DOCSLIB.ORG

Understanding the Usage of MPI in Exascale Proxy Applications

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Understanding the Usage of MPI in Exascale Proxy Applications Understanding the Usage of MPI in Exascale Proxy Applications Nawrin Sultana Anthony Skjellum Purushotham Bangalore Auburn University University of Tennessee at Chattanooga University of Alabama at Birmingham Auburn, AL Chattanooga, TA Birmingham, AL [email protected] [email protected] [email protected] Ignacio Laguna Kathryn Mohror Lawrence Livermore National Laboratory Lawrence Livermore National Laboratory Livermore, CA Livermore, CA [email protected] [email protected] Abstract—The Exascale Computing Project (ECP) focuses on suite [5] consists of proxies of ECP projects that represent the the development of future exascale-capable applications. Most key characteristics of these exascale applications. ECP applications use the Message Passing Interface (MPI) as In this paper, we explore the usage patterns of MPI among their parallel programming model and create mini-apps to serve as proxies. This paper explores the explicit usage of MPI in ECP proxy applications. We explore fourteen proxy appli- ECP proxy applications. For our study, we empirically analyze cations in particular from the ECP Proxy Apps Suite and fourteen proxy applications from the ECP Proxy Apps Suite. Our summarize the MPI functionality used in each. An earlier result shows that a small subset of features from MPI is com- survey [6] on different U.S. DOE applications shows that monly used in the proxies of exascale capable applications, even the HPC community will continue to use MPI in future when they reference third-party libraries. Our study contributes to a better understanding of the use of MPI in current exascale exascale systems for its flexibility, portability, and efficiency. applications. This finding can help focus software investments We study the proxies of those applications to understand how made for exascale systems in the MPI middleware including much and what features of the MPI standard they are using. optimization, fault-tolerance, tuning, and hardware-offload. This provides appropriate guidance to developers of full MPI Index Terms—Exascale Proxy Applications, MPI, Empirical implementations as well as extremely efficient subsets for Analysis future systems. There are multiple open source MPI implementations avail- I. INTRODUCTION able (e.g., OpenMPI [7], MPICH [8], and MVAPICH [9]), In high-performance computing (HPC) systems, MPI is an and many high-end vendors base their commercial versions extensively used API for data communication [1]. A con- on these source bases (e.g., Intel, Cray, and IBM). All of siderable number of large-scale scientific applications have the currently available MPI implementations are monoliths. been written based on MPI. These applications are success- However, sometimes a small subset of MPI is capable of fully running in current petascale systems with 1,000’s to running the proxy of a real application [10]. Large-scale appli- 100,000’s of processors [2] and will continue to run in future cations often use only a subset of MPI functionality. Our work exascale systems [3]. To increase the applicability of MPI in contributes to a better understanding of MPI usage in mini- next-generation exascale systems, the MPI Forum is actively apps that represent real exascale applications. It shows that working on updating and incorporating new constructs to MPI a small subset of features from MPI standard are commonly standard. used in the proxies of future exascale capable applications. The Exascale Computing Project (ECP) [4] is an effort to The remainder of this paper is organized as follows: Section accelerate the development and delivery of a capable exascale II discusses the overview of the proxy apps used in our study. computing ecosystem. Different ECP applications use different Section III demonstrates the MPI usage patterns among those parallel programming models. One of the focuses of ECP is to applications. Finally, we offer conclusions in section IV. provide exascale capability and continued relevance to MPI. These ECP applications are typically large and complex with II. OVERVIEW OF APPLICATIONS thousands to millions lines of code. As a means to access For our study, we targeted ECP Proxy Applications Suite 2.0 their performance and capabilities, most of the applications [5], which contains 15 proxy apps. We focused on applications create “mini-apps” to serve as their proxies or