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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Brian Van Essen Roger Pearce Sasha Ames Maya Gokhale Center for Applied Scientific Computing Lawrence Livemore National Laboratory Livermore CA USA Department of Computer Science and Engineering Texas A and M University USA

ISBN: (纸本)9781467309752

Data-intensive applications are best suited to high-performance computing architectures that contain large quantities of main memory. Creating these systems with DRAM-based main memory remains costly and power-intensive. Due to improvements in density and cost, non-volatile random access memories (NVRAM) have emerged as compelling storage technologies to augment traditional DRAM. This work explores the potential of future NVRAM technologies to store program state at performance comparable to DRAM. We have developed the PerMA NVRAM simulator that allows us to explore applications with working sets ranging up to hundreds of gigabytes per node. The simulator is implemented as a Linux device driver that allows application execution at native speeds. Using the simulator we show the impact of future technology generations of I/O-bus-attached NVRAM on an unstructured-access, level-asynchronous, Breadth-First Search (BFS) graph traversal algorithm. Our simulations show that within a couple of technology generations, a system architecture with local high performance NVRAM will be able to effectively augment DRAM to support highly concurrent data-intensive applications with large memory footprints. However, improvements will be needed in the I/O stack to deliver this performance to applications. The simulator shows that future technology generations of NVRAM in conjunction with an improved I/O runtime will enable parallel data-intensive applications to offload in-memory data structures to NVRAM with minimal performance loss.

关键词： Random access memory Nonvolatile memory Performance evaluation Delay computer architecture Flash memory Linux

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Novel Views of Performance Data to Analyze Large-scale Adaptive Applications 12

Novel Views of Performance Data to Analyze Large-scale Adapt...

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ACM/IEEE International Conference for High Performance computing, Networking, Storage, and Analysis

作者： Abhinav Bhatele Todd Gamblin Katherine E. Isaacs Brian T. N. Gunney Martin Schulz Peer-Timo Bremer Bernd Hamann Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA 94551 USA Department of Computer Science University of California Davis CA 95616 USA

ISBN: (纸本)9781467308052

Performance analysis of parallel scientific codes is becoming increasingly difficult due to the rapidly growing complexity of applications and architectures. Existing tools fall short in providing intuitive views that facilitate the process of performance debugging and tuning. In this paper, we extend recent ideas of projecting and visualizing performance data for faster, more intuitive analysis of applications. We collect detailed per-level and per-phase measurements for a dynamically load-balanced, structured AMR library and project per-core data collected in the hardware domain on to the application's communication topology. We show how our projections and visualizations lead to a rapid diagnosis of and mitigation strategy for a previously elusive scaling bottleneck in the library that is hard to detect using conventional tools. Our new insights have resulted in a 22% performance improvement for a 65,536-core run of the AMR library on an IBM Blue Gene/P system.

关键词： performance debugging rapidly growing complexity IBM interacting boson model IBM Libraries Phase measurement View Amphiregulin Antimicrobial Drug Resistance automatic meter reading Visualization rapid diagnosis Intuitive performance boost EQUIPMENT

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A hierarchical approach for load balancing on parallel multi-core systems

A hierarchical approach for load balancing on parallel multi...

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41st International Conference on Parallel Processing, ICPP 2012

作者： Pilla, Laércio L. Ribeiro, Christiane Pousa Cordeiro, Daniel Mei, Chao Bhatele, Abhinav Navaux, Philippe O.A. Broquedis, François Méhaut, Jean-François Kale, Laxmikant V. Institute of Informatics Federal University of Rio Grande Do Sul Porto Alegre Brazil LIG Laboratory CEA/INRIA Grenoble University Grenoble France Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA United States

