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About 10,000 papers for “parallel performance analysis”

Graph-Centric Performance Analysis for Large-Scale Parallel Applications

Yuyang Jin, Haojie Wang, Runxin Zhong et al. · IEEE Transactions on Parallel and Distributed Systems · 2024

Performance analysis is essential for understanding the performance behaviors of parallel programs and detecting performance bottlenecks. Whereas, complex interconnections across several types of performance bugs, as well as inter-process communications and data dependence, make efficient performance analysis even more difficult. Despite the fact that many performance tools have been developed, accurately identifying underlying performance bottlenecks for such complex scenarios requires specific in-depth analysis. Significant human efforts and analysis knowledge are often required to implement each specific analytic task. To alleviate the complexity of developing specific performance analytic tasks, we present a programmable performance analysis tool, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PerFlow</small> . In <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PerFlow</small> , a step-by-step performance analysis process is represented as an Analysis Flow Diagram, which is constructed with several performance analysis sub-tasks, namely passes, that can be defined by developers or provided by <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PerFlow</small> 's built-in analysis pass library. Furthermore, we define a Performance Abstraction Graph to describe the performance behavior of a parallel program, where the edges indicate the interactions between parallel units, therefore the analytic sub-tasks are converted to graph analysis tasks. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PerFlow</small> provides plentiful Python APIs for developing analytic tasks. Several case studies of real-world applications with up to 700 K lines of code are used to demonstrate the effectiveness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PerFlow</small> . The results indicate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PerFlow</small> makes it much easier to implement specific performance analytic tasks, and these tasks are performed automatically and efficiently to detect underlying performance bottlenecks.

Cited by 2

Performance of Parallel Conjugate Gradient Solvers in Meshfree Analysis

Youngjoon Kim, Colby C. Swan, Associate Professor · 2008

ABSTRACT: Meshfree analysis methods, on a per degree of freedom basis, are typically more computationally expensive and yet more accurate than finite element methods. For very large models, whether meshfree or finite element, the memory and computational effort associated with direct equation solvers makes them prohibitively expensive. In this work, the performance of different linear equation solvers with meshfree analysis methods is explored. In particular, parallel conjugate gradient solvers with both Jacobi diagonal preconditioning and incomplete Cholesky factorization preconditioning are tested on a number of different meshfree analysis applications and compared against the performance of a fast direct sparse equation solver. It is found in these exploratory computations that as the support size of meshfree shape functions increases, the condition number of the associated stiffness matrices increases, and the relative efficiency of iterative solvers suffers somewhat. Nevertheless, for normalized support sizes between one and two, the performance of both conjugate gradient solvers compares very 5 favorably with that of the sparse direct solver for intermediate ( N ≈ O( 10) ) and large problems.

Cited by 0

Performance Analysis of Parallel Applications under Message Passing Environments

Marian Bubak, Wlodzimierz Funika, Jacek Moscinski · 2007

We present a view on building a performance analysis tools for parallel applications running on networked workstations under message passing environments (PVM and MPI). Instrumenting a parallel program&amp;apos;s components is carried out with a graphical tool. An execution trace is processed using a special trace handling language. A series of metrics and visualizations help infer about application behavior. We show the results of applying the environment facilities to performance analysis of a lattice gas simulation parallel program.

Cited by 0

Performance Analysis Support for Object Oriented Parallel Scientific Applications

Jeffrey Nesheiwat, Boleslaw K. Szymanski · 1998

this report, involves instrumenting an executing application. This is the most time consuming of the analysis techniques, but since measurements are taken on the target system it is clearly the most accurate[14]. After a brief overview of the basic structure of analysis tools, we present a high level description of the scalable instrumentation and program database technique for collecting, storing, and filtering performance data. We validate this new approach by showing preliminary results using two finite element codes, one sequential and one parallel application. We conclude with a discussion of how this approach can be extended to include parallel object oriented applications. 1.1 Motivation of Research

Cited by 0

Analysis on FPGA Designs of Parallel High Performance Multipliers 

Retheesh.D Saveetha Engineering College · International Journal of communication and computer Technologies · 2019

For many applications from the areas of cryptography and coding, finite field multiplication is the most resource and time consuming operation. In this paper, optimized high performance parallel GF(2233) multipliers for an FPGA realization were designed and the time and area complexities were analyzed. One of the multipliers uses a new hybrid structure to implement the Karatsuba algorithm. For increasing performance, we make excessive use of pipelining and efficient control techniques and use a modern state-of-the-art FPGA technology. As a result we have, to our knowledge, the first hardware realization of sub quadratic arithmetic and currently the fastest and most efficient implementation of 233 bit finite field multipliers