exemplars. that use MPI. We found 14 applications in the suite that use Proxy application represents the key characteristics of the real MPI for communication. Some of these applications use a application without sharing the actual details. As part of ECP, hybrid model (OpenMP and MPI) of parallel programming multiple proxy apps have been developed. The ECP proxy apps where OpenMP is used for parallelism within a node while TABLE I APPLICATION OVERVIEW Application Language Description Third-party Library Programming Model Parallel algebric multigrid solver for linear systems AMG C hypre MPI, OpenMP arising from problems on unstructured grids Represent highly simplified communication patterns Ember C N/A MPI relevant to DOE application workloads Proxy application for Molecular Dynamics with ExaMiniMD C++ N/A MPI a Modular design Solves Euler equation of compressible gas dynamics Laghos C++ hypre, MFEM, Metis MPI using unstructured high-order finite elements Multi-purpose, scalable I/O proxy application that MACSio C HDF5, Silo MPI mimics I/O worklods of real applications Applies a 3D stencil calculation on a unit cube miniAMR C N/A MPI, OpenMP computational domain– divided into blocks Designed to evaluate different programming models miniQMC C++ N/A MPI, OpenMP for performance portability Detects graph community by implementing Louvain miniVite C++ N/A MPI, OpenMP method in distributed memory Thermal Hydraulics mini-app that solves a standard NEKbone Fortran N/A MPI Poisson equation Portrays the computational loads and dataflow of PICSARlite Fortran N/A MPI complex Particle-In-Cell codes Solves the seismic wave equations in Cartesian SW4lite C, Fortran N/A MPI, OpenMP coordinates Run 3D distributed memory discrete fast Fourier SWFFT C FFTW3 (MPI interface not used) MPI transform Solves radiative trasnfer equation in a multi-group thornado-mini Fortran HDF5 MPI two-moment approximation Represents a key computational kernel of the XSBench C N/A MPI, OpenMP Monte Carlo neutronics application MPI is used for parallelism among nodes. We empirically The majority of applications (93%) of Table I use analyzed these fourteen applications to understand how they MPI_Init for initialization. Only one application (PICSAR- use MPI. For the purpose of our study, we only did static code lite) uses MPI thread based initialization. In PICSARlite, analysis. MPI_Init_thread is called with MPI_THREAD_SINGLE Table I provides an overview of the applications. Although and MPI_THREAD_FUNNELED. In both cases, only one some of the applications are written in C++, they all use C or thread makes the MPI calls. Fortran to call MPI routines. A number of applications (36%) are dependent on third-party software libraries. However, not B. MPI Communication all of these libraries are MPI-based. Four of the applications— The MPI standard [13], [14] provides different techniques AMG, Laghos, MACSio, and thornado-mini use MPI-based for communication among processes. A set of processes are third-party numerical and I/O libraries. managed using “communicator” – an object that allows com- Almost 50% of the applications (6) use both MPI and munication isolation for a group of processes. OpenMP as their parallel programming model. The hypre [11] Point-to-point. Transmit message between a pair of pro- library also uses OpenMP along with MPI. The data model cesses where sender and receiver cooperate with each other, library, HDF5 [12] uses “pthread” as its parallel execution which is referred to as “two-sided” communication. To com- model. municate a message, the source process calls a send operation and the target process must call a receive operation. MPI III. USAGE OF MPI IN ECP PROXY APPS provides both blocking and non-blocking forms of point-to- point communications. In this section, we analyze the proxy applications to eluci- Wildcard. MPI allows the receive and probe operations to date their MPI usage patterns. specify a wildcard value for source and/or tag. It indicates that a process will accept a message from any source and/or A. MPI Initialization tag. The source wildcard is MPI_ANY_SOURCE and the tag All MPI programs must call MPI_Init or wildcard is MPI_ANY_TAG. The scope of these wildcards is MPI_Init_thread to initialize the MPI execution limited to the processes of the specified communicator. environment [13]. MPI can be initialized at most once. Collective. A communication that involves participation of In addition to initializing MPI, MPI_Init_thread also all processes of a given communicator. All processes of the initializes the MPI thread environment. It requests the desired given communicator need to make the collective call. Collec- level of thread support using the argument “required”. tive communications do not use tag. Collective communica- TABLE II MPI CALLS USED FOR COMMUNICATIONS Point-to-point Collective Application Blocking Non-blocking Blocking Non-blocking MPI Allreduce, MPI Reduce MPI Allgatherfvg, MPI Gatherfvg MPI Isend MPI Send MPI Alltoall AMG, Laghos MPI Irsend N/A MPI Recv MPI Barrier, MPI Bcast MPI Irecv MPI Scan MPI Scatterfvg MPI Send MPI Isend Ember MPI Barrier N/A MPI Recv MPI Irecv MPI Allreduce ExaMiniMD MPI Send MPI Irecv MPI Barrier
