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Benchmarking Modern databases for Storing and Profiling Very Large Scale HPC Communication Data 1

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15th BenchCouncil international symposium on Benchmarking, Measuring and Optimizing (Bench)

作者： Kousha, Pouya Zhou, Qinghua Subramoni, Hari Panda, Dhableswar K. Ohio State Univ Columbus OH 43210 USA

ISBN: (数字)9789819703166

ISBN: (纸本)9789819703159;9789819703166

Capturing cross-stack profiling of communication on HPC systems at fine granularity is critical for gaining insights into the detailed performance trade-offs and interplay among various components of HPC ecosystem. To enable this, one needs to be able to collect, store, and retrieve system-wide data at high fidelity. As modern HPC systems expand, ensuring high-fidelity, real-time communication profiling becomes more challenging, especially with the growing number of users employing profiling tools to monitor their workloads. We take on this challenge in this paper and identify the key metrics of performance that makes a database amenable to these needs. We then design benchmarks to measure and understand the performance of multiple, popular, open-source databases. Through rigorous experimental analysis, we demonstrate the performance and scalability trends of the selected databases to perform different types of fundamental storage and retrieval operations under various conditions. Through this work, we are able to achieve sub-second complex data querying serving up to 64 users and demonstrate a "9x" improvement in insertion latency through parallel data insertion, achieving a latency of 55 ms and 50% less disk space for inserting 200,000 rows of profiling data collected from a potential system that is "4x" the size of the state-of-the-art 19th-ranked Frontera supercomputing system at TACC with 8,368 nodes.

关键词： HPC Storage Database Profiling Communication

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A NUMA-aware Graph Database for Hybrid Memory System 21

A NUMA-aware Graph Database for Hybrid Memory System

引用

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： Tan, Shaoheng Liu, Chang Li, Dingding Tang, Yong Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

Compared to traditional relational databases (RDBMS), specialized graph databases (GDBs) can efficiently store and process graph data in both time and space. Hence, domains like social networks often use GDBs for data management, such as representing friendships. Optane DC Persistent Memory Module (DCPMM) is a novel memory device combining byte-addressability, non-volatility, high density and superior performance. Intuitively, applying GDB on DCPMM with the Non-Uniform Memory Access (NUMA) architecture may improve I/O performance, yet the real performance may be unstable due to stacked hardware characteristics. Specifically, DCPMM exhibits serious NUMA effects and unexpected read/write asymmetry compared to DRAM. To tackle these issues, we propose a NUMA-aware graph database called NAPGDB. NAPGDB employs delegated thread pools to improve the latency of accessing remote nodes. Additionally, it categorizes read/write requests into short-running and long-running tasks, with the former being served synchronously instead of using delegated threads to improve CPU utilization. We implement NAPGDB and the experimental results show that NAPGDB can improve the remote throughput by 1.3x-1.7x compared with the multithreaded Redis with RedisGraph. © 2023 IEEE.

关键词： Graph databases

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Optimizing Optical Character Recognition Accuracy in Livestock Weighting systems Through distributed Intelligent Computer Vision 1

Optimizing Optical Character Recognition Accuracy in Livesto...

引用

1st IEEE international symposium on Consumer Technology, ISCT 2024

作者： Wafa, M. Rosyad Ahsanul Toh-Arlim, Muhaimin Sukaridhoto, Sritrusta Budiarti, Rizqi Putri Nourma Prayudi, Agus Politeknik Elektronika Negeri Surabaya Dept. of Multimedia Creative Technology Surabaya Indonesia Universitas Nahdlatul Ulama Surabaya Faculty of Economics Business and Digital Technology Surabaya Indonesia

ISBN: (纸本)9798350365191

This research develops and optimizes an optical character recognition (OCR) system for goat weighing scales using intelligent computer vision and distributed computing to enhance accuracy and efficiency in real-world operational conditions. Images of the scales are captured and processed to remove noise, then recognized using deep learning algorithms. The implementation of distributed computing frameworks like Apache Hadoop and TensorFlow enables efficient parallel processing. Results show a 20% increase in OCR accuracy and a 30% improvement in processing efficiency compared to conventional systems. This integration provides a scalable and reliable solution for applications in the livestock industry, with potential adaptation for various operational conditions and other livestock types. © 2024 IEEE.

