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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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distributed Aerial 3D Object Mapping Reconstruction Using Message Passing Interface 26

Distributed Aerial 3D Object Mapping Reconstruction Using Me...

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26th international Electronics symposium (IES) on Shaping the Future - Society 5.0 and Beyond

作者： Arissabarno, Cahyo Winarno, Idris Sukaridhoto, Sritrusta Politekn Elekt Negeri Surabaya Dept Informat & Comp Engn Surabaya Indonesia

ISBN: (纸本)9798350392005;9798350391992

Aerial reconstruction of 3D object mapping involves utilizing drones or Unmanned Aerial Vehicles (UAVs) equipped with cameras to capture images for high-resolution 3D model reconstruction of various objects and terrains. This technique is applicable across various fields, such as Urban Planning, Vehicle Navigation, Environmental Studies, Civil Engineering, and Cultural Heritage Conservation. Drones capture hundreds of overlapping aerial photos, which are then processed to generate photorealistic 3D representations of topographic surfaces using photogrammetry software. However, the reconstruction process is time-consuming and computationally intensive, especially when processing large datasets. This research addresses these challenges by employing parallel computing using the Message Passing Interface (MPI) to distribute the computational workload across multiple nodes. By utilizing MPI, the computational tasks are efficiently divided among four nodes, significantly reducing processing time and alleviating the burden on individual CPUs. The study demonstrates that parallel computing with MPI reduces the reconstruction time from 74 minutes to 27 minutes, achieving a speedup of 2.74 times. This research demonstrates a novel application of MPI to the domain of aerial 3D reconstruction, achieving a specific and substantial reduction in processing time. Additionally, it provides insights into future enhancements, such as parallel texture reconstruction and automation of translation processes.

关键词： Message Passing Interface parallel computing MPI 3D Reconstruction Aerial 3D Object Mapping

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A hybrid clustering algorithm for high-performance edge computing devices [Short] 21

A hybrid clustering algorithm for high-performance edge comp...

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21st IEEE international symposium on parallel and distributed computing (ISPDC)

作者： Laccetti, Giuliano Lapegna, Marco Romano, Diego Univ Naples Federico II Dept Math & Applicat Naples Italy CNR Inst High Performance Comp & Networking Naples Italy

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

Clustering algorithms are efficient tools for discovering correlations or affinities within large datasets and are the basis of several Artificial Intelligence processes based on data generated by sensor networks. Recently, such algorithms have found an active application area closely correlated to the Edge computing paradigm. The final aim is to transfer intelligence and decision-making ability near the edge of the sensors networks, thus avoiding the stringent requests for low-latency and large-bandwidth networks typical of the Cloud computing model. In such a context, the present work describes a new hybrid version of a clustering algorithm for the NVIDIA Jetson Nano board by integrating two different parallel strategies. The algorithm is later evaluated from the points of view of the performance and energy consumption, comparing it with two high-end GPU-based computing systems. The results confirm the possibility of creating intelligent sensor networks where decisions are taken at the data collection points.

关键词： Edge computing hybrid parallelism clustering algorithms performance vs. energy consumption tradeoff

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Helping Faculty Teach Software Performance Engineering

Helping Faculty Teach Software Performance Engineering

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

作者： Owens, John D. Hoppe, Bruce Univ Calif Davis Dept Elect & Comp Engr Davis CA 95616 USA Connect Associates Arlington MA USA

ISBN: (纸本)9798350364613;9798350364606

Over the academic year 2022-23, we discussed the teaching of software performance engineering with more than a dozen faculty across North America and beyond. Our outreach was centered on research-focused faculty with an existing interest in this course material. These discussions revealed an enthusiasm for making software pertimmance engineering a more prominent part of a curriculum for computer scientists and engineers. Here, we discuss how MIT's longstanding efforts in this area may serve as a launching point for community development of a software performance engineering curriculum, challenges in and solutions for providing the necessary infrastructure to universities, and future directions.

关键词： Performance distributed parallel cluster computing data structures and algorithms software performance engineering

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Self-aware Reliable and Reconfigurable computing Systems - An Overview

Self-aware Reliable and Reconfigurable Computing Systems - A...