Load more

Recommended publications
  • The Component Architecture of Open Mpi: Enabling Third-Party Collective Algorithms∗
    THE COMPONENT ARCHITECTURE OF OPEN MPI: ENABLING THIRD-PARTY COLLECTIVE ALGORITHMS∗ Jeffrey M. Squyres and Andrew Lumsdaine Open Systems Laboratory, Indiana University Bloomington, Indiana, USA [email protected] [email protected] Abstract As large-scale clusters become more distributed and heterogeneous, significant research interest has emerged in optimizing MPI collective operations because of the performance gains that can be realized. However, researchers wishing to develop new algorithms for MPI collective operations are typically faced with sig- nificant design, implementation, and logistical challenges. To address a number of needs in the MPI research community, Open MPI has been developed, a new MPI-2 implementation centered around a lightweight component architecture that provides a set of component frameworks for realizing collective algorithms, point-to-point communication, and other aspects of MPI implementations. In this paper, we focus on the collective algorithm component framework. The “coll” framework provides tools for researchers to easily design, implement, and exper- iment with new collective algorithms in the context of a production-quality MPI. Performance results with basic collective operations demonstrate that the com- ponent architecture of Open MPI does not introduce any performance penalty. Keywords: MPI implementation, Parallel computing, Component architecture, Collective algorithms, High performance 1. Introduction Although the performance of the MPI collective operations [6, 17] can be a large factor in the overall run-time of a parallel application, their optimiza- tion has not necessarily been a focus in some MPI implementations until re- ∗This work was supported by a grant from the Lilly Endowment and by National Science Foundation grant 0116050.
    [Show full text]
  • Infiniband and 10-Gigabit Ethernet for Dummies
    The MVAPICH2 Project: Latest Developments and Plans Towards Exascale Computing Presentation at OSU Booth (SC ‘19) by Hari Subramoni The Ohio State University E-mail: [email protected] http://www.cse.ohio-state.edu/~subramon Drivers of Modern HPC Cluster Architectures High Performance Accelerators / Coprocessors Interconnects - InfiniBand high compute density, high Multi-core <1usec latency, 200Gbps performance/watt SSD, NVMe-SSD, Processors Bandwidth> >1 TFlop DP on a chip NVRAM • Multi-core/many-core technologies • Remote Direct Memory Access (RDMA)-enabled networking (InfiniBand and RoCE) • Solid State Drives (SSDs), Non-Volatile Random-Access Memory (NVRAM), NVMe-SSD • Accelerators (NVIDIA GPGPUs and Intel Xeon Phi) • Available on HPC Clouds, e.g., Amazon EC2, NSF Chameleon, Microsoft Azure, etc. NetworkSummit Based Computing Sierra Sunway K - Laboratory OSU Booth SC’19TaihuLight Computer 2 Parallel Programming Models Overview P1 P2 P3 P1 P2 P3 P1 P2 P3 Memo Memo Memo Logical shared memory Memor Memor Memor Shared Memory ry ry ry y y y Shared Memory Model Distributed Memory Model Partitioned Global Address Space (PGAS) SHMEM, DSM MPI (Message Passing Interface)Global Arrays, UPC, Chapel, X10, CAF, … • Programming models provide abstract machine models • Models can be mapped on different types of systems – e.g. Distributed Shared Memory (DSM), MPI within a node, etc. • PGAS models and Hybrid MPI+PGAS models are Network Based Computing Laboratorygradually receiving importanceOSU Booth SC’19 3 Designing Communication Libraries for Multi-Petaflop and Exaflop Systems: Challenges Co-DesignCo-Design Application Kernels/Applications OpportuniOpportuni tiesties andand Middleware ChallengeChallenge ss acrossacross Programming Models VariousVarious MPI, PGAS (UPC, Global Arrays, OpenSHMEM), LayersLayers CUDA, OpenMP, OpenACC, Cilk, Hadoop (MapReduce), Spark (RDD, DAG), etc.
