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15th international Green and Sustainable computing conference

作者： Tang, Chengyu Baskiyar, Sanjeev Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA

ISBN: (纸本)9798331507879;9798331507862

Traffic congestion is a pervasive problem causing severe environmental and economic issues. In recent years, traffic signal control using reinforcement learning (RL) has come a long way. Most existing studies focus on using distributed agents with data exchange among neighbors, which, however, increases network complexity and usage and still suffers from the lack of broader coordination. Meanwhile, the attention mechanism has achieved tremendous success, and advances in vehicle-to-infrastructure (V2I) communications have enabled real-time collections of granular data. However, integrating these technologies into traffic signal control remains under-explored. Therefore, we present GreenLight, a forward-thinking and eco-friendly traffic signal control framework that can be applied to V2I-equipped fog computing environments. For a large urban area, traffic signals are divided into clusters, each coordinated by a fog node with an RL agent. Intra-cluster indexed self-attention is applied to extract context-aware features that the fog-residing RL agent utilizes to determine the proper signal control command. Results of simulation experiments using both synthetic and real-world scenarios show that the presented framework yields lower waiting time, emissions, and fuel consumption compared to baseline methods, indicating its potential for next-generation transportation systems.

关键词： Traffic Signal Control Reinforcement Learning Self-Attention Emissions Fuel Consumption Fog computing V2I

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Proof-of-Work-Based Consensus in Expected-Constant Time 43rd

Proof-of-Work-Based Consensus in Expected-Constant Time

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43rd Annual international conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT)

作者： Garay, Juan Kiayias, Aggelos Shen, Yu Texas A&M Univ College Stn TX USA Univ Edinburgh Edinburgh Midlothian Scotland IOG Edinburgh Midlothian Scotland

ISBN: (纸本)9783031587337;9783031587344

In the traditional consensus problem (aka Byzantine agreement), parties are required to agree on a common value despite the malicious behavior of some of them, subject to the condition that if all the honest parties start the execution with the same value, then that should be the outcome. This problem has been extensively studied by both the distributed computing and cryptographic protocols communities. With the advent of blockchains, whose main application-a distributed ledger-essentially requires that miners agree on their views, new techniques have been proposed to solve the problem, and in particular in so-called "permissionless" environments, where parties are not authenticated or have access to point-to-point channels and, further, may come and go as they please. So far, the fastest way to achieve consensus in the proof-of-work (PoW)-based setting of Bitcoin, takes O(polylog.) number of rounds, where. is the security parameter. We present the first protocol in this setting that requires expected-constant number of rounds. Furthermore, we show how to apply securely sequential composition in order to yield a fast distributed ledger protocol that settles all transactions in expected-constant time. Our result is based on a novel instantiation of " m-for-1 PoWs" on parallel chains that facilitates our basic building block, Chain-King Consensus. The techniques we use, via parallel chains, to port classical protocol design elements (such as Phase-King Consensus, super-phase sequential composition and others) into the permissionless setting may be of independent interest.

关键词： distributed ledger

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Hierarchical and distributed optimization of distribution network considering spatial and temporal distribution of electric vehicle charging load

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ENERGY REPORTS 2023年 9卷 308-322页

作者： Liu, Shu Fang, Chen Jia, Shicheng Xiang, Yueping Yang, Jianwei State Grid Shanghai Municipal Elect Power Co Shanghai 200122 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

With the increasing penetration of electric vehicles (EV) and distributed generations (DG) in distribution networks, operation and control of distribution networks (DN) is faced with many new challenges. Considering the remarkable characteristics of distribution network layered by voltage level and the spatial and temporal distribution of EV charging load, a hierarchical and distributed optimization method for DN is proposed. Firstly, the spatial and temporal distribution prediction model of EV charging load is established, which is composed of three parts: the resident travel probability model, the vehicle mobility model and traffic network model. Secondly, these three parts runs jointly to simulate the charging demand generated by EV, and then the charging load is connected to the charging station in the low-voltage distribution station area. Thirdly, considering the operation characteristics of the new energy units, energy storage system (ESS), DG and EV charging station, a dynamic economic dispatching model is established. Fourthly, according to the hierarchical operation characteristics of the medium- and low-voltage distribution network (MLV), a distributed optimization algorithm of DN is proposed to realize the hierarchical and decoupled calculation, which transforms the traditional centralized serial computing mode into distributed parallel computing mode, and improves the computing efficiency of the system. At last, the effectiveness and superiority of the proposed strategy are verified by the simulation test. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Spatial and temporal distribution distributed optimization Medium and low voltage distribution network Economic dispatching of distribution network ADMM

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Communication-Computation Overlapping for parallel Multigrid Methods

Communication-Computation Overlapping for Parallel Multigrid...

