Lightweight And Statistical Techniques For Petascale Debugging
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Lightweight And Statistical Techniques For Petascale Debugging
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Author :
language : en
Publisher:
Release Date : 2011
Lightweight And Statistical Techniques For Petascale Debugging written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Petascale platforms with O(105) and O(106) processing cores are driving advancements in a wide range of scientific disciplines. These large systems create unprecedented application development challenges. Scalable correctness tools are critical to shorten the time-to-solution on these systems. Currently, many DOE application developers use primitive manual debugging based on printf or traditional debuggers such as TotalView or DDT. This paradigm breaks down beyond a few thousand cores, yet bugs often arise above that scale. Programmers must reproduce problems in smaller runs to analyze them with traditional tools, or else perform repeated runs at scale using only primitive techniques. Even when traditional tools run at scale, the approach wastes substantial effort and computation cycles. Continued scientific progress demands new paradigms for debugging large-scale applications. The Correctness on Petascale Systems (CoPS) project is developing a revolutionary debugging scheme that will reduce the debugging problem to a scale that human developers can comprehend. The scheme can provide precise diagnoses of the root causes of failure, including suggestions of the location and the type of errors down to the level of code regions or even a single execution point. Our fundamentally new strategy combines and expands three relatively new complementary debugging approaches. The Stack Trace Analysis Tool (STAT), a 2011 R & D 100 Award Winner, identifies behavior equivalence classes in MPI jobs and highlights behavior when elements of the class demonstrate divergent behavior, often the first indicator of an error. The Cooperative Bug Isolation (CBI) project has developed statistical techniques for isolating programming errors in widely deployed code that we will adapt to large-scale parallel applications. Finally, we are developing a new approach to parallelizing expensive correctness analyses, such as analysis of memory usage in the Memgrind tool. In the first two years of the project, we have successfully extended STAT to determine the relative progress of different MPI processes. We have shown that the STAT, which is now included in the debugging tools distributed by Cray with their large-scale systems, substantially reduces the scale at which traditional debugging techniques are applied. We have extended CBI to large-scale systems and developed new compiler based analyses that reduce its instrumentation overhead. Our results demonstrate that CBI can identify the source of errors in large-scale applications. Finally, we have developed MPIecho, a new technique that will reduce the time required to perform key correctness analyses, such as the detection of writes to unallocated memory. Overall, our research results are the foundations for new debugging paradigms that will improve application scientist productivity by reducing the time to determine which package or module contains the root cause of a problem that arises at all scales of our high end systems. While we have made substantial progress in the first two years of CoPS research, significant work remains. While STAT provides scalable debugging assistance for incorrect application runs, we could apply its techniques to assertions in order to observe deviations from expected behavior. Further, we must continue to refine STAT's techniques to represent behavioral equivalence classes efficiently as we expect systems with millions of threads in the next year. We are exploring new CBI techniques that can assess the likelihood that execution deviations from past behavior are the source of erroneous execution. Finally, we must develop usable correctness analyses that apply the MPIecho parallelization strategy in order to locate coding errors. We expect to make substantial progress on these directions in the next year but anticipate that significant work will remain to provide usable, scalable debugging paradigms.
Lightweight And Statistical Techniques For Petascale Petascale Debugging
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Author :
language : en
Publisher:
Release Date : 2014
Lightweight And Statistical Techniques For Petascale Petascale Debugging written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
This project investigated novel techniques for debugging scientific applications on petascale architectures. In particular, we developed lightweight tools that narrow the problem space when bugs are encountered. We also developed techniques that either limit the number of tasks and the code regions to which a developer must apply a traditional debugger or that apply statistical techniques to provide direct suggestions of the location and type of error. We extend previous work on the Stack Trace Analysis Tool (STAT), that has already demonstrated scalability to over one hundred thousand MPI tasks. We also extended statistical techniques developed to isolate programming errors in widely used sequential or threaded applications in the Cooperative Bug Isolation (CBI) project to large scale parallel applications. Overall, our research substantially improved productivity on petascale platforms through a tool set for debugging that complements existing commercial tools. Previously, Office Of Science application developers relied either on primitive manual debugging techniques based on printf or they use tools, such as TotalView, that do not scale beyond a few thousand processors. However, bugs often arise at scale and substantial effort and computation cycles are wasted in either reproducing the problem in a smaller run that can be analyzed with the traditional tools or in repeated runs at scale that use the primitive techniques. New techniques that work at scale and automate the process of identifying the root cause of errors were needed. These techniques significantly reduced the time spent debugging petascale applications, thus leading to a greater overall amount of time for application scientists to pursue the scientific objectives for which the systems are purchased. We developed a new paradigm for debugging at scale: techniques that reduced the debugging scenario to a scale suitable for traditional debuggers, e.g., by narrowing the search for the root-cause analysis to a small set of nodes or by identifying equivalence classes of nodes and sampling our debug targets from them. We implemented these techniques as lightweight tools that efficiently work on the full scale of the target machine. We explored four lightweight debugging refinements: generic classification parameters, such as stack traces, application-specific classification parameters, such as global variables, statistical data acquisition techniques and machine learning based approaches to perform root cause analysis. Work done under this project can be divided into two categories, new algorithms and techniques for scalable debugging, and foundation infrastructure work on our MRNet multicast-reduction framework for scalability, and Dyninst binary analysis and instrumentation toolkits.