ISBN: (纸本)9780769547961

Multi-core compute nodes with non-uniform memory access (NUMA) are now a common architecture in the assembly of large-scale parallel machines. On these machines, in addition to the network communication costs, the memory access costs within a compute node are also asymmetric. Ignoring this can lead to an increase in the data movement costs. Therefore, to fully exploit the potential of these nodes and reduce data access costs, it becomes crucial to have a complete view of the machine topology (i.e. the compute node topology and the interconnection network among the nodes). Furthermore, the parallel application behavior has an important role in determining how to utilize the machine efficiently. In this paper, we propose a hierarchical load balancing approach to improve the performance of applications on parallel multi-core systems. We introduce NucoLB, a topology-aware load balancer that focuses on redistributing work while reducing communication costs among and within compute nodes. NucoLB takes the asymmetric memory access costs present on NUMA multi-core compute nodes, the interconnection network overheads, and the application communication patterns into account in its balancing decisions. We have implemented NucoLB using the Charm++ parallel runtime system and evaluated its performance. Results show that our load balancer improves performance up to 20% when compared to state-of-the-art load balancers on three different NUMA parallel machines. © 2012 IEEE.

关键词： Topology

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Heterogeneous Task Scheduling for Accelerated OpenMP

Heterogeneous Task Scheduling for Accelerated OpenMP

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Thomas R.W. Scogland Barry Rountree Wu-chun Feng Bronis R. de Supinski Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA USA Center for Applied Scientific Computing Lawrence Livemore National Laboratory Livermore CA USA

Heterogeneous systems with CPUs and computational accelerators such as GPUs, FPGAs or the upcoming Intel MIC are becoming mainstream. In these systems, peak performance includes the performance of not just the CPUs but also all available accelerators. In spite of this fact, the majority of programming models for heterogeneous computing focus on only one of these. With the development of Accelerated Open MP for GPUs, both from PGI and Cray, we have a clear path to extend traditional Open MP applications incrementally to use GPUs. The extensions are geared toward switching from CPU parallelism to GPU parallelism. However they do not preserve the former while adding the latter. Thus computational potential is wasted since either the CPU cores or the GPU cores are left idle. Our goal is to create a runtime system that can intelligently divide an accelerated Open MP region across all available resources automatically. This paper presents our proof-of-concept runtime system for dynamic task scheduling across CPUs and GPUs. Further, we motivate the addition of this system into the proposed Open MP for Accelerators standard. Finally, we show that this option can produce as much as a two-fold performance improvement over using either the CPU or GPU alone.

关键词： Graphics processing unit Dynamic scheduling Integrated circuits Acceleration Schedules Programming Runtime

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Abstract: Exploring Performance Data with Boxfish

Abstract: Exploring Performance Data with Boxfish

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High Performance computing, Networking, Storage and Analysis (SCC), SC Companion:

作者： Katherine E. Isaacs Aaditya G. Landge Todd Gamblin Peer-Timo Bremer Valerio Pascucci Bernd Hamann Institute for Data Analysis and Visualization Department of Computer Science University of California Davis CA USA Scientific Computing and Imaging Institute University of Utah Salt Lake UT USA Center for Applied Scientific Computing Lawrence Livermore National Laboratories Livermore CA USA

The growth in size and complexity of scaling applications and the systems on which they run pose challenges in analyzing and improving their overall performance. With metrics coming from thousands or millions of processes, visualization techniques are necessary to make sense of the increasing amount of data. To aid the process of exploration and understanding, we announce the initial release of Boxfish, an extensible tool for manipulating and visualizing data pertaining to application behavior. Combining and visually presenting data and knowledge from multiple domains, such as the application's communication patterns and the hardware's network configuration and routing policies, can yield the insight necessary to discover the underlying causes of observed behavior. Boxfish allows users to query, filter and project data across these domains to create interactive, linked visualizations.

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DI-MMAP: A High Performance Memory-Map Runtime for Data-Intensive Applications

DI-MMAP: A High Performance Memory-Map Runtime for Data-Inte...

