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检索条件"任意字段=2nd International Symposium on Parallel and Distributed Processing and Applications"

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A2FL: Autonomous and Adaptive File Layout in HPC through Real-time Access Pattern Analysis 38

A2FL: Autonomous and Adaptive File Layout in HPC through Rea...

引用

international parallel and distributed processing symposium (IPDPS)

作者： Sung, Dong Kyu Son, Yongseok Sim, Alex Wu, Kesheng Byna, Suren Tang, Houjun Eom, Hyeonsang Kim, Changjong Kim, Sunggon Seoul Natl Univ Seoul South Korea Chung Ang Univ Seoul South Korea Lawrence Berkeley Natl Lab Berkeley CA USA Ohio State Univ Columbus OH USA Seoul Natl Univ Sci & Technol Seoul South Korea

ISBN: (纸本)9798350387117;9798350387124

Various scientific applications with different I/O characteristics are executed in HPC systems. However, underlying parallel file systems are unaware of these characteristics of applications, and using a single fixed file layout for all applications can degrade the performance of HPC systems. In this paper, we propose A2FL, an autonomous and adaptive file layout adjustment scheme that optimizes parallel file system configurations by analyzing the access pattern of the applications. The key steps of A2FL are as follows: (1) A2FL initially intercepts the I/O operations of the application, recording their access patterns in real-time. (2) The access patterns are then transformed into a graphical representation used for predicting I/O performance and providing adjustment recommendations. (3) A2FL autonomously adjusts the file layout based on the prediction results, delivering an optimal file layout within the parallel file system. Moreover, we propose A2FL-Compound which analyzes an access pattern by dividing it into smaller components to optimize the file layout in a fine-grained manner. Our evaluations demonstrate that A2FL significantly enhances I/O performance, with improvements of up to 65.9x compared to the default file layout.

关键词： HPC I/O Access Pattern Machine Learning Storage parallel File System

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TFIDF Text Keyword Mining Method Based on Hadoop distributed Platform Under Massive Data 2

TFIDF Text Keyword Mining Method Based on Hadoop Distributed...

引用

2nd IEEE international Conference on Image processing and Computer applications, ICIPCA 2024

作者： Dai, Zheng Zhao, Xinping Cui, Bingtao Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350360240

Keyword extraction is an important part of text mining. Keywords extracted from text can explore the theme of the whole text and be further applied to text recommendation or text search. Under the wave of big data, the traditional text keyword extraction algorithm frequently appears in the low efficiency of the training and testing process on a single machine. Based on this, this paper proposes the TFIDF text classification algorithm based on the distributed platform of Hadoop, and gives the specific process of the algorithm implementation. The parallel TFIDF text classification algorithm considering the position of words in the document is implemented by MapReduce programming model, and compared with the traditional serial algorithm, the experiment is carried out in single machine and cluster mode. Experiments show that the proposed method improves the performance of the algorithm significantly, and can realize efficient information extraction at high speed in the face of massive data. © 2024 IEEE.

关键词： MapReduce

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Proceedings - 2024 IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

Proceedings - 2024 IEEE International Symposium on Parallel ...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

ISBN: (纸本)9798331509712

The proceedings contain 317 papers. The topics discussed include: HSDP: accelerating large-scale model training via efficient sharded data parallelism;multilevel load balancing algorithm for domestic heterogeneous manycore architecture;fast distributed polynomial multiplication algorithm for lattice-based cryptographic decryption in blockchain systems;carbon-aware distributed energy management for data center microgrids based on blockchain;distributed energy management for carbon neutral data centers;MTRP: a high-performance cost-efficient buffering scheme for multi-tenant cloud services;a diffusion-mamba approach with boundary sampling for imbalanced classification;a two-phase encrypted traffic classification scheme in programmable data plane;and a sorted and dynamic graph storage system of the hybrid memory architecture.

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LARA: Latency-Aware Resource Allocator for Stream processing applications 32

LARA: Latency-Aware Resource Allocator for Stream Processing...