Data Centric Parallel Debugging Technique For Petascale Computers
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Author : Minh Ngoc Dinh
language : en
Publisher:
Release Date : 2012
Data Centric Parallel Debugging Technique For Petascale Computers written by Minh Ngoc Dinh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Petascale computers and computing systems have the potential to solve large-scale, data-intensive problems in science and engineering. Petascale scientific applications, such as the Weather Research and Forecasting Model (WRF), involve enormous multi-dimensional data structures and operate with hundreds of thousands of concurrent processing threads. On the one hand, programming languages and environments have evolved significantly to support parallel application developers to explore the advantages in terms of computational power and memory usage. Co-array Fortran, Split-C, MPI and OpenMP are some successful examples. On the other hand, debugging tools for highly parallel software are still immature, especially in techniques for controlling multiple processes and monitoring large scale data structures during debugging time. Typically, contemporary parallel debuggers allow users to control more than one processing thread while supporting the same examination and visualisation operations that of sequential debuggers. This approach restricts the use of parallel debuggers when it comes to large scale scientific applications run across hundreds of thousands compute cores. First, manually observing the runtime data to detect error becomes impractical because the data is too big. Second, performing expensive but useful debugging operations, such as distributed expression evaluation, becomes infeasible as the computational codes become more complex, involving larger data structures, and as the machines become larger.This thesis explores the idea of a data-centric debugging approach, which could be used to make parallel debuggers more powerful. It discusses the use of ad-hoc debug-time assertions that allow a user to reason about the state of a parallel computation. These assertions are modeled on programming language systems that support the verification and validation of program state as a whole rather than focusing on that of only a single process state. The advantage of this approach is the capability to reason about the massive data structure at runtime. Furthermore, on parallel machines, the debugger's performance can be improved by exploiting the underlying parallel platform. The available compute cores can execute parallel debugging functions while idling at a program breakpoint.
High Performance Computing For Structural Mechanics And Earthquake Tsunami Engineering
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Author : Shinobu Yoshimura
language : en
Publisher: Springer
Release Date : 2015-10-26
High Performance Computing For Structural Mechanics And Earthquake Tsunami Engineering written by Shinobu Yoshimura and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-26 with Science categories.
Huge earthquakes and tsunamis have caused serious damage to important structures such as civil infrastructure elements, buildings and power plants around the globe. To quantitatively evaluate such damage processes and to design effective prevention and mitigation measures, the latest high-performance computational mechanics technologies, which include telascale to petascale computers, can offer powerful tools. The phenomena covered in this book include seismic wave propagation in the crust and soil, seismic response of infrastructure elements such as tunnels considering soil-structure interactions, seismic response of high-rise buildings, seismic response of nuclear power plants, tsunami run-up over coastal towns and tsunami inundation considering fluid-structure interactions. The book provides all necessary information for addressing these phenomena, ranging from the fundamentals of high-performance computing for finite element methods, key algorithms of accurate dynamic structural analysis, fluid flows with free surfaces, and fluid-structure interactions, to practical applications with detailed simulation results. The book will offer essential insights for researchers and engineers working in the field of computational seismic/tsunami engineering.
Software For Exascale Computing Sppexa 2016 2019
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Author : Hans-Joachim Bungartz
language : en
Publisher: Springer Nature
Release Date : 2020-07-30
Software For Exascale Computing Sppexa 2016 2019 written by Hans-Joachim Bungartz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Computers categories.
This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest.
Pattern Recognition And Machine Learning
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Author : Christopher M. Bishop
language : en
Publisher: Springer
Release Date : 2016-08-23
Pattern Recognition And Machine Learning written by Christopher M. Bishop and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-23 with Computers categories.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
Contemporary High Performance Computing
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Author : Jeffrey S. Vetter
language : en
Publisher: CRC Press
Release Date : 2017-11-23
Contemporary High Performance Computing written by Jeffrey S. Vetter and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-23 with Computers categories.
Contemporary High Performance Computing: From Petascale toward Exascale focuses on the ecosystems surrounding the world’s leading centers for high performance computing (HPC). It covers many of the important factors involved in each ecosystem: computer architectures, software, applications, facilities, and sponsors. The first part of the book examines significant trends in HPC systems, including computer architectures, applications, performance, and software. It discusses the growth from terascale to petascale computing and the influence of the TOP500 and Green500 lists. The second part of the book provides a comprehensive overview of 18 HPC ecosystems from around the world. Each chapter in this section describes programmatic motivation for HPC and their important applications; a flagship HPC system overview covering computer architecture, system software, programming systems, storage, visualization, and analytics support; and an overview of their data center/facility. The last part of the book addresses the role of clouds and grids in HPC, including chapters on the Magellan, FutureGrid, and LLGrid projects. With contributions from top researchers directly involved in designing, deploying, and using these supercomputing systems, this book captures a global picture of the state of the art in HPC.
Data Intensive Text Processing With Mapreduce
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Author : Jimmy Lin
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Data Intensive Text Processing With Mapreduce written by Jimmy Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
Frontiers In Massive Data Analysis
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Author : National Research Council
language : en
Publisher: National Academies Press
Release Date : 2013-09-03
Frontiers In Massive Data Analysis written by National Research Council and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-03 with Mathematics categories.
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Tools For High Performance Computing 2009
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Author : Matthias S. Müller
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-05-27
Tools For High Performance Computing 2009 written by Matthias S. Müller and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-27 with Computers categories.
As more and more hardware platforms support parallelism, parallel programming is gaining momentum. Applications can only leverage the performance of multi-core processors or graphics processing units if they are able to split a problem into smaller ones that can be solved in parallel. The challenges emerging from the development of parallel applications have led to the development of a great number of tools for debugging, performance analysis and other tasks. The proceedings of the 3rd International Workshop on Parallel Tools for High Performance Computing provide a technical overview in order to help engineers, developers and computer scientists decide which tools are best suited to enhancing their current development processes.