Cited by 1

Execution-driven performance analysis for distributed and parallel systems

Thomas Fahringer, Bernhard Scholz, Xian‐He Sun · 2000

Article Free Access Share on Execution-driven performance analysis for distributed and parallel systems Authors: Thomas Fahringer Institute for Software Science, University of Vienna, Liechtensteinstrasse 22, A-1090, Vienna, Austria Institute for Software Science, University of Vienna, Liechtensteinstrasse 22, A-1090, Vienna, AustriaView Profile , Bernhard Scholz Institute of Computer Languages, Vienna University of Technology, Argentinierstr. 8/4, A-1040, Vienna, Austria Institute of Computer Languages, Vienna University of Technology, Argentinierstr. 8/4, A-1040, Vienna, AustriaView Profile , Xian-He Sun Department of Computer Science, Illinois Institute of Technology, Chicago, IL Department of Computer Science, Illinois Institute of Technology, Chicago, ILView Profile Authors Info & Claims WOSP '00: Proceedings of the 2nd international workshop on Software and performanceSeptember 2000 Pages 204–215https://doi.org/10.1145/350391.350433Published:01 September 2000Publication History 4citation249DownloadsMetricsTotal Citations4Total Downloads249Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Cited by 7

Analysis of High-Performance Parallel Computing using TOPSIS Method

Computer Science Engineering and Technology · 2024

High-performance parallel computing involves the simultaneous execution of multiple tasks or processes, orchestrated to achieve improved computational speed and efficiency. This approach leverages the power of parallelism, exploiting both multi-core CPUs and GPUs, distributed computing clusters, and specialized hardware accelerators. The fundamental idea is to divide a task into smaller sub-tasks that can be executed concurrently, thereby reducing processing time and enhancing overall performance. High-performance parallel computing is a transformative approach that enables us to tackle computationally intensive tasks efficiently. This abstract highlight its significance in contemporary computing and sets the stage for further exploration of the intricacies and innovations within this dynamic field. Researchers and practitioners continue to push the boundaries of what is achievable, making high-performance parallel computing a cornerstone of modern computational science and technology. High-performance parallel computing research is of paramount significance due to its transformative impact across diverse fields. It empowers scientists to tackle complex problems that were once computationally intractable, unlocking new frontiers in scientific discovery. It drives innovation in engineering and design, optimizing product development and manufacturing processes across industries. In healthcare, it accelerates genomics research and drug discovery, offering hope for improved medical treatments. Financial institutions rely on it for data analysis and risk assessment, shaping the global economy. Weather forecasting and environmental modelling are enhanced, aiding disaster preparedness and conservation efforts. In the digital age, parallel computing underpins artificial intelligence, enabling advancements in natural language processing and machine learning. Furthermore, it has vital applications in national security, space exploration, and materials science. In essence, high-performance parallel computing research serves as the backbone of technological progress, fostering innovation, efficiency, and problem-solving across a wide spectrum of disciplines, ultimately shaping the future of our world. TOPSIS, this method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. From the result Scatter- free imaging is got the first rank and object-scatter imaging is having the lowest rank.

Cited by 3Open access

Performance analysis for parallel programs from multicore to petascale

Tallent, Nathan Russell · Rice Digital Scholarship Archive (Rice University) · 2011

Cutting-edge science and engineering applications require petascale computing. Petascale computing platforms are characterized by both extreme parallelism (systems of hundreds of thousands to millions of cores) and hybrid parallelism (nodes with multicore chips). Consequently, to effectively use petascale resources, applications must exploit concurrency at both the node and system level --- a difficult problem. The challenge of developing scalable petascale applications is only partially aided by existing languages and compilers. As a result, manual performance tuning is often necessary to identify and resolve poor parallel and serial efficiency.\nOur thesis is that it is possible to achieve unique, accurate, and actionable insight into the performance of fully optimized parallel programs by measuring them with asynchronous-sampling-based call path profiles; attributing the resulting binary-level measurements to source code structure; analyzing measurements on-the-fly and postmortem to highlight performance inefficiencies; and presenting the resulting context- sensitive metrics in three complementary views. To support this thesis, we have developed several techniques for identifying performance problems in fully optimized serial, multithreaded and petascale programs. First, we describe how to attribute very precise (instruction-level) measurements to source-level static and dynamic contexts in fully optimized applications --- all for an average run-time overhead of a few percent. We then generalize this work with the development of logical call path profiling and apply it to work-stealing-based applications. Second, we describe techniques for pinpointing and quantifying parallel inefficiencies such as parallel idleness, parallel overhead and lock contention in multithreaded executions. Third, we show how to diagnose scalability bottlenecks in petascale applications by scaling our our measurement, analysis and presentation tools to support large-scale executions. Finally, we provide a coherent framework for these techniques by sketching a unique and comprehensive performance analysis methodology. This work forms the basis of Rice University's HPCTOOLKIT performance tools.