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High Performance computing, Networking, Storage and Analysis (SCC), SC Companion:

作者： Brian Van Essen Henry Hsieh Sasha Ames Maya Gokhale Lawrence Livermore National Laboratory Livermore CA US Department of Computer Science University of California Los Angeles USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA

ISBN: (纸本)9781467362184

We present DI-MMAP, a high-performance runtime that memory-maps large external data sets into an application's address space and shows significantly better performance than the Linux mmap system call. Our implementation is particularly effective when used with high performance locally attached Flash arrays on highly concurrent, latency-tolerant data-intensive HPC applications. We describe the kernel module and show performance results on a benchmark test suite and on a new bioinformatics metagenomic classification application. For the complex metagenomics classification application, DI-MMAP performs up to 4.88× better than standard Linux mmap.

关键词： Runtime Linux Benchmark testing Standards Databases Random access memory Instruction sets

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Erratum to: Measurement of the inclusive jet cross-section in proton-proton collisions at s = 7 TeV using 4.5 fb −1 of data with the ATLAS detector

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Journal of High Energy Physics 2015年第9期2015卷 1-39页

作者： G. Aad B. Abbott J. Abdallah S. Abdel Khalek O. Abdinov R. Aben B. Abi M. Abolins O. S. AbouZeid H. Abramowicz H. Abreu R. Abreu Y. Abulaiti B. S. Acharya L. Adamczyk D. L. Adams J. Adelman S. Adomeit T. Adye T. Agatonovic-Jovin J. A. Aguilar-Saavedra M. Agustoni S. P. Ahlen F. Ahmadov G. Aielli H. Akerstedt T. P. A. Åkesson G. Akimoto A. V. Akimov G. L. Alberghi J. Albert S. Albrand M. J. Alconada Verzini M. Aleksa I. N. Aleksandrov C. Alexa G. Alexander G. Alexandre T. Alexopoulos M. Alhroob G. Alimonti L. Alio J. Alison B. M. M. Allbrooke L. J. Allison P. P. Allport J. Almond A. Aloisio A. Alonso F. Alonso C. Alpigiani A. Altheimer B. Alvarez Gonzalez M. G. Alviggi K. Amako Y. Amaral Coutinho C. Amelung D. Amidei S. P. Amor Dos Santos A. Amorim S. Amoroso N. Amram G. Amundsen C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders G. Anders K. J. Anderson A. Andreazza V. Andrei X. S. Anduaga S. Angelidakis I. Angelozzi P. Anger A. Angerami F. Anghinolfi A. V. Anisenkov N. Anjos A. Annovi A. Antonaki M. Antonelli A. Antonov J. Antos F. Anulli M. Aoki L. Aperio Bella R. Apolle G. Arabidze I. Aracena Y. Arai J. P. Araque A. T. H. Arce J-F. Arguin S. Argyropoulos M. Arik A. J. Armbruster O. Arnaez V. Arnal H. Arnold M. Arratia O. Arslan A. Artamonov G. Artoni S. Asai N. Asbah A. Ashkenazi B. Åsman L. Asquith K. Assamagan R. Astalos M. Atkinson N. B. Atlay B. Auerbach K. Augsten M. Aurousseau G. Avolio G. Azuelos Y. Azuma M. A. Baak A. Baas C. Bacci H. Bachacou K. Bachas M. Backes M. Backhaus J. Backus Mayes E. Badescu P. Bagiacchi P. Bagnaia Y. Bai T. Bain J. T. Baines O. K. Baker P. Balek F. Balli E. Banas Sw. Banerjee A. A. E. Bannoura V. Bansal H. S. Bansil L. Barak S. P. Baranov E. L. Barberio D. Barberis M. Barbero T. Barillari M. Barisonzi T. Barklow N. Barlow B. M. Barnett R. M. Barnett Z. Barnovska A. Baroncelli G. Barone A. J. Barr F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton P. Bartos V. Bartsch A. Bassalat A. Basye R. L. Bates J. R. Batley M. Battaglia M. Battistin F. Bauer H CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States Institute of Physics Academia Sinica Taipei Taiwan LAL Université Paris-Sud and CNRS/IN2P3 Orsay France Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam Netherlands Department of Physics Oklahoma State University Stillwater United States Department of Physics and Astronomy Michigan State University East Lansing United States Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel CERN Geneva Switzerland Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London United Kingdom AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Physics Department Brookhaven National Laboratory Upton United States Department of Physics Yale University New Haven United States Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Laboratorio de Instrumentacao e Fisica Experimental de Particulas - LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada Spain Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland Department of Physics Boston University Boston United States Joint Institute for Nuclear Research JINR Dubna Dubna Russia INFN

