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Hierarchical Communication Optimization for distributed DNN Training 31

Hierarchical Communication Optimization for Distributed DNN ...

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31st IEEE international conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Verma, Om Shivam Malakar, Preeti Indian Institute of Technology Kanpur India

ISBN: (纸本)9798331509118

Motivation: Large scale deep neural network models use a lot of training data, evident from the large datasets curated for their training, such as ImageNet. Thus it is necessary to use high performance computing (HPC) systems for training of these large-scale models. Various techniques have been developed for parallelizing model training - data-parallelism, model-parallelism, and the more recent FSDP (Fully Sharded Data parallel). © 2024 IEEE

关键词： Neural network models

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Communication Interchange For Artificial Intelligence systems

Communication Interchange For Artificial Intelligence System...

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international conference on Computing, Networking and Communications (ICNC)

作者： Voicu, Razvan Cristian Pande, Aarush Kaunteya Tanveer, Muhammad Hassan Chang, Yusun Kennesaw State Univ Dept Robot & Mechatron Engn Atlanta GA 30339 USA

ISBN: (纸本)9798350371000;9798350370997

The rise and proliferation of Artificial Intelligence (AI) technologies are bringing transformative changes to various sectors, signaling a new era of innovation in fields as diverse as medicine, manufacturing, and even day-to-day social interactions. Notable advancements are not just confined to textual understanding, as seen in models like GPT, but also extend to visual cognition through image recognition and more. Beyond surface interactions and predictions, AI finds profound applications in life-saving domains such as medical diagnostics and becomes an integral part of daily life through chatbot-based customer interactions. However, as the horizon of AI expands, a crucial yet often overlooked aspect emerges- the underlying mission-critical infrastructure required to support and deploy these models effectively. The intricacies of efficient communication systems, foundational for real-time AI model operations, take center stage in ensuring the seamless functioning of AI-driven applications. This paper explores the quintessential changes needed in communication paradigms to keep pace with the evolving AI landscape. Specifically, we highlight the pivotal role of multipath communication in enhancing the responsiveness and efficiency of AI applications [1]. As a case in point, we investigate its impact on mission-critical operations in robotics. Through experimentation and analysis, the results elucidate the substantial benefits of this approach, revealing a significant improvement in delay metrics. This work underscores the imperative of aligning communication systems with the ever-growing demands of AI, ensuring that infrastructural capabilities do not lag in the race for innovation.

关键词： Artificial Intelligence (AI) Programmable Networking parallel Communication Centralized - distributed systems Real-time Control Multipath Communication AI Infrastructure

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O(N) distributed direct factorization of structured dense matrices using runtime systems 23

O(N) distributed direct factorization of structured dense ma...

引用

52nd international conference on parallel Processing (ICPP)

作者： Deshmukh, Sameer Ma, Qinxiang Yokota, Rio Bosilca, George Tokyo Inst Technol Sch Comp Tokyo Japan Tokyo Inst Technol Global Sci Informat & Comp Ctr Tokyo Japan Univ Tennessee Innovat Comp Lab Knoxville TN USA

ISBN: (纸本)9798400708435

Structured dense matrices result from boundary integral problems in electrostatics and geostatistics, and also Schur complements in sparse preconditioners such as multi-frontal methods. Exploiting the structure of such matrices can reduce the time for dense direct factorization from O(N-3) to O(N). The Hierarchically Semi-Separable (HSS) matrix is one such low rank matrix format that can be factorized using a Cholesky-like algorithm called ULV factorization. The HSS-ULV algorithm is highly parallel because it removes the dependency on trailing sub-matrices at each HSS level. However, a key merge step that links two successive HSS levels remains a challenge for efficient parallelization. In this paper, we use an asynchronous runtime system PaRSEC with the HSS-ULV algorithm. We compare our work with STRUMPACK and LORAPO, both state-of-the-art implementations of dense direct low rank factorization, and achieve up to 2x better factorization time for matrices arising from a diverse set of applications on up to 128 nodes of Fugaku for similar or better accuracy for all the problems that we survey.

关键词： low rank approximation runtime systems HSS matrix distributed

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ABi-BFS: A High-performance parallel Breadth-First Search on Shared-memory systems 29

ABi-BFS: A High-performance Parallel Breadth-First Search on...