    [Show full text]
  • User Manual Revision: C96e096
    Bright Cluster Manager 8.1 User Manual Revision: c96e096 Date: Fri Sep 14 2018 ©2018 Bright Computing, Inc. All Rights Reserved. This manual or parts thereof may not be reproduced in any form unless permitted by contract or by written permission of Bright Computing, Inc. Trademarks Linux is a registered trademark of Linus Torvalds. PathScale is a registered trademark of Cray, Inc. Red Hat and all Red Hat-based trademarks are trademarks or registered trademarks of Red Hat, Inc. SUSE is a registered trademark of Novell, Inc. PGI is a registered trademark of NVIDIA Corporation. FLEXlm is a registered trademark of Flexera Software, Inc. PBS Professional, PBS Pro, and Green Provisioning are trademarks of Altair Engineering, Inc. All other trademarks are the property of their respective owners. Rights and Restrictions All statements, specifications, recommendations, and technical information contained herein are current or planned as of the date of publication of this document. They are reliable as of the time of this writing and are presented without warranty of any kind, expressed or implied. Bright Computing, Inc. shall not be liable for technical or editorial errors or omissions which may occur in this document. Bright Computing, Inc. shall not be liable for any damages resulting from the use of this document. Limitation of Liability and Damages Pertaining to Bright Computing, Inc. The Bright Cluster Manager product principally consists of free software that is licensed by the Linux authors free of charge. Bright Computing, Inc. shall have no liability nor will Bright Computing, Inc. provide any warranty for the Bright Cluster Manager to the extent that is permitted by law.
    [Show full text]
  • Setting up a Beowulf Cluster Using Open MPI on Linux
    Setting up a Beowulf Cluster Using Open MPI on Linux https://techtinkering.com/2009/12/02/setting-up-a-beowulf-c... TechTinkering ============= Twitter YouTube Email GitHub RSS Setting up a Beowulf Cluster Using Open MPI on Linux 2 December 2009 Lawrence Woodman #Beowulf Clusters #Distributed Processing #High Performance Computing #Linux #MPI #Parallel Processing I have been doing a lot of work recently on Linear Genetic Programming. This requires a great deal of processing power and to meet this I have been using Open MPI to create a Linux cluster. What follows is a quick guide to getting a cluster running. The basics really are very simple and, depending on the size, you can get a simple cluster running in less than half an hour, assuming you already have the machines networked and running Linux. What is a Beowulf Cluster? A Beowulf Cluster is a collection of privately networked computers which can be used for parallel computations. They consist of commodity hardware running open source software such as Linux or a BSD, often coupled with PVM (Parallel Virtual Machine) or MPI (Message Passing Interface). A standard set up will consist of one master node which will control a number of slave nodes. The slave nodes are typically headless and generally all access the same files from a server. They have been referred to as Beowulf Clusters since before 1998 when the original Beowulf HOWTO was released. What is Open MPI? Open MPI is one of the leading MPI-2 implementations used by many of the TOP 500 Supercomputers. It is open source and is developed and maintained by a consortium of academic, research, and industry partners.
