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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...

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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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Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline parallelism 23

Redundancy-Free High-Performance Dynamic GNN Training with H...

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32nd international symposium on High-Performance parallel and distributed Computing (HPDC) part of the ACM Federated Computing Research Conference (FCRC)

作者： Xia, Yaqi Zhang, Zheng Wang, Hulin Yang, Donglin Zhou, Xiaobo Cheng, Dazhao Wuhan Univ Sch Comp Sci Wuhan Peoples R China Nvidia Corp Santa Clara CA USA Univ Macau IOTSC Macau Peoples R China

ISBN: (纸本)9798400701559

Temporal Graph Neural Networks (TGNNs) extend the success of Graph Neural Networks to dynamic graphs. distributed TGNN training requires efficiently tackling temporal dependency, which often leads to excessive cross-device communication that generates significant redundant data. However, existing systems are unable to remove the redundancy in data reuse and transfer, and suffer from severe communication overhead in a distributed setting. This paper presents Sven, an algorithm and system co-designed TGNN training library for the end-to-end performance optimization on multi-node multi-GPU systems. Exploiting dependency patterns of TGNN models and characteristics of dynamic graph datasets, we design redundancy-free data organization and load-balancing partitioning strategies that mitigate the redundant data communication and evenly partition dynamic graphs at the vertex level. Furthermore, we develop a hierarchical pipeline mechanism integrating data prefetching, micro-batch pipelining, and asynchronous pipelining to mitigate the communication overhead. As the first scaling study on the memory-based TGNNs training, experiments conducted on an HPC cluster of 64 GPUs show that Sven can achieve up to 1.7x-3.3x speedup over the state-of-art approaches and a factor of up to 5.26x communication efficiency improvement.

关键词： distributed training dynamic GNN pipeline parallelism

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The Internet of Trust: Securing and Transparent Supply Chains with Blockchain and IoT 18

The Internet of Trust: Securing and Transparent Supply Chain...

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18th IEEE international Conference on Advanced Networks and Telecommunications systems, ANTS 2024

作者： Singal, Gaurav Kushwaha, Riti Sharma, Ashish Tewari, Tulika Netaji Subhash University of Technology SMIEEE Computer Science and Engineering Delhi India Bennett University School of Computer Science and Technology Greater Noida India Manipal University Computer Science and Engineering Jaipur India Netaji Subhash University of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798350391725

In an era where digital commerce continues to burgeon, the conventional supply chain confronts challenges of inefficiency, fraud, and a dearth of transparency. Blockchain, renowned for its decentralized and immutable ledger, emerges as a compelling solution to transform the e-commerce supply chain landscape. In conventional supply chain practices marked by centralized databases and manual record-keeping, inefficiencies and a dearth of transparency are widespread. Challenges stemming from cumbersome paperwork, delayed information flow, and vulnerability to errors hindered effective coordination, traceability, and overall operational efficiency. Blockchain and IoT can elevate e-commerce supply chains by ensuring transparent, secure, and automated data-sharing, fostering efficiency and trust throughout the entire process. We have presented an Ethereum-based blockchain and Internet of Things e-commerce platform in this paper. We describe in detail how to put in place a system that stores a product's journey's details on an immutable distributed ledger. This allows for secure tracing of a product's origin. The prototype of the system is deployed and tested on Hardhat testing framework. Traditional databases and cloud storages have been replaced by blockchain and IPFS. IPFS is used as the decentralized and distributed storage layer, facilitating the secure and efficient storage of credentials. Furthermore, the results of our experiments and security and scalability analyses demonstrate the viability of our suggested model. © 2024 IEEE.

关键词： distributed ledger

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Efficient distributed Secure Memory with Migratable Merkle Tree 29

Efficient Distributed Secure Memory with Migratable Merkle T...

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29th IEEE international symposium on High-Performance Computer Architecture (HPCA)

作者： Feng, Erhu Du, Dong Xia, Yubin Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9781665476522

Hardware-assisted enclaves with memory encryption have been widely adopted in the prevailing architectures, e.g., Intel SGX/TDX, AMD SEV, ARM CCA, etc. However, existing enclave designs fall short in supporting efficient cooperation among cross-node enclaves (i.e., multi-machines) because the range of hardware memory protection is within a single node. A naive approach is to leverage cryptography at the application level and transfer data between nodes through secure channels (e.g., SSL). However, it incurs orders of magnitude costs due to expensive encryption/decryption, especially for distributed applications with large data transfer, e.g., MapReduce and graph computing. A secure and efficient mechanism of distributed secure memory is necessary but still missing. This paper proposes Migratable Merkle Tree (MMT), a design enabling efficient distributed secure memory to support distributed confidential computing. MMT sets up an integrity forest for distributed memory on multiple nodes. It allows an enclave to securely delegate an MMT closure, which contains both data and metadata of a subtree, to a remote enclave. By reusing the memory encryption mechanisms of existing enclaves, our design achieves secure data transfer without software re-encryption. We have implemented a prototype of MMT and a trusted firmware for management, and further applied MMT to real-world distributed applications. The evaluation results show that compared with existing systems using the AES-NI instruction, MMT can achieve up to 13x speedup on data transferring, and gain 12%similar to 58% improvement on the end-to-end performance of MapReduce and PageRank.