关键词： Scales (weighing instruments)

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Shared-Memory parallel Algorithms for Community Detection in Dynamic Graphs

Shared-Memory Parallel Algorithms for Community Detection in...

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

作者： Sahu, Subhajit Kothapalli, Kishore Banerjee, Dip Sankar Int Inst Informat Technol Hyderabad Hyderabad 500032 India Indian Inst Technol Jodhpur Surpura Bypass Rd Karwar 342030 Rajasthan India

ISBN: (纸本)9798350364613;9798350364606

Community detection is the problem of identifying natural divisions in networks. A relevant challenge in this problem is to find communities on rapidly evolving graphs. In this paper, we present our parallel Dynamic Frontier (DF) approach. Given a batch update of edge deletions or insertions, this approach incrementally identifies an approximate set of affected vertices in the graph with minimal overhead. We apply this approach to both Louvain, a high quality, and Label Propagation Algorithm (LPA), a fast static community detection algorithm. Our approach achieves a mean speedup of 7.3x and 6.7x, when applied to Louvain and LPA respectively, compared to our parallel and optimized implementation of Delta-screening, a recently proposed state-of-the-art approach. Finally, we show how to combine Louvain and LPA with the DF approach to arrive at a hybrid algorithm. This algorithm produces high-quality communities while providing a speedup of 2.0 x on top of DF-based Louvain.

关键词： Population dynamics

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ADSwitch: Adaptive CPU Scheduling for distributed systems 21

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

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作者： Zhong, Yidong Ao, Yang Li, Dingding Li, Jianguo Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. Therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. The experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness. © 2023 IEEE.

关键词： Recurrent neural networks

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Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs 36

Optimizing Huffman Decoding for Error-Bounded Lossy Compress...

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

作者： Rivera, Cody Di, Sheng Tian, Jiannan Yu, Xiaodong Tao, Dingwen Cappello, Franck Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA

ISBN: (纸本)9781665481069

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and cuSZ is a version of SZ designed to take advantage of the GPU's power. At present, cuSZ's compression performance has been optimized significantly while its decompression still suffers considerably lower performance because of its sophisticated loss-less compression step-a customized Huffman decoding. In this work, we aim to significantly improve the Huffman decoding performance for cuSZ, thus improving the overall decompression performance in turn. To this end, we first investigate two state-of-the-art GPU Huffman decoders in depth. Then, we propose a deep architectural optimization for both algorithms. Specifically, we take full advantage of CUDA GPU architectures by using shared memory on decoding/writing phases, online tuning the amount of shared memory to use, improving memory access patterns, and reducing warp divergence. Finally, we evaluate our optimized decoders on an Nvidia V100 GPU using eight representative scientific datasets. Our new decoding solution obtains an average speedup of 3.64x over cuSZ's Huffman decoder and improves its overall decompression performance by 2.43x on average.

关键词： distributed processing Analytical models Memory management Graphics processing units distributed databases Throughput Data models

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Performance comparison of distributed in memory databases in dynamic fog environments 20

Performance comparison of distributed in memory databases in...

引用

20th IEEE Int symposium on parallel and distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Vlahavas, George Michaiiidou, Anna-Valentini Toliopoulos, Theodoros Psotniadis, Vasileios G. Gounaris, Anastasios Vakali, Attiena Aristotle Univ Thessaloniki Sch informat Thessaloniki Greece

ISBN: (纸本)9781665464970

Fog computing is an extension of cloud computing to overcome limitations of the latter. It brings data processing and storage closer to edge devices, while offering higher processing power and storage space compared to the edge. In edge or fog environments, data is often ephemeral, so storing data in memory provides considerable performance benefits. Edge or fog nodes may also fail or disconnect, so distributed database systems are used in order to provide durability and availability. Database performance is an important factor when designing a fog platform. We assess the performance of two distributed, in memory databases, Apache Ignite and Redis, measured as throughput, with respect to different parameters: number of nodes, number of replicas, persistency, network bandwidth, network latency and number of operations. In all cases, Redis outperforms Apache Ignite, however, Apache Ignite scales very well when the number of operations is increased.

