused by high compression overhead and (2) decline of Overlapping benefit owing to the possible data dependency in GC schemes. In this paper, we propose COVAP, a novel GC scheme designing a new coarse-grained filter, makes the compression overhead close to zero. COVAP ensures an almost complete overlap of communication and computation by employing adaptive compression ratios and tensor sharding tailored to specific training tasks. COVAP also adopts an improved error feedback mechanism to maintain training accuracy. Experiments are conducted on Alibaba Cloud ECS instances with different DNNs of real-world applications. The results illustrate that COVAP outperforms existent GC schemes in time-to-solution by 1.92x-15.39x and exhibits near-linear scaling. Furthermore, COVAP achieves best scalability under experiments on four different cluster sizes. © 2023 IEEE.

关键词： data parallel distributed deep learning gradient compression Overlapping

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A Discrete Grey Wolf Optimizer Metaheuristic for Task Offloading in Multi-Server MEC with Batteryless Devices 29

A Discrete Grey Wolf Optimizer Metaheuristic for Task Offloa...

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

作者： Tang, Yinyin Yin, Guichang Cong, Peijin Sun, Jin Zhou, Junlong Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798350330717

The maturation of energy harvesting technologies enables the integration of batteryless devices in advanced computing paradigms. For example, in mobile edge computing (MEC), batteryless mobile devices can charge when they have insufficient energy to perform task offloading, thereby relaxing the energy constraints in developing offloading strategies. This paper studies the problem of minimizing the latency of task execution in an MEC system with multiple resource-limited servers and multiple batteryless devices under intermittent operation conditions. We formulate this problem as an integer program-based optimization model and propose a discrete grey wolf optimizer (DGWO) algorithm to solve the formulated problem. DGWO uses a task sequence, which is a permutation of all tasks to be offloaded, to represent an offloading solution and introduces a discrete representation of grey wolves to link each grey wolf with a solution. For each discrete grey wolf, we design an effective task allocation strategy to designate the computing resources of MEC servers for each offloaded task. We further define a set of discrete operations upon the discrete representation to update the positions of grey wolves, for the purpose of enhancing DGWO's global search capability. Experimental results demonstrate that DGWO outperforms other baseline metaheuristics with reduced task execution latency and improved computational efficiency. © 2023 IEEE.

关键词： batteryless devices discrete grey wolf optimizer mobile edge computing task offloading

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A performance-oriented comparative study of the Chapel high-productivity language to conventional programming environments 13

A performance-oriented comparative study of the Chapel high-...

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13th international Workshop on Programming Models and Applications for Multicores and Manycores (PMAM) part of PPoPP conference

作者： Helbecque, Guillaume Gmys, Jan Carneiro, Tiago Melab, Nouredine Bouvry, Pascal Univ Lille CNRS CRIStAL UMR 9189 Inria Lille Nord Europe Lille France Univ Luxembourg FSTM Esch Sur Alzette Luxembourg Univ Luxembourg DCS FSTM SnT Luxembourg Luxembourg

ISBN: (纸本)9781450393393

The increase in complexity, diversity and scale of high performance computing environments, as well as the increasing sophistication of parallel applications and algorithms call for productivity-aware programming languages for high-performance computing. Among them, the Chapel programming language stands out as one of the more successful approaches based on the Partitioned Global Address Space programming model. Although Chapel is designed for productive parallel computing at scale, the question of its competitiveness with well-established conventional parallel programming environments arises. To this end, this work compares the performance of Chapel-based fractal generation on shared- and distributed-memory platforms with corresponding OpenMP and MPI+X implementations. The parallel computation of the Mandelbrot set is chosen as a test-case for its high degree of parallelism and its irregular workload. Experiments are performed on a cluster composed of 192 cores using the French national testbed Grid'5000. Chapel as well as its default tasking layer demonstrate high performance in shared-memory context, while Chapel competes with hybrid MPI+OpenMP in distributed-memory environment.