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32nd Euromicro international Conference on parallel, distributed and Network-Based processing (PDP)

作者： Benedetti, Priscilla Coviello, Giuseppe Rao, Kunal Chakradhar, Srimat NEC Labs Amer Inc Princeton NJ 95110 USA

ISBN: (纸本)9798350363074;9798350363081

One of the key metrics of interest for stream processing applications is "latency", which indicates the total time it takes for the application to process and generate insights from streaming input data. For mission-critical video analytics applications like surveillance and monitoring, it is of paramount importance to report an incident as soon as it occurs so that necessary actions can be taken right away. Stream processing applications are typically developed as a chain of microservices and are deployed on container orchestration platforms like Kubernetes. Allocation of system resources like "cpu" and "memory" to individual application microservices has direct impact on "latency". Kubernetes does provide ways to allocate these resources e.g. through fixed resource allocation or through vertical pod autoscaler (VPA), however there is no straight-forward way in Kubernetes to prioritize "latency" for an end-toend application pipeline. In this paper, we present LARA, which is specifically designed to improve "latency" of stream processing application pipelines. LARA uses a regression-based technique for resource allocation to individual microservices. We implement four real-world video analytics application pipelines i.e. license plate recognition, face recognition, human attributes detection and pose detection, and show that compared to fixed allocation, LARA is able to reduce latency by up to similar to 2.8X and is consistently better than VPA. While reducing latency, LARA is also able to deliver over 2X throughput compared to fixed allocation and is almost always better than VPA.

关键词： Stream processing microservices latency minimization autoscaling Kubernetes

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Multilevel Load Balancing Algorithm for Domestic Heterogeneous Manycore Architecture 22

Multilevel Load Balancing Algorithm for Domestic Heterogeneo...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Ma, Yi Chen, Xin Guo, Heng Li, Fang Liu, Xin Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9798331509712

Load imbalance often occurs in particle-in-cell simulations on parallel computing, which seriously affects the efficiency of applications. Due to the characteristics of multilevel parallelism and communication asymmetry of compute nodes in domestic heterogeneous manycore architecture, the impact of load imbalance is more prominent. The paper proposes a multilevel load-balancing algorithm for domestic heterogeneous manycore architecture. Inside the supernode, computing tasks are redivided based on manycore acceleration. Between the supernodes, a greedy-based communication mode is designed to minimize communication across supernodes. The experimental results show that the proposed algorithm achieves almost ideal dynamic load balance, and improves the performance of the evaporation module in two-phase flow simulation by 10.9-19.7 times for the 50 million-sized grid. © 2024 IEEE.

关键词： parallel architectures

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parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection applications 38

Parallel Approximations for High-Dimensional Multivariate No...

引用

international parallel and distributed processing symposium (IPDPS)

作者： Zhang, Xiran Abdulah, Sameh Cao, Jian Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal Saudi Arabia King Abdullah Univ Sci & Technol Stat Program Thuwal Saudi Arabia King Abdullah Univ Sci & Technol Extreme Comp Res Ctr Thuwal 23955 Saudi Arabia Univ Houston Dept Math Houston TX USA

ISBN: (纸本)9798350387117;9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n(3)) and space complexity of O(n(2)), mainly due to a Cholesky factorization operation for an n x n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing

关键词： Cholesky factorization Confidence region detection Excursion Set Multivariate normal probability Separation-of-Variables algorithm Tile low-rank

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A Workflow Scheduling Algorithm Based on Linear Evaluation for Dynamic distributed Computing Environments 22

A Workflow Scheduling Algorithm Based on Linear Evaluation f...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Li, Xin Zeng, Zhan Chen, Yuxin Deng, Jinglong Hong, Yue Zheng, Wei Xiamen University Department of Computer Science Xiamen China

ISBN: (纸本)9798331509712

The development of computer architecture towards multi-core processors brings new opportunities and challenges for efficient task scheduling in scientific workflows. This paper investigates the key challenges of scheduling scientific workflows, typically represented as Directed Acyclic Graphs (DAGs), in multi-core and dynamic distributed computing environments. Traditional static and dynamic scheduling algorithms face limitations in handling complex DAG structures and complex computational environments. To address these issues, we propose an improved LinearPB algorithm that simplifies the node priority calculation through linear evaluation, thereby reducing computational overhead and enhancing scheduling performance. Our extensive experiments demonstrate that the LinearPB algorithm significantly reduces execution time, outperforms existing algorithms in most cases. © 2024 IEEE.

关键词： Scheduling algorithms

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Characterizing Power and Performance Interference Scalability in the 28-core ARM ThunderX2 32

Characterizing Power and Performance Interference Scalabilit...