关键词： MapReduce

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A Locality-aware Cooperative distributed Memory Caching for parallel Data Analytic Applications 36

A Locality-aware Cooperative Distributed Memory Caching for ...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Hung, Chia-Ting Chou, Jerry Chen, Ming-Hung Chung, I-Hsin Natl Tsing Hua Univ Comp Sci Dept Hsinchu Taiwan IBM TJ Watson Res Ctr New York NY USA

ISBN: (纸本)9781665497473

Memory caching has long been used to fill up the performance gap between processor and disk for reducing the data access time of data-intensive computations. Previous studies on caching mostly focus on optimizing the hit rate of a single machine. But in this paper, we argue that the caching decision of a distributed memory system should be performed in a cooperative manner for the parallel data analytic applications, which are commonly used by emerging technologies, such as Big Data and AI (Artificial Intelligence), to perform data mining and sophisticated analytics on larger data volume in a shorter time. A parallel data analytic job consists of multiple parallel tasks. Hence, the completion time of a job is bounded by its slowest task, meaning that the job cannot benefit from caching until all inputs of its tasks are cached. To address the problem, we proposed a cooperative caching design that periodically rearranges the cache placement among nodes according to the data access pattern while taking the task dependency and network locality into account. Our approach is evaluated by a trace-driven simulator using both synthetic workload and real-world traces. The results show that we can reduce the average completion times up to 33% compared to a non-collaborative caching polices and 25% compared to other start-of-the-art collaborative caching policies.

关键词： parallel Data Processing Caching Algorithm Performance distributed systems

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Compiler -Driven SWAR parallelism for High -Performance Bitboard Algorithms

Compiler -Driven SWAR Parallelism for High -Performance Bitb...

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

作者： Fey, Florian Gorlatch, Sergei Univ Munster Munster Germany

ISBN: (纸本)9798350364613;9798350364606

parallel high-performance computing relies on cache -efficient, branch-free algorithms that are often expressed as imperative computations over multi-dimensional arrays. Numerous problem domains, spanning from image processing to graph analytics, and from state space exploration in combinatorial optimization to computer Chess, require carefully crafted algorithms that capitalize on patterns inherent in the underlying problem structure. A renowned technique, SIMD-Within-A-Register (SWAR), harnesses integer arithmetic to attain significant hardware parallelism. However, this approach typically demands labor-intensive efforts from domain experts with specialized knowledge of the underlying hardware architecture. We therefore present a compiler-driven approach that automates the transformation of conventional array -based C-code into highly tuned integer arithmetic, exploiting SWAR parallelism without the requirement of tedious manual optimization efforts. Our approach achieves substantial performance improvements, exhibiting an average speedup of 30x compared to conventional array-based implementations.

关键词： High-Performance Computing Cache-Efficient Algorithms parallel Computing SWAR SIMD-parallelism Compiler-Driven Optimization

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Moving from Composable to Programmable 36

Moving from Composable to Programmable

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Chen, Zhongyi Renambot, Luc Long, Lance Brown, Maxine Johnson, Andrew E. Univ Illinois Elect Visualizat Lab Comp Sci Chicago IL 60607 USA

ISBN: (纸本)9781665497473

In today's Big Data era, data scientists require modern workflows to quickly analyze large-scale datasets using complex codes to maintain the rate of scientific progress. These scientists often rely on available campus resources or off-the-shelf computational systems for their applications. Unified infrastructure or over-provisioned servers can quickly become bottlenecks for specific tasks, wasting time and resources. Composable infrastructure helps solve these problems by providing users with new ways to increase resource utilization. Composable infrastructure disaggregates a computer's components - CPU, GPU (accelerators), storage and networking - into fluid pools of resources, but typically relies upon infrastructure engineers to architect individual machines. Infrastructure is either managed with specialized command-line utilities, user interfaces or specification files. These management models are cumbersome and difficult to incorporate into data-science workflows. We developed a high-level software API, Composastructure, which, when integrated into modern workflows, can be used by infrastructure engineers as well as data scientists to reorganize composable resources on demand. Composastructure enables infrastructures to be programmable, secure, persistent and reproducible. Our API composes machines, frees resources, supports multi-rack operations, and includes a Python module for Jupyter Notebooks.

