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检索条件"任意字段=IEEE International Symposium on Parallel and Distributed Processing with Applications"

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Shared-Memory parallel Algorithms for Fully Dynamic Maintenance of 2-Connected Components 36

Shared-Memory Parallel Algorithms for Fully Dynamic Maintena...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Haryan, Chirayu Anant Ramakrishna, G. Kothapalli, Kishore Banerjee, Dip Sankar Indian Inst Technol Tirupati Tirupati Andhra Pradesh India Int Inst Informat Technol Hyderabad Hyderabad India Indian Inst Technol Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9781665481069

Finding the biconnected components of a graph has a large number of applications in many other graph problems including planarity testing, computing the centrality metrics, finding the (weighted) vertex cover, coloring, and the like. Recent years saw the design of efficient algorithms for this problem across sequential and parallel computational models. However, current algorithms do not work in the setting where the underlying graph changes over time in a dynamic manner via the insertion or deletion of edges. the insertion or deletion of edges. Dynamic algorithms in the sequential setting that obtain the biconnected components of a graph upon insertion or deletion of a single edge are known from over two decades ago. parallel algorithms for this problem are not heavily studied. In this paper, we design shared-memory parallel algorithms that obtain the biconnected components of a graph subsequent to the insertion or deletion of a batch of edges. Our algorithms hence will be capable of exploiting the parallelism adduced due to a batch of updates. We implement our algorithms on an AMD EPYC 7742 CPU having 128 cores. Our experiments on a collection of 10 realworld graphs from multiple classes indicate that our algorithms outperform parallel state-of-the-art static algorithms.

关键词： Semiconductor device modeling Measurement distributed processing Heuristic algorithms Instruction sets Computational modeling Maintenance engineering

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A Dynamic distributed Scheduler for DNN Inference on the Edge 1

A Dynamic Distributed Scheduler for DNN Inference on the Edg...

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1st international symposium on parallel Computing and distributed Systems, PCDS 2024

作者： Hu, Fei Mishra, Shivakant University of Colorado Department of Computer Science BoulderCO United States

ISBN: (纸本)9798350349658

Edge computing plays a pivotal role in IoT applications that require rapid and secure data processing. How-ever, these applications are typically resource-demanding, and the resources available at the edge are often significantly less than those available in cloud environments. Thus, optimally utilizing these constrained resources is key to fulfilling various application requirements such as low latency, privacy, and cost-effectiveness. Given the dynamic and hybrid characteristics of IoT environments, static scheduling approaches frequently fail to adequately address these complex and varying demands. This paper presents the design, implementation, and evaluation of a dynamic distributed scheduler tailored for DNN inferences on edge computing platforms, based on sophisticated sub-model profiling techniques. This scheduler's core feature is its ability to continuously monitor the state of the IoT infrastructure, dynam-ically adapting the distribution of computing tasks according to real-time environmental and network conditions. Diverging from previous research that typically segments DNN models into two parts, this study proposes a more granular approach that splits models into multiple segments, thereby maximizing the utilization of diverse edge computing resources. The implementation of this scheduler, including integration with a GPU, demonstrates its effectiveness across various DNN models, highlighting its practical utility in leveraging edge computing capabilities. © 2024 ieee.

关键词： Cloud platforms

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Highly Scalable Large-Scale Asynchronous Graph processing using Actors 23

Highly Scalable Large-Scale Asynchronous Graph Processing us...

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23rd ieee/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Elmougy, Youssef Hayashi, Akihiro Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350302080

With the accelerating growth of Big Data, real-world graph processing applications now need to tackle graphs with billions of vertices and trillions of edges, thereby increasing the demand for effective solutions to application scalability. Unfortunately, current approaches to implementing these applications on modern HPC systems exhibit poor scale-out performance with increasing numbers of nodes. The scalability challenges for these applications are driven by large data sizes, synchronization overheads, and fine-grained communications with irregular data accesses and poor locality. This paper presents the scalability of a novel Actor-based programming system, which provides a lightweight runtime that supports fine-grained asynchronous execution and automatic message aggregation atop a Partitioned Global Address Space (PGAS) communication layer. Evaluations of the Jaccard Index and PageRank applications on the NERSC Perlmutter system demonstrate nearly perfect scaling up to 1, 000 nodes and 64K cores (one-third of the targeted 3000-nodes for Perlmutter). In addition, our Actor-based implementations of Jaccard Index and PageRank executed with parallel efficiencies of 85.7% and 63.4% for the largest run of 64K cores. This performance represents a 29.6x speedup relative to UPC and OpenSHMEM versions of PageRank.

关键词： parallel Graph processing Actors Selectors distributed Systems Shared Memory PGAS OpenSHMEM

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Accelerating CKKS Homomorphic Encryption with Data Compression on GPUs 67

Accelerating CKKS Homomorphic Encryption with Data Compressi...

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67th ieee international Midwest symposium on Circuits and Systems (MWSCAS)

作者： Phan, Quoc Bao Nguyen, Linh Nguyen, Tuy Tan No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA

ISBN: (纸本)9798350387186;9798350387179

Homomorphic encryption (HE) algorithms, particularly the Cheon-Kim-Kim-Song (CKKS) scheme, offer significant potential for secure computation on encrypted data, making them valuable for privacy-preserving machine learning. However, high latency in large integer operations in the CKKS algorithm hinders the processing of large datasets and complex computations. This paper proposes a novel strategy that combines lossless data compression techniques with the parallel processing power of graphics processing units to address these challenges. Our approach demonstrably reduces data size by 90% and achieves significant speedups of up to 100 times compared to conventional approaches. This method ensures data confidentiality while mitigating performance bottlenecks in CKKS-based computations, paving the way for more efficient and scalable HE applications.