    [Show full text]
  • Hybrid MPI & Openmp Parallel Programming
    Hybrid MPI & OpenMP Parallel Programming MPI + OpenMP and other models on clusters of SMP nodes Rolf Rabenseifner1) Georg Hager2) Gabriele Jost3) [email protected] [email protected] [email protected] 1) High Performance Computing Center (HLRS), University of Stuttgart, Germany 2) Regional Computing Center (RRZE), University of Erlangen, Germany 3) Texas Advanced Computing Center, The University of Texas at Austin, USA Tutorial M02 at SC10, November 15, 2010, New Orleans, LA, USA Hybrid Parallel Programming Slide 1 Höchstleistungsrechenzentrum Stuttgart Outline slide number • Introduction / Motivation 2 • Programming models on clusters of SMP nodes 6 8:30 – 10:00 • Case Studies / pure MPI vs hybrid MPI+OpenMP 13 • Practical “How-To” on hybrid programming 48 • Mismatch Problems 97 • Opportunities: Application categories that can 126 benefit from hybrid parallelization • Thread-safety quality of MPI libraries 136 10:30 – 12:00 • Tools for debugging and profiling MPI+OpenMP 143 • Other options on clusters of SMP nodes 150 • Summary 164 • Appendix 172 • Content (detailed) 188 Hybrid Parallel Programming Slide 2 / 169 Rabenseifner, Hager, Jost Motivation • Efficient programming of clusters of SMP nodes SMP nodes SMP nodes: cores shared • Dual/multi core CPUs memory • Multi CPU shared memory Node Interconnect • Multi CPU ccNUMA • Any mixture with shared memory programming model • Hardware range • mini-cluster with dual-core CPUs Core • … CPU(socket) • large constellations with large SMP nodes SMP board … with several sockets
    [Show full text]
  • Bright Cluster Manager User Manual
    Bright Cluster Manager 6.0 User Manual Revision: 3473 Date: Tue, 22 Jan 2013 Table of Contents Table of Contents . i 1 Introduction 1 1.1 What Is A Beowulf Cluster? . 1 1.2 Brief Network Description . 2 2 Cluster Usage 5 2.1 Login To The Cluster Environment . 5 2.2 Setting Up The User Environment . 6 2.3 Environment Modules . 6 2.4 Compiling Applications . 9 3 Using MPI 11 3.1 Interconnects . 11 3.2 Selecting An MPI implementation . 12 3.3 Example MPI Run . 12 4 Workload Management 17 4.1 What Is A Workload Manager? . 17 4.2 Why Use A Workload Manager? . 17 4.3 What Does A Workload Manager Do? . 17 4.4 Job Submission Process . 18 4.5 What Do Job Scripts Look Like? . 18 4.6 Running Jobs On A Workload Manager . 18 4.7 Running Jobs In Cluster Extension Cloud Nodes Using cmsub 19 5 SLURM 21 5.1 Loading SLURM Modules And Compiling The Executable 21 5.2 Running The Executable With salloc ............ 22 5.3 Running The Executable As A SLURM Job Script . 24 6 SGE 29 6.1 Writing A Job Script . 29 6.2 Submitting A Job . 33 6.3 Monitoring A Job . 34 6.4 Deleting A Job . 35 7 PBS Variants: Torque And PBS Pro 37 7.1 Components Of A Job Script . 38 7.2 Submitting A Job . 44 ii Table of Contents 8 Using GPUs 51 8.1 Packages . 51 8.2 Using CUDA . 51 8.3 Using OpenCL . 52 8.4 Compiling Code .