It was found that the non-perturbative corrections calculated using Pythia with the Perugia 2011 tune did not include the effect of the underlying event. The affected correction factors were recomputed using the Pythia 6.427 generator. These corrections are applied as baseline to the NLO pQCD calculations and thus the central values of the theoretical predictions have changed by a few percent with the new corrections. This has a minor impact on the agreement between the data and the theoretical predictions. Figures 2 and 6 to 13, and all the tables have been updated with the new values. A few sentences in the discussion in sections 5.2 and 9 were altered or removed.

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Lecture Notes in Computational science and Engineering: Preface

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Lecture Notes in Computational science and Engineering 2012年 87 LNCSE卷 v-vii页

作者： Forth, Shaun Hovland, Paul Phipps, Eric Utke, Jean Walther, Andrea Applied Mathematics and Scientific Computing Cranfield University Shrivenham Swindon United Kingdom Mathematics and Computer Science Division Argonne National Laboratory Argonne IL United States Sandia National Laboratory Albuquerque NM United States Department of Mathematics University of Paderborn Paderborn Germany

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Modeling the Performance of an Algebraic Multigrid Cycle Using Hybrid MPI/OpenMP

Modeling the Performance of an Algebraic Multigrid Cycle Usi...

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International Conference on Parallel Processing (ICPP)

作者： Hormozd Gahvari William Gropp Kirk E. Jordan Martin Schulz Ulrike Meier Yang Department of Computer Science University of Illinois Urbana-Champaign Urbana IL USA Computational Science Center IBM Thomas J. Watson Research Center Cambridge MA USA Center for Applied Scientific Computing Lawrence Livemore National Laboratory Livermore CA USA

The rise of multicore cluster architectures has led to intense interest in using a combination of MPI and OpenMP to more effectively program these machines. We present a performance model for hybrid implementation of the solve cycle of algebraic multigrid (AMG), a popular iterative solver for large sparse linear systems and a key component of many scientific simulations. We validate the model on two leading parallel platforms, and discuss implications for applications programmed in a hybrid model on future machines.

关键词： Bandwidth Multicore processing Message systems Interpolation Computational modeling Instruction sets

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Performance Modeling of Algebraic Multigrid on Blue Gene/Q: Lessons Learned

Performance Modeling of Algebraic Multigrid on Blue Gene/Q: ...

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High Performance computing, Networking, Storage and Analysis (SCC), SC Companion:

作者： Hormozd Gahvari William Gropp Kirk E. Jordan Martin Schulz Ulrike Meier Yang Department of Computer Science University of Illinois Urbana-Champaign Urbana IL USA IBM Thomas J. Watson Research Center Cambridge MA USA Center for Applied Scientific Computing Lawrence Livermore National Laboratories Livermore CA USA

ISBN: (纸本)9781467362184

The IBM Blue Gene/Q represents a large step in the evolution of massively parallel machines. It features 16-core compute nodes, with additional parallelism in the form of four simultaneous hardware threads per core, connected together by a five-dimensional torus network. Machines are being built with core counts in the hundreds of thousands, with the largest, Sequoia, featuring over 1.5 million cores. In this paper, we develop a performance model for the solve cycle of algebraic multigrid on Blue Gene/Q to help us understand the issues this popular linear solver for large, sparse linear systems faces on this architecture. We validate the model on a Blue Gene/Q at IBM, and conclude with a discussion of the implications of our results.

关键词： PERFORMANCE MODELING core interacting boson model IBM parallel machines Algebra color blue lessons learned

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