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

作者： Goehringer, Diana Podlubne, Ariel Vargas, Fabian Krstic, Milos Tech Univ Dresden Chair Adapt Dynam Syst Dresden Germany IHP Leibniz Inst Innovat Mikroelekt Frankfurt Oder Germany Univ Potsdam Potsdam Germany

ISBN: (纸本)9798350364613;9798350364606

In demanding environments like space, avionics or robotics, computing systems face challenges affecting their lifetime and reliability. Traditional approaches like static hardware redundancy prove ineffective for modern, complex systems. This work presents an overview of self-aware, reliable, and reconfigurable computing systems leveraging Field Programmable Gate Arrays (FPGAs). Despite increasing application demands, FPGA resources remain limited, posing challenges in implementing static redundancy alongside high performance and energy efficiency. Addressing these challenges requires innovative resilience concepts, emphasizing adaptability, reliability, and energy optimization. This paper surveys existing literature and identifies open challenges in achieving resilient computing systems for demanding applications.

关键词： reconfigurable computing reliability

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Towards Faster Fully Homomorphic Encryption Implementation with Integer and Floating-point computing Power of GPUs 37

Towards Faster Fully Homomorphic Encryption Implementation w...

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37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Fan, Guang Zheng, Fangyu Wan, Lipeng Gao, Lili Zhao, Yuan Dong, Jiankuo Song, Yixuan Wang, Yuewu Lin, Jingqiang Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Univ Chinese Acad Sci Sch Cryptog Beijing Peoples R China Chinese Acad Sci Data Assurance & Commun Secur Res Ctr Beijing Peoples R China Nanjing Univ Informat Sci & Technol Dept Comp & Software Nanjing Peoples R China Ant Grp Hangzhou Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Univ Sci & Technol China Sch Cyber Secur Hefei Peoples R China

ISBN: (纸本)9798350337662

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without knowledge of the plaintext message and currently has been the focus of both academia and industry. However, the performance issue hinders its largescale application, highlighting the urgent requirements of highperformance FHE implementations. With noticing the tremendous potential of GPUs in the field of cryptographic acceleration, this paper comprehensively investigates how to convert the available computing resources residing in GPUs into FHE workhorses, and implement a full set of low-level and middle-level FHE primitives based on two arithmetic units (i.e., INT32 and FP64 units) with three types of data precision (i.e., INT32, INT64 and FP64). This paper gives a comprehensive evaluation and comparison based on each roadmap. Our implementations of fundamental functions outperform the implementations on the same platform by 1.7x to 16.7x. Taking CKKS FHE schemes as a case study, our implementation of homomorphic multiplication achieves 3.2x speedup over the state-of-the-art GPU-based implementation, even considering the difference of platforms. The detailed evaluation and comparison of this paper would offer a vital reference for the follow-up work to choose appropriate underlying arithmetic units and important primitive optimizations in GPU-based FHE implementations.

关键词： Floating point computing Power Fully Homomorphic Encryption GPU High Performance Implementation

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GPU-Accelerated Error-Bounded Compression Framework for Quantum Circuit Simulations 37

GPU-Accelerated Error-Bounded Compression Framework for Quan...

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37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Shah, Milan Yu, Xiaodong Di, Sheng Lykov, Danylo Alexeev, Yuri Becchi, Michela Cappello, Franck North Carolina State Univ Raleigh NC 27695 USA Argonne Natl Lab Argonne IL 60439 USA Univ Chicago Chicago IL USA

ISBN: (纸本)9798350337662

Quantum circuit simulations enable researchers to develop quantum algorithms without the need for a physical quantum computer. Quantum computing simulators, however, all suffer from significant memory footprint requirements, which prevents large circuits from being simulated on classical supercomputers. In this paper, we explore different lossy compression strategies to substantially shrink quantum circuit tensors in the QTensor package (a state-of-the-art tensor network quantum circuit simulator) while ensuring the reconstructed data satisfy the user-needed fidelity. Our contribution is fourfold. (1) We propose a series of optimized pre- and post-processing steps to boost the compression ratio of tensors with a very limited performance overhead. (2) We characterize the impact of lossy decompressed data on quantum circuit simulation results, and leverage the analysis to ensure the fidelity of reconstructed data. (3) We propose a configurable compression framework for GPU based on cuSZ and cuSZx, two state-of-the-art GPU-accelerated lossy compressors, to address different use-cases: either prioritizing compression ratios or prioritizing compression speed. (4) We perform a comprehensive evaluation by running 9 state-of-the-art compressors on an NVIDIA A100 GPU based on QTensor-generated tensors of varying sizes. When prioritizing compression ratio, our results show that our strategies can increase the compression ratio nearly 10 times compared to using only cuSZ. When prioritizing throughput, we can perform compression at the comparable speed as cuSZx while achieving 3-4x higher compression ratios. Decompressed tensors can be used in QTensor circuit simulation to yield a final energy result within 1-5% of the true energy value.