    [Show full text]
  • MPI Decades of Community and MPI Industry Experience in MXM Apis, Software Network Infrastructure Development of HPC Network Layers Optimizations Software
    Unified Communication X (UCX) UCF Consortium Project ISC 2019 UCF Consortium . Mission: • Collaboration between industry, laboratories, and academia to create production grade communication frameworks and open standards for data centric and high-performance applications . Projects • UCX – Unified Communication X • Open RDMA . Board members • Jeff Kuehn, UCF Chairman (Los Alamos National Laboratory) • Gilad Shainer, UCF President (Mellanox Technologies) • Pavel Shamis, UCF treasurer (ARM) • Brad Benton, Board Member (AMD) • Duncan Poole, Board Member (NVIDIA) • Pavan Balaji, Board Member (Argonne National Laboratory) • Sameh Sharkawi, Board Member (IBM) • Dhabaleswar K. (DK) Panda, Board Member (Ohio State University) • Steve Poole, Board Member (Open Source Software Solutions) © 2018 UCF Consortium 2 UCX - History A new project based on concept and ideas APIs, context, from multiple thread safety, generation of HPC etc. networks stack • Performance • Scalability Modular Architecture • Efficiency UCX • Portability UCCS PAMI Open APIs, Low-level optimizations LA MPI Decades of community and MPI industry experience in MXM APIs, software Network infrastructure development of HPC network Layers optimizations software 2000 2004 2010 2011 2012 © 2018 UCF Consortium 3 UCX High-level Overview © 2018 UCF Consortium 4 UCX Framework . UCX is a framework for network APIs and stacks . UCX aims to unify the different network APIs, protocols and implementations into a single framework that is portable, efficient and functional . UCX doesn’t focus on supporting
    [Show full text]
  • Open-MPI Over MOSIX Parallel Computing in a Clustered World
    Open-MPI over MOSIX Parallel Computing in a Clustered World ∗ y z Adam Lev-Libfeld, Supervised by: Amnon Barak and Alex Margolin School of Computer Science and Engineering The Hebrew University of Jerusalem, Israel September 14, 2009 Abstract Recent increased interest in Cloud computing emphasizes the need to find an adequate solution to the load-balancing problem in parallel comput- ing - efficiently running several jobs concurrently on a cluster of shared computers (nodes). One approach to solve this problem is by preemp- tive process migration- the transfer of running processes between nodes. A possible drawback of this approach is the increased overhead between heavily communicating processes. This project presents a solution to this last problem by incorporating the process migration capability of MOSIX into Open-MPI and by reducing the resulting communication overhead. Specifically, we developed a module for direct communication (DiCOM) between migrated Open-MPI processes, to overcome the increased com- munication latency of TCP/IP between such processes. The outcome is reduced run-time by improved resource allocation. This document is Open Source Secure: Written from scratch in Emacs on a Unix machine using LATEX version 2" ∗[email protected], ID 200226983, Corresponding author. [email protected] [email protected] To Dad Acknowledgments Special thanks to Amnon Shiloh for the extensive help in all MOSIX related issues. Thanks to Tal Maoz, Tal Ben-Nun and the System group for the moral and technical support. All graphs, graphics, diagrams and charts, as well as any other artworks in this document is the result of the original work of the author unless mentioned otherwise.
    [Show full text]
  • Writing MPI Programs for Odyssey
    Writing MPI Programs for Odyssey Teresa Kaltz, PhD Research Computing 1 FAS IT Research Computing • Provide compute resources to FAS and SEAS for research purposes • Leverage FAS IT infrastructure • Architect and manage RC resources • Support both shared and dedicated hardware • Also have expertise on staff – Domain level – Programming 2 What is Odyssey? • Generic name for RC resources is "odyssey" • This is the name of the original cluster – 4096 core Infiniband cluster – Originally listed as #60 on Top500! • Now it is just the alias for the login pool • There are many compute and storage resources available – 6000+ cores – PB+ storage 3 Using RC Resources • Users login to access node pool – odyssey.fas.harvard.edu • Compute resources accessed via LSF batch queuing system • Software environment controlled via modules • Ability to run parallel jobs – Many parallel applications installed on Odyssey – You can also run your own parallel code... so let's get programming! 4 What is parallel computing? • Doing calculations concurrently (“in parallel”) • Instruction level parallelism happens “automagically” – Intel Harpertown can execute 4 flops/tick • Thread and process level parallelism must be explicitly programmed – Some compilers offer autoparallelism features • Type of parallel computing available depends on compute infrastructure 5 Processing Element (PE) • Almost all CPU’s in Odyssey are multicore • Each core can execute instructions and is called a “processing element” in this presentation 6 Shared Memory Computer Architecture • PE’s
    [Show full text]
  • MPI-Based Approaches for Java
    Java for High Performance Computing MPI-based Approaches for Java http://www.hpjava.org/courses/arl Instructor: Bryan Carpenter Pervasive Technology Labs Indiana University [email protected] 1 MPI: The Message Passing Interface RMI originated in the Java world. Efforts like JavaParty and Manta aimed to bring RMI into the HPC world, by improving its performance. MPI is a technology from the HPC world, which various people have worked on importing into Java. – MPI is the HPC Message Passing Interface standardized in the early 1990s by the MPI Forum—a substantial consortium of vendors and researchers. – It is an API for communication between nodes of a distributed memory parallel computer (typically, now, a workstation cluster). – The original standard defines bindings to C and Fortran (later C++). – The low-level parts of API are oriented to: fast transfer of data from user program to network; supporting multiple modes of message synchronization available on HPC platforms; etc. – Higher level parts of the API are concerned with organization of process groups and providing the kind of collective communications seen in typical parallel applications. [email protected] 1 Features of MPI MPI (http://www-unix.mcs.anl.gov/mpi) is an API for sending and receiving messages. But it goes further than this. – It is essentially a general platform for Single Program Multiple Data (SPMD) parallel computing on distributed memory architectures. – In this respect it is directly comparable with the PVM (Parallel Virtual Machine) environment that was one of its precursors. It introduced the important abstraction of a communicator, which is an object something like an N-way communication channel, connecting all members of a group of cooperating processes.