引用

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

作者： Shi, Jifan Xu, Yun University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350330717

Breadth-first search (BFS) is a cornerstone in graph traversal, widely employed in areas such as social network analysis, routing algorithms, and biological network exploration. As the size of these graphs increases, the performance of BFS becomes significant. There are two key factors that influence BFS performance: synchronization overhead and memory access efficiency. To address them, we propose the ABi-BFS, an asynchronous and bidirectional optimized breadth-first search algorithm. ABi-BFS novelly designs an asynchronous mode for node-expanding and task-assignment processes to decrease the synchronization overhead and introduces an optimized strategy to select the frontier nodes in the bottom-up step of bidirectional search to lower the number of node accesses. Our experiment demonstrates that ABi-BFS outperforms other implementations of state-of-the-art PBFS algorithms on various graphs and achieves the highest scalability among them on shared-memory systems. © 2023 IEEE.

关键词： Breadth-First Search Graph Processing parallel Algorithm

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19th international conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2018

19th International Conference on Parallel and Distributed Co...

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19th international conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2018

ISBN: (纸本)9789811359064

The proceedings contain 49 papers. The special focus in this conference is on parallel and distributed Computing, Applications and Technologies. The topics include: A real-time routing protocol in wireless sensor-actuator network;privacy preserving classification based on perturbation for network traffic;fault diagnosis of a wireless sensor network using a hybrid method;an optimization theory of home occupants’ access data for determining smart grid service;automatic classification of transformed protocols using deep learning;covert timing channel design for uniprocessor real-time systems;parallelization of the DIANA algorithm in openMP;Flash animation watermarking algorithm based on SWF tag attributes;efficient scheduling strategy for data collection in delay-tolerant wireless sensor networks with a mobile sink;analysis of massive e-learning processes: An approach based on big association rules mining;SGNet: Design of optimized DCNN for real-time face detection;A study on L1 data cache bypassing methods for high-performance GPUs;Memory contention aware power management for high performance GPUs;Dynamic selective warp scheduling for GPUs using L1 data Cache locality information;an efficient model and algorithm for privacy-preserving trajectory data publishing;what makes charitable crowdfunding projects successful: A research based on data mining and social capital theory;A SwarmESB based architecture for an european healthcare insurance system in compliance with GDPR;a study on deriving and simulating pre-risk on complex gas facilities for preventing accidents;body gesture modeling for psychology analysis in job interview based on deep spatio-temporal approach;green vs revenue: Data center profit maximization under green degree constraints;evaluation for two bloom filters’ configuration.

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Scalable Big Spatial Data Processing with SQL Query Compilation and distributed Morsel-driven parallelism

Scalable Big Spatial Data Processing with SQL Query Compilat...

引用

2024 IEEE international conference on Big Data, BigData 2024

作者： Sahni, Rahul Zhang, Xiaozheng Chatterjee, Sudip Ray, Suprio University of New Brunswick Fredericton Canada

ISBN: (纸本)9798350362480

The rapid rise in spatial data volumes from diverse sources necessitate efficient spatial data processing capability. Although most relational databases support spatial extensions of SQL query features, they offer limited scalability. Traditional relational database query processing follows a pull-based (or tuple-at-a-time) model of query processing. This is not efficient for processing large volumes of data. A number of specialized spatial data processing systems were developed that extend cluster computing frameworks, such as Spark and Hadoop. However, these systems are characterized by limited or no support for spatial SQL query execution. The few systems that support SQL querying, suffer from the overheads of the pull-based *** present a compilation-based distributed SQL query processing system. It follows a data-centric query compilation approach that takes a SQL query and generates distributed C++ (UPC++) based physical query plans. The generated code is compiled and executed on a distributed in-memory high performance framework based on the Partitioned Global Address Space (PGAS) paradigm. We also introduce morsel-driven parallelism for scalable spatial query execution in a distributed runtime. We conduct experimental evaluation of our system with two real-world datasets on a number of spatial query workloads. Experimental results demonstrate that our system performs significantly better than a leading spatial big data system Apache Sedona and distributed parallel relational database Citus. © 2024 IEEE.

关键词： Structured Query Language

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Multi-Criteria Mesh Partitioning for an Explicit Temporal Adaptive, Tasl-Distribnted Finite-volume Solver

Multi-Criteria Mesh Partitioning for an Explicit Temporal Ad...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Lasserre, Alice Couteyen-Carpaye, Jean-Marie Guermouche, Abdou Namyst, Raymond Inria Le Chesnay Rocquencourt France Airbus CRT Toulouse France Univ Bordeaux Bordeaux France

ISBN: (纸本)9798350364613;9798350364606

The aerospace industry is one of the largest users of numerical simulation, which is an essential tool in the field of aerodynamic engineering, where many fluid dynamics simulations are involved. In order to obtain the most accurate solutions, some of these simulations use unstructured finite volume solvers that cope with irregular meshes by using explicit time-adaptive integration methods. Modern parallel implementations of these solvers rely on task-based runtime systems to perform fine-grained load balancing and to avoid unnecessary synchronizations. Although such implementations greatly improve performance compared to a classical fork-join MPI+OpenMP variants, it remains a challenge to keep all cores busy throughout the simulation loop. In this article, we first investigate the origins of this lack of parallelism. We emphasize that the irregular structure of the task graph plays a major role in the inefficiency of the computation distribution. Our main contribution is to improve the shape of the task graph by using a new mesh partitioning strategy. The originality of our approach is to take the temporal level of mesh cells into account during the mesh partitioning phase. We evaluate our approach by integrating our solution in an ArianeGroup production code used by Airbus. We show that our partitioning method leads to a more balanced task graph. The resulting task scheduling is up to two times faster for meshes ranging from 200,000 to 12,000,000 components.