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

作者： Nakajima, Kengo Univ Tokyo Informat Technol Ctr RIKEN R CCS Tokyo Japan

ISBN: (纸本)9798350364613;9798350364606

Preconditioned iterative methods based on the Krylov subspace technique are widely employed in various scientific and technical computing. When utilizing large-scale parallel computing systems, the communication overhead tends to increase with the growth in the number of nodes, making its reduction a crucial challenge. In parallel finite element methods (FEM) and finite volume methods (FVM), halo communication and computation overlapping (CC-Overlapping) are commonly employed, often in conjunction with the dynamic loop scheduling feature of OpenMP. This approach has been primarily applied to sparse matrix-vector products (SpMV) and explicit solvers. Previous studies by the author have proposed reordering techniques for applying CC-Overlapping to processes involving global data dependencies, such as the Conjugate Gradient method preconditioned by Incomplete Cholesky Factorization (ICCG). Successful implementations on massively parallel supercomputers demonstrated high parallel performance, but the application of CC-Overlapping was limited to SpMV. In the present work, the author proposes a method to apply CC-Overlapping to the forward and backward substitutions of the IC(0) smoother of the parallel Conjugate Gradient method preconditioned by Multigrid (MGCG). Using up to 4,096 nodes on Wisteria/EMEC-01 (Odyssey) with A64FX, performance improvement of approximately 40+% was achieved compared to the original implementation, while improvement was 20+% on 1,024 nodes of Oakbridge-CX system with Intel Xeon CPU's.

关键词： parallel computing Iterative Solvers Multigrid Manycore Communication-Computation Overlapping

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27th international European conference on parallel and distributed computing, Euro-Par 2021

27th International European Conference on Parallel and Distr...

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27th international European conference on parallel and distributed computing, Euro-Par 2021

ISBN: (纸本)9783030856649

The proceedings contain 38 papers. The special focus in this conference is on parallel and distributed computing. The topics include: An MPI-based Algorithm for Mapping Complex Networks onto Hierarchical Architectures;pipelined Model parallelism: Complexity Results and Memory Considerations;efficient and Systematic Partitioning of Large and Deep Neural Networks for parallelization;A GPU Architecture Aware Fine-Grain Pruning Technique for Deep Neural Networks;Towards Flexible and Compiler-Friendly Layer Fusion for CNNs on Multicore CPUs;smart distributed DataSets for Stream Processing;colony: parallel Functions as a Service on the Cloud-Edge Continuum;horizontal Scaling in Cloud Using Contextual Bandits;geo-distribute Cloud Applications at the Edge;Automatic Low-Overhead Load-Imbalance Detection in MPI Applications;A Fault Tolerant and Deadline Constrained Sequence Alignment Application on Cloud-Based Spot GPU Instances;sustaining Performance While Reducing Energy Consumption: A Control Theory Approach;algorithm Design for Tensor Units;a Scalable Approximation Algorithm for Weighted Longest Common Subsequence;TSLQueue: An Efficient Lock-Free Design for Priority Queues;G-Morph: Induced Subgraph Isomorphism Search of Labeled Graphs on a GPU;accelerating Graph Applications Using Phased Transactional Memory;Efficient GPU Computation Using Task Graph parallelism;towards High Performance Resilience Using Performance Portable Abstractions;Enhancing Load-Balancing of MPI Applications with Workshare;trace-Based Workload Generation and Execution;particle-In-Cell Simulation Using Asynchronous Tasking;Exploiting Co-execution with OneAPI: Heterogeneity from a Modern Perspective;designing a 3D parallel Memory-Aware Lattice Boltzmann Algorithm on Manycore Systems;Fault-Tolerant LU Factorization Is Low Cost;Mixed Precision Incomplete and Factorized Sparse Approximate Inverse Preconditioning on GPUs;GPU-Accelerated Mahalanobis-Average Hierarchical Clustering Analysis.

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Assessing the Linguistic Design Quality of APIs of distributed Systems and Microservices 34

Assessing the Linguistic Design Quality of APIs of Distribut...

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34th Annual international conference on Collaborative advances in Software and computing, CASCON 2024

作者： Dey, Krishno Cao, Hung Palma, Francis University of New Brunswick SE+AI Research Lab Faculty of Computer Science NB Canada University of New Brunswick Analytics Everywhere Lab Faculty of Computer Science NB Canada

ISBN: (纸本)9798331504830

APIs or Application Programming Interfaces help distributed systems and microservices to expose their functionalities and serve as a means for communication among them. In contrast to a poorly designed API, a well-designed API is easy for users to understand and use. Thus, APIs with high-quality design are essential both for API providers and client developers. This paper aims to assess the linguistic design quality of APIs in distributed systems and microservices by automatically detecting good and poor design practices, commonly known as patterns and antipatterns, respectively. We rely on syntactic and semantic analyses for automatic assessment of the design quality of APIs using detection heuristics. Syntactic analysis involves analyzing the structure and syntax of the APIs, while semantic analysis involves analyzing API documentation, descriptions, and parameters. We achieved an overall accuracy of more than 93% in detecting patterns and antipatterns. Our detection results also suggest that antipatterns are prevalent in the APIs of distributed systems and microservices. Our findings will assist API developers in identifying poor design practices and improving the design quality of their APIs. © 2024 IEEE.