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32nd Euromicro international Conference on parallel, distributed and Network-Based processing (PDP)

作者： Calero, Ibai Petit, Salvador Gomez, Maria E. Sahuquillo, Julio Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9798350363074;9798350363081

Nowadays, energy efficiency is a major concern in any type of processor-based device, ranging from processor servers to supercomputers, including mobile battery-fed devices. In recent years, systems based on the ARM architecture, traditionally better suited for mobile and embedded systems, have increased their market share in segments commonly occupied by x86 processors. This growth is due, to some extent, to the excellent energy efficiency shown by high-performance ARM processors. Designing software and hardware energy-efficient systems requires a sound knowledge of the relationship among three main axes: component activity, power, and inter-application interference at the shared resources. This problem aggravates in many-core processors, which are ubiquitous in high-performance servers. This paper characterizes the aforementioned axes in a 28-core ARM ThunderX2 processor. Experimental results show that the performance of highly-scalable single-threaded applications is sustained regardless of the number of applications and interference introduced at the shared resources at the cost of increasing power. In contrast, low-scalable applications require much less power and experience a huge performance degradation as their number increases.

关键词： ARM ThunderX2 workload characterization system performance power consumption inter-application interference

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QGIP: A Framework Bridging Quantum Grayscale Image processing and applications 22

QGIP: A Framework Bridging Quantum Grayscale Image Processin...

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22nd IEEE international symposium on parallel and distributed processing with applications, ISPA 2024

作者： Che, Xilong Zhang, Jiale Chen, Shuo Peng, Shun Hu, Juncheng Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331509712

Quantum computing offers parallel processing capabilities and resource-saving advantages, particularly useful for managing expansive datasets and complex image processing tasks. Grayscale images, being the simplest single-channel image mode, are frequently employed in artificial intelligence training. Before actual image applications, various image processing operations are typically required. However, the restoration of a grayscale image of dimensions 2n × 2n after a series of linear transformations poses a challenge. Existing methods typically involve finding the inverse of the most recent linear transformation or re-encoding the image followed by repeated operations until the final transformation, resulting in excessive computational overhead and disconnection from subsequent quantum grayscale image applications. To address this issue, we propose a universal quantum linear restoration algorithm for grayscale image, denoted as QLR, which effectively bridges the stages of linear transformation and subsequent image applications. QLR reduces the time complexity from O(2n) to O(n) compared to classical counterpart. Building upon the QLR algorithm, we further propose two quantum resource-optimized compression methods for optional lossless image storage. Combining with other quantum algorithms and techniques, we design a framework (QGIP) aimed at bridging the processes of quantum grayscale image processing and applications. Experiments simulated on the IBM Quantum platform validate the correctness and efficiency of our proposal. © 2024 IEEE.

关键词： Image reconstruction

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Picasso: Memory-Efficient Graph Coloring Using Palettes With applications in Quantum Computing 38

Picasso: Memory-Efficient Graph Coloring Using Palettes With...

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international parallel and distributed processing symposium (IPDPS)

作者： Ferdous, S. M. Neff, Reece Peng, Bo Shuvo, Salman Minutoli, Marco Mukherjee, Sayak Kowalski, Karol Becchi, Michela Halappanavar, Mahantesh Pacif Northwest Natl Lab Richland WA 99354 USA North Carolina State Univ Raleigh NC USA

ISBN: (纸本)9798350387117;9798350387124

A coloring of a graph is an assignment of colors to vertices such that no two neighboring vertices have the same color. The need for memory-efficient coloring algorithms is motivated by their application in computing clique partitions of graphs arising in quantum computations where the objective is to map a large set of Pauli strings into a compact set of unitaries. We present Picasso, a randomized memory-efficient iterative parallel graph coloring algorithm with theoretical sublinear space guarantees under practical assumptions. The parameters of our algorithm provide a trade-off between coloring quality and resource consumption. To assist the user, we also propose a machine learning model to predict the coloring algorithm's parameters considering these trade-offs. We provide a sequential and parallel implementation of the proposed algorithm. We perform an experimental evaluation on a 64-core AMD CPU equipped with 512 GB of memory and an Nvidia A100 GPU with 40GB of memory. For a small dataset where existing coloring algorithms can be executed within the 512 GB memory budget, we show up to 68x memory savings. On massive datasets, we demonstrate that GPU-accelerated Picasso can process inputs with 49.5x more Pauli strings (vertex set in our graph) and 2,478x more edges than state-of-the-art parallel approaches.

关键词： Graph coloring quantum computing memory-efficient algorithms

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