关键词： scheduling DAG task-based applications heterogeneous systems

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HPC-in-Containers: A Containerized parallel Environment for parallel Programming Learning Using Docker

HPC-in-Containers: A Containerized Parallel Environment for ...

引用

2024 international conference on Information Technology systems and Innovation, ICITSI 2024

作者： Arisal, Andria Suhartanto, Heru National Research and Innovation Agency Research Center for Data and Information Sciences Bandung Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798331511470

This paper presents HPC-in-Containers, a novel containerized parallel computing environment using Docker. It is designed to facilitate learning parallel programming concepts, where users do not have to deploy a multicomputer infrastructure. Instead, they can use their desktop and laptop to easily have a multicomputer environment by utilizing containerization on the multi-core CPU. The environment offers a comprehensive platform for exploring various parallel programming paradigms, including distributed memory parallelism, shared memory parallelism, and hybrid approaches. We include source code samples for each paradigm to demonstrate its capabilities: parallel multicomputer implementations using MPI, parallel thread implementations for single computers using OpenMP, and hybrid parallelization techniques combining both MPI and OpenMP. Furthermore, to enhance user interaction and ease of use, the Docker image incorporates a Jupyter Notebook interface with a C interpreter, allowing users to write, execute, and visualize parallel code through a web browser. This feature makes HPC-in-Containers particularly suitable for educational settings, enabling students to experiment with parallel programming concepts in a controlled, reproducible environment. Our work contributes to the field of parallel computing education by providing a flexible, scalable, and easily deployable platform that bridges the gap between theoretical concepts and practical implementation. It offers a valuable resource for educators, students, and researchers seeking to explore and master the intricacies of parallel programming in a containerized setting. © 2024 IEEE.

关键词： Students

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Performance Evaluation of distributed k-WTA on Dynamic Undirected Connected Graphs and Its Application to Task Allocation 29

Performance Evaluation of Distributed k-WTA on Dynamic Undir...

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

作者： Liu, Kexin Zhang, Yinyan Jinan University College of Cyber Security Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9798350330717

The k-winners-take-all (k-WTA) network is a model based on competition. The extant literature on k-WTA models only deals with static undirected connected graphs. In the actual application scenario, the static undirected connection graph cannot adapt to the complex and changing communication conditions. Thus, in this paper we find a distributed k-WTA network that can be applied to dynamic undirected connected graphs. Our experimental simulation results demonstrate that the network efficiently identify the top k largest inputs from n inputs in dynamic undirected connected graphs. Furthermore, we apply this network to a distributed multi-robot target tracking task assignment scenario. Our proposed algorithm shows promising results in this application, indicating its potential in real-world scenarios. © 2023 IEEE.

关键词： Dynamic undirected connected graphs k-winners-take-all Tasks allocation

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distributed Key Derivation for Multi-Party Management of Blockchain Digital Assets 29

Distributed Key Derivation for Multi-Party Management of Blo...

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

作者： Zhong, Lin Wang, Yujue Ding, Yong Du, Jun He, Kevin Zhang, Andrew New Huo Digital Limited Hong Kong Hong Kong Hangzhou Innovation Institute of Beihang University Hangzhou China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security Guilin China HKCT Institute for Higher Education Institute of Cyberspace Technology Hong Kong Hong Kong

ISBN: (纸本)9798350330717

The current Single-User Key Derivation (SKD) caters to individual management of blockchain's tree-structured assets but falls short for threshold signatures aimed at multi-party control of blockchain assets. We introduce a distributed Key Derivation (DKD) for collaborative management of these assets. Our DKD aligns with SKD and the prevalent Decentralized Key Generation (DKG) in threshold signatures, employing the GG18 DKG [1] protocol to safely refresh child keys without affecting the parent key or other nodes. It maintains the hierarchical structure while preventing privilege escalation attacks. Experimental tests show that our DKD's key derivation time is about 240 μs, minor compared to GG18's DKG time. © 2023 IEEE.

关键词： Blockchain Digital assets distributed key derivation Threshold signature

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