关键词： APIs distributed Systems GraphQL Linguistic Design Quality Microservices NLP REST Semantic Analysis

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Intelligent Test Paper Generation Algorithm Integrating Normal Distribution and parallel Genetic Algorithm 5

Intelligent Test Paper Generation Algorithm Integrating Norm...

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5th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Liu, Chuangye Huang, Guimin School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350368284

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms. © 2024 IEEE.

关键词： Consensus algorithm

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Verified High Performance computing: The SyDPaCC Approach 16th

Verified High Performance Computing: The SyDPaCC Approach

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16th international conference on Verification and Evaluation of Computer and Communication Systems (VECoS)

作者： Loulergue, Frederic Ed-Dbali, Ali Univ Orleans INSA CVL LIFO EA 4022 Orleans France

ISBN: (纸本)9783031497360;9783031497377

The SYDPACC framework for the COQ proof assistant is based on a transformational approach to develop verified efficient scalable parallel functional programs from specifications. These specifications are written as inefficient (potentially with a high computational complexity) sequential programs. We obtain efficient parallel programs implemented using algorithmic skeletons that are higher-order functions implemented in parallel on distributed data structures. The output programs are constructed step-by-step by applying transformation theorems. Leveraging COQ type classes, the application of transformation theorems is partly automated. The current version of the framework is presented and exemplified on the development of a parallel program for the maximum segment sum problem. This program is experimented on a parallel machine.

关键词： program transformation scalable parallel computing functional programming interactive theorem proving

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Flover: A Temporal Fusion Framework for Efficient Autoregressive Model parallel Inference 30

Flover: A Temporal Fusion Framework for Efficient Autoregres...

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30th international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Yao, Jinghan Alnaasan, Nawras Chen, Tian Shafi, Aamir Subramoni, Hari Panda, Dhabaleswar K. (DK) Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9798350383225

Autoregressive models, despite their commendable performance in a myriad of generative tasks, face challenges stemming from their inherently sequential structure. Inference on these models, by design, harnesses a temporal dependency, where the current token's probability distribution is conditioned on preceding tokens. This inherent characteristic severely impedes computational efficiency during inference as a typical inference request can require more than thousands of tokens, where generating each token requires a load of entire model weights, making the inference more memory-bound. The large overhead becomes profound in real deployment where requests arrive randomly, necessitating various generation lengths. Existing solutions, such as dynamic batching and concurrent instances, introduce significant response delays and bandwidth contention, falling short of achieving optimal latency and throughput. To address these shortcomings, we propose Flover - a temporal fusion framework for efficiently inferring multiple requests in parallel. We deconstruct the general generation pipeline into pre-processing and token generation, and equip the framework with a dedicated work scheduler for fusing the generation process temporally across all requests. By orchestrating the token-level parallelism, Flover exhibits optimal hardware efficiency and significantly spares the system resources. By further employing a fast buffer reordering algorithm that allows memory eviction of finished tasks, it brings over 11x inference speedup on GPT and 16x on LLAMA compared to the cutting-edge solutions provided by NVIDIA FasterTransformer. Crucially, by leveraging the advanced tensor parallel technique, Flover proves efficacious across diverse computational landscapes, from single-GPU setups to distributed scenarios, thereby offering robust performance optimization that adapts to variable use cases.

关键词： Autoregressive model Inference frameworks parallel Pipelining distributed inference

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parallel Implementation of SHA256 on Multizone Heterogeneous Systems 21

Parallel Implementation of SHA256 on Multizone Heterogeneous...

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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： Luo, Yongtao Liu, Jie Xiao, Tiaojie Gong, Chunye National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Program for Frontier Equipment Changsha China National Supercomputer Center in Tianjin Tianjin China

ISBN: (纸本)9798350329223

SHA-256 plays an important role in widely used applications, such as data security, data integrity, digital signatures, and cryptocurrencies. However, most of the current optimized implementations of SHA-256 are based on CPUs or dedicated hardware, such as ASICs and FPGAs. Consequently, there is a need to explore whether new heterogeneous parallel framework can improve the computational performance of the hash function. To address this issue, we conducted a study on the MT-3000 platform, which is a special architecture processor for the next-generation exascale prototype supercomputer. We proposed MT-SHA256, a heterogeneous multistage parallel implementation for hashing multiple messages on the MT-3000. Combining the architectural features of this processor, we developed an effective solution that significantly improved the computational performance of SHA-256. As a result, MT-SHA256 achieved a maximum throughput of 1045.68 MB/s on a single acceleration core of MT-3000. This is 9.84x higher than the C code implementation on one CPU core of MT-3000. We also performed a scalability test and found that MT-SHA256 achieved a throughput of 98.04 GB/s on a computing node, and extended to 512 nodes (2048 acceleration clusters) on this system with good scalability. © 2023 IEEE.

关键词： Hash functions

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