    [Show full text]
  • An Openshmem Example
    Oak Ridge National Laboratory Computing and Computational Sciences Directorate Balancing Performance and Portability with Containers in HPC: An OpenSHMEM Example Thomas Naughton, Lawrence Sorrillo, Adam Simpson and Neena Imam Oak Ridge National Laboratory August 8, 2017 OpenSHMEM 2017 Workshop, Annapolis, MD, USA ORNL is managed by UT-Battelle for the US Department of Energy Introduction • Growing interest in methods to support user driven software customizations in an HPC environment – Leverage isolation mechanisms & container tools • Reasons – Improve productivity of users – Improve reproducibility of computational experiments Computational Research & UNCLASSIFIED // FOR OFFICIAL USE ONLY Development Programs Containers • What’s a Container? – “knobs” to tailor classic UNIX fork/exec model – Method for encapsulating an execution environment – Leverages operating system (OS) isolation mechanisms • Resource namespaces & control groups (cgroups) Example: Process gets “isolated” view of running processes (PIDs), and a control group that restricts it to 50% of CPU • Container Runtime – Coordinate OS mechanisms – Provide streamlined (simplified) access for user – Initialization of “container” & interfaces to attach/interact Computational Research & UNCLASSIFIED // FOR OFFICIAL USE ONLY Development Programs Container Motivations • Compute mobility – Build application with software stack and carry to compute – Customize execution environment to end-user preferences • Reproducibility – Run experiments with the same software & data – Archive experimental
    [Show full text]
  • (HPC) Cluster Opera Ng Sy
    Abstract Since 2001 we have used FreeBSD as a high performance compung (HPC) cluster operang system. In the process we have ported a number of HPC tools including Ganglia, Globus, Open MPI, and Sun Grid Engine. In this talk we will discuss the process of porng these types of applicaons and issues encountered while maintaining these tools. In addion to generally issues of porng code from one Unix‐like operang system to another, there are several type of porng common to many HPC infrastructure codes which we will explore. Beyond porng, we will discuss how the ports collecon aids our use of HPC applicaons and ways we think overall integraon could be improved. 1 At Aerospace we have designed, built, and operated a FreeBSD HPC cluster since 2001. This picture shows the Fellowship cluster in it’s current form with 352 dual processor nodes. In the process of building and running Fellowship we have ported a number of open source HPC tools to FreeBSD. In this talk I will discuss the process of porng them, issues we encountered, and a few pet peeves about applicaon portability. 2 Some of the tools we have ported to FreeBSD include Sun Grid Engine—also known as SGE—a batch job manager; the Ganglia monitoring system, a cluster/grid monitoring tool; and Open MPI a leading implementaon of the Message Passing Interfaces which is a toolkit for message based parallel programming. 3 The Ganglia monitoring systems, usually refered to as Ganglia, is a cluster and grid monitoring tool that provides current and historical data on the status of a cluster or collecon of clusters.
    [Show full text]