关键词： Chapel MPI Multi-core OpenMP parallel computing Productivity-awareness

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Proceedings - 2020 IEEE international Symposium on parallel and distributed Processing with Applications, 2020 IEEE international conference on Big Data and Cloud computing, 2020 IEEE international Symposium on Social computing and Networking and 2020 IEEE international conference on Sustainable computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020

Proceedings - 2020 IEEE International Symposium on Parallel ...

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18th IEEE international Symposium on parallel and distributed Processing with Applications, 10th IEEE international conference on Big Data and Cloud computing, 13th IEEE international Symposium on Social computing and Networking and 10th IEEE international conference on Sustainable computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020

ISBN: (纸本)9781665414852

The proceedings contain 202 papers. The topics discussed include: multi-relational graph convolutional networks for skeleton-based action recognition;identifying the right person in social networks with double metaphone codes;a novel strong cache consistency mechanism in ICN based on role division and lease model;demystifying the largest live game streaming platform via black-box measurement;framework-agnostic optimization of repeated skewed joins at massive scale;remote procedure call optimization of big data systems based on data awareness;MCSEC: secure coded matrix multiplication scheme for edge computing with minimum communication cost;low-rate DoS attack detection based on WPD-EE algorithm;user identity linkage across location-based social networks with spatio-temporal check-in patterns;optimizing 3-D placement of multiple UAVs based on Taguchiâ€™s method;and feature envy detection based on Bi-LSTM with self-attention mechanism.

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F3A: Fairness-Aware AI-Workloads Allocation Considering Multidimensional User Demands in JointCloud 15

F3A: Fairness-Aware AI-Workloads Allocation Considering Mult...

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15th IEEE international conference on Joint Cloud computing (JCC)

作者： Yang, Jiacheng Yi, Guodong Gao, Fei Shi, Peichang Wang, Huaimin Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Proc Changsha 410073 Peoples R China Xiangjiang Lab Changsha 410073 Peoples R China Hunan Univ Technol & Business Sch Adv Interdisciplinary Studies Changsha 410073 Peoples R China

ISBN: (纸本)9798350387339

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users' demands while ensuring CSPs' fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users' multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

关键词： JointCloud computing Fairness Task Scheduling AI Workloads AI data center

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A parallelizable Counterfactual Generation Method Based on Gradient Optimization 29

A Parallelizable Counterfactual Generation Method Based on G...

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

作者： Ding, Haorun Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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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...

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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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A Review on Microgrid Protection Issues And Their Counter-measures 2

A Review on Microgrid Protection Issues And Their Counter-me...

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2nd IEEE international conference on advances in Electrical, computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Sahu, Umesh Kumar Rao, B. Chiranjeev Patel, Sandeep Kumar Shri Shankaracharya Tech Campus Elect & Elect Engn Dept Bhilai India Maulana Azad Natl Inst Technol Elect Engn Dept Bhopal India

ISBN: (纸本)9781665411202

Microgrid has become progressively alluring among the consumers, consequently, there will be a tremendous increment in its installation in the forthcoming years. It has an advantage that it can be handled in both (i.e. Grid connected & Islanded Mode) modes of operations. Its main sources of power are PV arrays, Micro- turbines, FuelCells, Battery storage system, Wind-turbines and the list continues. All these sources are called distributed Energy Resources (DER's). DER's are some sort of localized energy sources, setup at the place where it is required. DER's is one of the fastest emerging sources of power in the current story line. It has helped in dwindling our dependence in the utility grid, lessen the losses and chiefly, being cost-effective. But, the addition of DER's has drastically affected the peculiarities of the microgrid, making its operation more complex and complicated in nature. Meanwhile, it has affected the protection system in an intense way. The bi-directional current flow, irregular shift of the system voltage-profile, alteration of the system-dynamic behavior and a lot more. Apart from this, the precipitous addition of the power electronic devices in the model affects the fault level and are slowly-&-gradually affecting the persuasiveness of the traditionally used protection system. The different obstacles and their consequences on the microgrid have been discussed.

关键词： distributed energy resources microgrid point-of-common-coupling distribution network sympathetic tripping islanding differential protection

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