Cited by 0Open access

Performance Analysis and Optimization on a Parallel Atmospheric General

2008

An analysis is presented of the primary factors influencing the performance of a parallel implementation of the UCLA atmospheric general circulation model (AGCM) on distributedmemory, massively parallel computer systems. Several modifications to the original parallel AGCM code aimed at improving its numerical efficiency, load-balance and single-node code performance are discussed. The impact of these optimization strategies on the performance on two of the state-of-the-art parallel computers, the Intel Paragon and Cray T3D, is presented and analyzed. It is found that implementation of a loadbalanced FFT algorithm results in a reduction in overall execution time of approximately 45 % compared to the original convolution-based algorithm. Preliminary results of the application of a load-balancing scheme for the Physics part of the AGCM code suggest additional reductions in execution time of 15-20 % can be achieved. Finally, several strategies for improving the single-node performance of the code are presented, and the results obtained thus far suggest reductions in execution time in the range of 30-40 % are possible. 1.

Cited by 0

Performance Analysis of Scheduling Algorithms in Simulated Parallel Environment

Anupreet Kaur, Pawan Kumar · 2015

evaluate and compare the performance of various Scheduling Algorithms. Efficiency of scheduling algorithms is essential in order to attain optimal performance from parallel programming systems. The proposed work consider a connected set of workstations as a “pool of processors ” to analyze the performance of scheduling algorithms. Scheduling algorithms select the best possible subset of workstations for a task to minimize its completion time. Virtualization of parallelization environment is carried out with the help of simulated program, which simulates all components of actual processors so as to give best possible outcome. Such simulated framework will provide the stage for the young researchers to model and evaluate their scheduling policies on virtual parallel environment. The paper presents first come first serve, smallest job first and round robin scheduling approaches where general problem is analyzed and modeled for a simple scenario. The performance of proposed schemes is evaluated and benefits are highlighted in terms of Response

Cited by 0

Parallel program performance analysis and prediction on NOW systems.

Kuan Ching Li · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2001

Processamento distribuído tem sido utilizado amplamente para melhorar o desempenho de aplicações com alta demanda computacional. Diferentes arquiteturas e topologias distribuídas têm sido pesquisadas e utilizadas para prover o alto desempenho, proporcionando assim o recurso necessário para a exploração do paralelismo presente nas aplicações. A facilidade para construir sistemas computacionais de alto desempenho a partir de estações de trabalho interligadas através de redes de alta velocidade, aliada ao custo relativamente baixo e ao crescente avanço da tecnologia de circuitos integrados, possibilita a montagem de redes de computadores de baixo custo para a execução de aplicações paralelas. Devido a este fato, diversos sistemas de software para redes de estações têm sido desenvolvidos, visando a integração dos componentes distribuídos para a agregação das suas capacidades de processamento. No entanto, o processo de desenvolvimento de aplicações é complexo e difícil, dado que são necessários identificar o paralelismo existente nestas aplicações, e providenciar as comunicações necessárias. Neste trabalho, é apresentada uma proposta de metodologia de análise e predição de desempenho de programas paralelos, implementados com interface de passagem de mensagem (MPI), em ambientes de redes de estações de trabalho. É definida neste trabalho uma extensão da classe de grafos de tempo T-graph, denominado T-graph*, que representa, em alto nível, os programas paralelos instrumentados com MPI no nível de grafos. Com a construção de um grafo nesta classe, é possível conhecer o fluxo da execução do programa, do ponto de vista algorítmico. Ainda, é definida uma outra classe de grafos, denominada DP*Graph, que representa os programas paralelos com alto grau de detalhes, como mostrar de forma clara pontos de ocorrência de comunicação entre os nós de processamento do sistema computacional. Em paralelo com recursos e técnicas de modelagem analítica, são definidas estratégias para a avaliação de desempenho dos sistemas computacionais envolvidos. Uma vez obtidas as representações em grafos do programa paralelo e junto com as modelagens já refinadas e definidas, é possível efetuar avaliações necessárias e obter assim predições de desempenho, baseadas em dados experimentais obtidos previamente. Finalmente, os resultados experimentais obtidos mostram a viabilidade da metodologia definida nesta proposta, tanto a sua utilização e quanto à coerência das estratégias aplicadas neste trabalho.

Cited by 0

Performance Analysis of Two Parallel Relays with ARQ Protocol

Lun Tang, Rong Chai, Qianbin Chen et al. · 2009

Cooperative transmission is an efficient technique to realize diversity gain in wireless fading channels via a distributed way. In this paper, we consider a wireless network composed of a source, two parallel relays and a destination terminal. We exploit the limited feedback from the destination and propose cooperative ARQ protocols for two parallel relays. An analysis model to analyze and compare the data link layer packet error rate (PER) and throughput of cooperative transmission with joint relay selection and cooperative ARQ protocols for two parallel relays in slow fading wireless channel is established. Furthermore, The performance of this scheme are evaluated through numerical analysis and Monte-Carlo simulations and it is demonstrated that the proposed scheme can always achieve the lowest packet error rate (PER) and the highest throughput.

Cited by 0