关键词： Fog environments benchmark distributed in memory databases Apache Ignite Redis

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Smooth Switching Control Strategy of parallel-type Power Conversion System Off-grid 14

Smooth Switching Control Strategy of Parallel-type Power Con...

引用

14th IEEE international symposium on Power Electronics for distributed Generation systems, PEDG 2023

作者： He, Hao Xu, Jingyuan Qing, Hongyang Zhang, Chunjiang Yanshan University College of Electrical Engineering Qinhuangdao City China State Grid Jibei Electric Power Co. Ltd Changli County Power Supply Branch Qinhuangdao City China

ISBN: (纸本)9798350328233

The power conversion system (PCS) based on droop control is voltage control in parallel. The island and grid-connected operation of the PCS are controlled by turning off and on the grid-side static transfer switch (STS). When the grid fails suddenly, the STS breaking command is issued. The PCS runs in island through the breaking process, but a longer time voltage shock, power and frequency oscillation will occur after breaking process is over. In order to solve above problems and achieve smooth transition of parallel PCS from grid-connected to island mode, a new type feedback link of voltage and current controller is designed in this paper. It combines the differential and integral controllers with droop controller to achieve smooth switching from grid-connected to island mode. In addition, the stability of the improved system is analyzed through root locus. The simulation results verify that the proposed control strategy can achieve a smooth transition from grid-connection to island mode. © 2023 IEEE.

关键词： Root loci

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Modeling Memory Contention between Communications and Computations in distributed HPC systems 36

Modeling Memory Contention between Communications and Comput...

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

作者： Denis, Alexandre Jeannot, Emmanuel Swartvagher, Philippe Inria Bordeaux Sud Ouest Bordeaux France

ISBN: (纸本)9781665497473

To amortize the cost of MPI communications, distributed parallel HPC applications can overlap network communications with computations in the hope that it improves global application performance. When using this technique, both computations and communications are running at the same time. But computation usually also performs some data movements. Since data for computations and for communications use the same memory system, memory contention may occur when computations are memory-bound and large messages are transmitted through the network at the same time. In this paper we propose a model to predict memory band-width for computations and for communications when they are executed side by side, according to data locality and taking contention into account. Elaboration of the model allowed to better understand locations of bottleneck in the memory system and what are the strategies of the memory system in case of contention. The model was evaluated on many platforms with different characteristics, and showed a prediction error in average lower than 4 %.

关键词： HPC MPI Memory Contention NUMA Band-width Predictive Models Multicore Processing

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parallel quantum circuit extraction from MBQC-patterns

Parallel quantum circuit extraction from MBQC-patterns

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

作者： Quanz, Marcel Staudacher, Korbinian Fuerlinger, Karl Ludwig Maximilians Univ Munchen Munich Germany Ludwig Maximilians Univ Munchen MNM Team Munich Germany

ISBN: (纸本)9798350364613;9798350364606

In the model of measurement-based quantum computing (MBQC), computations are performed via sequential measurements on a highly entangled graph state. MBQC is a natural model for photonic quantum computing and has been shown to be useful for tasks like optimization and verification of general quantum computations. Therefore, it is often necessary to translate between MBQC and the predominantly used quantum circuit model in a fast and reliable way. While there are algorithms with linear complexity to extract quantum circuits from measurement patterns using additional ancilla qubits, efficient ancilla-free extraction has shown to be more costly. We develop strategies to parallelize an existing extraction algorithm based on ZX-calculus by exploiting the graph structure of measurement patterns and evaluate the performance on patterns obtained from a benchmark set of quantum circuits. Our results suggest that possible parallelization speedups are closely related to the graph structure of a pattern.

关键词： MBQC ZX-calculus gflow

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