关键词： distributed systems testbed implementation and deployment composable infrastructure deep learning visualization infrastructure as code

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parallel Maximum Cardinality Matching for General Graphs on GPUs

Parallel Maximum Cardinality Matching for General Graphs on ...

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

作者： Schwing, Gregory Grosu, Daniel Schwiebert, Loren Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9798350364613;9798350364606

The matching problem formulated as Maximum Cardinality Matching in General Graphs (MCMGG) finds the largest matching on graphs without restrictions. The Micah-Vazirani algorithm has the best asymptotic complexity for solving MCMGG when the graphs are sparse. parallelizing matching in general graphs on the GPU is difficult for multiple reasons. First, the augmenting path procedure is highly recursive, and NVID1A GPUs use registers to store kernel arguments, which eventually spill into cached device memory, with a performance penalty. Second, extracting parallelism from the matching process requires partitioning the graph to avoid any overlapping augmenting paths. We propose an implementation of the Micali-Vazirani algorithm which identifies bridge edges using threadparallel breadth-first search, followed by block-parallel path augmentation and blossom contraction. Augmenting path and Union find methods were implemented as stack-based iterative methods, with a stack allocated in shared memory. Our experimentation shows that compared to the serial implementation, our approach results in up to 15-fold speed-up for very sparse regular graphs, up to 5 -fold slowdown for denser regular graphs, and finally a 50-fold slowdown for power-law distributed Kronecker graphs. This implementation has been open-sourced for further research on developing combinatorial graph algorithms on GPUs.

关键词： matching general graphs Micali-Vazirani algorithm CUDA GPU

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RLRP: High-Efficient Data Placement with Reinforcement Learning for Modern distributed Storage systems 36

RLRP: High-Efficient Data Placement with Reinforcement Learn...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Lu, Kai Zhao, Nannan Wan, Jiguang Fei, Changhong Zhao, Wei Deng, Tongliang Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China SenseTime Res Shenzhen Peoples R China

ISBN: (纸本)9781665481069

Modern distributed storage systems with massive data and storage nodes pose higher requirements to the data placement strategy. Furthermore, with emerged new storage devices, heterogeneous storage architecture has become increasingly common and popular. However, traditional strategies expose great limitations in the face of these requirements, especially do not well consider distinct characteristics of heterogeneous storage nodes yet, which will lead to suboptimal performance. In this paper, we present and evaluate the RLRP, a deep reinforcement learning (RL) based replica placement strategy. RLRP constructs placement and migration agents through the Deep-Q-Network (DQN) model to achieve fair distribution and adaptive data migration. Besides, RLRP provides optimal performance for heterogeneous environment by an attentional Long Short-term Memory (LSTM) model. Finally, RLRP adopts Stagewise Training and Model fine-tuning to accelerate the training of RL models with large-scale state and action space. RLRP is implemented on Park and the evaluation results indicate RLRP is a highly efficient data placement strategy for modern distributed storage systems. RLRP can reduce read latency by 10%similar to 50% in heterogeneous environment compared with existing strategies. In addition, RLRP is used in the real-world system Ceph, which improves the read performance of Ceph by 30%similar to 40%.

关键词： distributed Storage Data Placement Reinforcement Learning Hashing Heterogeneous Environment

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Research on Optimization of Communication Technology Based on Deep Learning 3

Research on Optimization of Communication Technology Based o...

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3rd international Conference on Electronics and Information Technology, EIT 2024

作者： Zhang, Zhaoxiang China Mobile Group Device Co. Ltd BeiJing100053 China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369151

distributed deep learning systems commonly use synchronous data parallelism to train models. However, communication overhead can be costly in distributed environments with limited communication bandwidth. To reduce communication overhead, an intuitive approach is to increase the communication interval. However, increasing the communication interval often affects the convergence speed of the model, requiring more epochs to train the model to the target accuracy, thereby reducing the statistical efficiency of the training algorithm. To address these issues in distributed synchronous data parallel training algorithms and communication interval selection methods, this paper proposes a training algorithm that combines skip communication strategies and correction techniques to ensure low communication overhead and high statistical efficiency. The training algorithm maintains a small batch size through local updates in each training process and employs correction techniques to reduce the divergence among individual local models, thereby ensuring high statistical efficiency. At the same time, the training algorithm utilizes a skip communication strategy to update the global model, meaning that the global model is not updated with every iteration, but rather updated once every few iterations. This reduces the frequency of communication, ensuring low communication overhead. Experimental results show that this training algorithm reduces total training time by 88.9%, 74.9%, 34.1%, and 82.4% compared to SSGD, SkipSSGD, LocalSGD, and SMA, respectively. © 2024 IEEE.

关键词： Training Deep learning distributed databases Bandwidth parallel processing Data models Communications technology Information technology Optimization Convergence

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