关键词： compression quantum computing GPU

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CATC: A task scheduling based on computing-aware and task classification in Pervasive Edge computing 22

CATC: A task scheduling based on computing-aware and task cl...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Yang, JiaHui Zhang, Yang Zhang, ShuKui Zhu, Mingyu Wang, YingYing Soochow University School of Computer Science and Technology Suzhou215006 China Global Institute of Software Technology Suzhou215163 China

ISBN: (纸本)9798331509712

With no infrastructure, the battlefield environment has characteristics such as device heterogeneity, uneven resource distribution, and dynamic node movement. Additionally, the sensing tasks produced by devices are complex and delay-sensitive, making it challenging to complete the tasks within the time limit if a centralized unified scheduling strategy is employed. In order to fully utilize the resources in the battlefield environment, optimize the load situation in the battlefield scenario, and guarantee the stability of task offloading, this paper proposes a scheduling strategy based on computing-aware and task classification (CATC). We first design a distributed computing-aware mechanism to ensure the detection of surrounding resources and load conditions by the generating task devices. Based on this, we classify the battlefield tasks into multi-attribute tasks according to the actual situation, and develop an offloading strategy for different task types, taking into account computation, storage, transmission, and load situation. The simulation results demonstrate that the proposed CATC strategy has a high task completion rate and can satisfy the definite delay requirements of delay-sensitive tasks. © 2024 IEEE.

关键词： Computation offloading

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Remote Execution of OpenCL and SYCL Applications via rOpenCL

Remote Execution of OpenCL and SYCL Applications via rOpenCL

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37th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Alves, Rui Rutin, Jose Inst Politecn Braganca Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Lab Sustentabil & Tecnol Reg Montanha SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9798350311990

With the increasing computational demands of modern applications, heterogeneous systems continue to have an important role in accelerating computationally intensive tasks, a trend confirmed by the most recent HPC architectures. Efficiently exploiting these systems implies the use of specific programming paradigms, such as the classic OpenCL model, or modern single-source alternatives, like SYCL. However, the original execution model of these approaches does not provision for the use of coprocessors other than those directly attached to the host system where the heterogeneous application starts. Over time, several solutions emerged to cope with this limitation, both at the hardware and software level, allowing to exploit remote/distributed co-processors. In this paper, a representative set of seminal OpenCL API Forwarders is revisited and their performance compared with rOpenCL (a recently introduced platform of the same kind), using the classical matrix multiplication case study. In addition, given the importance of SYCL, which has been steadily gaining traction, this paper also exploits the potential of rOpenCL in supporting SYCL applications that use remote accelerators. To that end, another set of benchmarks is used, with both OpenCL and SYCL implementations, allowing not only to gather insight into the performance trade-offs of local versus remote (via rOpenCL) execution, but also about the current performance differential between the two programming models.

关键词： HPC Heterogeneous computing API Forwarders OpenCL SYCL

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APPFIS: An Advanced parallel Programming Framework for Iterative Stencil Based Scientific Applications in HPC Environments 21

APPFIS: An Advanced Parallel Programming Framework for Itera...

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21st IEEE international symposium on parallel and distributed computing (ISPDC)

作者： Sharif, Md Bulbul Ghafoor, Sheikh Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

ISBN: (数字)9781665488020

ISBN: (纸本)9781665488020

Developing performant parallel applications for the distributed environment is challenging and requires expertise in both the HPC system and the application domain. We have developed a C++-based framework called APPFIS that hides the system complexities by providing an easy-to-use interface for developing performance portable structured grid-based stencil applications. APPFIS's user interface is hardware agnostic and provides partitioning, code optimization, and automatic communication for stencil applications in distributed HPC environment. In addition, it offers straightforward APIs for utilizing multiple GPU accelerators, shared memory, and node-level parallelizations with automatic optimization for computation and communication overlapping. We have tested the functionality and performance of APPFIS using several applications on three platforms (Stampede2 at Texas Advanced computing Center, Bridges-2 at Pittsburgh Supercomputing Center, and Summit Supercomputer at Oak Ridge National Laboratory). Experimental results show comparable performance to hand-tuned code with an excellent strong and weak scalability up to 4096 CPUs and 384 GPUs.

关键词： GPU MPI parallel Programming Framework Structured Grid Iterative Stencil Summit Stampede2 Bridges-2

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