关键词： Homomorphic encryption graphics processing units data compression CKKS privacy-preserving

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Scalable Node Embedding Algorithms using distributed Sparse Matrix Operations

Scalable Node Embedding Algorithms using Distributed Sparse ...

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

作者： Ranawaka, Isuru Azad, Ariful Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9798350364613;9798350364606

We introduce a distributed memory parallel algorithm for force-directed node embedding that places vertices of a graph into a low-dimensional vector space based on the interplay of attraction among neighboring vertices and repulsion among distant vertices. We develop our algorithms using two sparse matrix operations, SDDMM and SpMM. We propose a configurable pull -push -based communication strategy that optimizes memory usage and data transfers based on computing resources and asynchronous MPI communication to overlap communication and computation. Our algorithm scales up to 256 nodes on distributed supercomputers by surpassing the performance of state-of-the-art algorithms

关键词： Embedding SDDMM Sparse Matrix Computation SpMM

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Generalized Flow-Graph Programming Using Template Task-Graphs: Initial Implementation and Assessment 36

Generalized Flow-Graph Programming Using Template Task-Graph...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Schuchart, Joseph Nookala, Poornima Javanmard, Mohammad Mahdi Herault, Thomas Valeev, Edward F. Bosilca, George Harrison, Robert J. Univ Tennessee Innovat Comp Lab Knoxville TN 37996 USA SUNY Stony Brook Inst Adv Computat Sci Stony Brook NY 11794 USA Meta Platforms Inc New York NY USA Virginia Polytech Inst & State Univ Dept Chem Blacksburg VA 24061 USA

ISBN: (纸本)9781665481069

We present and evaluate TTG, a novel programming model and its C++ implementation that by marrying the ideas of control and data flowgraph programming supports compact specification and efficient distributed execution of dynamic and irregular applications. Programming interfaces that support taskbased execution often only support shared memory parallel environments;a few support distributed memory environments, either by discovering the entire DAG of tasks on all processes, or by introducing explicit communications. The first approach limits scalability, while the second increases the complexity of programming. We demonstrate how TTG can address these issues without sacrificing scalability or programmability by providing higher-level abstractions than conventionally provided by taskcentric programming systems, without impeding the ability of these runtimes to manage task creation and execution as well as data and resource management efficiently. TTG supports distributed memory execution over 2 different task runtimes, PaRSEC and MADNESS. Performance of four paradigmatic applications (in graph analytics, dense and block-sparse linear algebra, and numerical integrodifferential calculus) with various degrees of irregularity implemented in TTG is illustrated on large distributed-memory platforms and compared to the state-of-theart implementations.

关键词： Hybrid programming techniques in applications

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Essentials of parallel Graph Analytics 36

Essentials of Parallel Graph Analytics

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Osama, Muhammad Porumbescu, Serban D. Owens, John D. Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781665497473

We identify the graph data structure, frontiers, operators, an iterative loop structure, and convergence conditions as essential components of graph analytics systems based on the native-graph approach. Using these essential components, we propose an abstraction that captures all the significant programming models within graph analytics, such as bulksynchronous, asynchronous, shared-memory, message-passing, and push vs. pull traversals. Finally, we demonstrate the power of our abstraction with an elegant modern C++ implementation of single-source shortest path and its required components.

关键词： parallel graph analytics graph traversal algorithms

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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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Mnemonic: A parallel Subgraph Matching System for Streaming Graphs 36

Mnemonic: A Parallel Subgraph Matching System for Streaming ...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Bhattarai, Bibek Huang, Howie George Washington Univ Washington DC 20052 USA

ISBN: (纸本)9781665481069

Finding patterns in large highly connected datasets is critical for value discovery in business development and scientific research. This work focuses on the problem of subgraph matching on streaming graphs, which provides utility in a myriad of real-world applications ranging from social network analysis to cybersecurity. Each application poses a different set of control parameters, including the restrictions for a match, type of data stream, and search granularity. The problem-driven design of existing subgraph matching systems makes them challenging to apply for different problem domains. This paper presents Mnemonic, a programmable system that provides a high-level API and democratizes the development of a wide variety of subgraph matching solutions. Importantly, Mnemonic also delivers key data management capabilities and optimizations to support real-time processing on long-running, high-velocity multi-relational graph streams. The experiments demonstrate the versatility of Mnemonic, as it outperforms several state-of-the-art systems by up to two orders of magnitude.

关键词： subgraph graph pattern matching isomorphism streaming

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(POSTER) Visual Transformer Models on UAV-Captured Images for Person Detection 20

(POSTER) Visual Transformer Models on UAV-Captured Images fo...

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20th Annual international Conference on distributed Computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Zahia, Ionel-Gabriel Popescu, Dan Ichim, Loretta Natl Univ Sci & Technol POLITEHN Bucharest Fac Automat Control & Comp Sci Bucharest Romania

ISBN: (纸本)9798350369458;9798350369441

One of the most important applications of UAVs is person detection for security or rescue tasks. The goal of the proposed paper is to develop, experiment, and compare the performance of two new neural networks based on the transformer architecture, Detection Transformer and Vision Transformer. Two datasets were used, an own one for testing and COCO for learning. The results are promising to take into account the difficulties of person detection at a distance.

关键词： unmanned aerial vehicle person detection image processing neural networks performance metrics

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