Setting up an Experimental Linux Based Cluster System
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Slurm Overview and Elasticsearch Plugin
Slurm Workload Manager Overview SC15 Alejandro Sanchez [email protected] Copyright 2015 SchedMD LLC http://www.schedmd.com Slurm Workload Manager Overview ● Originally intended as simple resource manager, but has evolved into sophisticated batch scheduler ● Able to satisfy scheduling requirements for major computer centers with use of optional plugins ● No single point of failure, backup daemons, fault-tolerant job options ● Highly scalable (3.1M core Tianhe-2 at NUDT) ● Highly portable (autoconf, extensive plugins for various environments) ● Open source (GPL v2) ● Operating on many of the world's largest computers ● About 500,000 lines of code today (plus test suite and documentation) Copyright 2015 SchedMD LLC http://www.schedmd.com Enterprise Architecture Copyright 2015 SchedMD LLC http://www.schedmd.com Architecture ● Kernel with core functions plus about 100 plugins to support various architectures and features ● Easily configured using building-block approach ● Easy to enhance for new architectures or features, typically just by adding new plugins Copyright 2015 SchedMD LLC http://www.schedmd.com Elasticsearch Plugin Copyright 2015 SchedMD LLC http://www.schedmd.com Scheduling Capabilities ● Fair-share scheduling with hierarchical bank accounts ● Preemptive and gang scheduling (time-slicing parallel jobs) ● Integrated with database for accounting and configuration ● Resource allocations optimized for topology ● Advanced resource reservations ● Manages resources across an enterprise Copyright 2015 SchedMD LLC http://www.schedmd.com -
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. -
Intel® SSF Reference Design: Intel® Xeon Phi™ Processor, Intel® OPA
Intel® Scalable System Framework Reference Design Intel® Scalable System Framework (Intel® SSF) Reference Design Cluster installation for systems based on Intel® Xeon® processor E5-2600 v4 family including Intel® Ethernet. Based on OpenHPC v1.1 Version 1.0 Intel® Scalable System Framework Reference Design Summary This Reference Design is part of the Intel® Scalable System Framework series of reference collateral. The Reference Design is a verified implementation example of a given reference architecture, complete with hardware and software Bill of Materials information and cluster configuration instructions. It can confidently be used “as is”, or be the foundation for enhancements and/or modifications. Additional Reference Designs are expected in the future to provide example solutions for existing reference architecture definitions and for utilizing additional Intel® SSF elements. Similarly, more Reference Designs are expected as new reference architecture definitions are introduced. This Reference Design is developed in support of the specification listed below using certain Intel® SSF elements: • Intel® Scalable System Framework Architecture Specification • Servers with Intel® Xeon® processor E5-2600 v4 family processors • Intel® Ethernet • Software stack based on OpenHPC v1.1 2 Version 1.0 Legal Notices No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade. This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. -
SUSE Linux Enterprise High Performance Computing 15 SP3 Administration Guide Administration Guide SUSE Linux Enterprise High Performance Computing 15 SP3
SUSE Linux Enterprise High Performance Computing 15 SP3 Administration Guide Administration Guide SUSE Linux Enterprise High Performance Computing 15 SP3 SUSE Linux Enterprise High Performance Computing Publication Date: September 24, 2021 SUSE LLC 1800 South Novell Place Provo, UT 84606 USA https://documentation.suse.com Copyright © 2020–2021 SUSE LLC and contributors. All rights reserved. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or (at your option) version 1.3; with the Invariant Section being this copyright notice and license. A copy of the license version 1.2 is included in the section entitled “GNU Free Documentation License”. For SUSE trademarks, see http://www.suse.com/company/legal/ . All third-party trademarks are the property of their respective owners. Trademark symbols (®, ™ etc.) denote trademarks of SUSE and its aliates. Asterisks (*) denote third-party trademarks. All information found in this book has been compiled with utmost attention to detail. However, this does not guarantee complete accuracy. Neither SUSE LLC, its aliates, the authors nor the translators shall be held liable for possible errors or the consequences thereof. Contents Preface vii 1 Available documentation vii 2 Giving feedback vii 3 Documentation conventions viii 4 Support x Support statement for SUSE Linux Enterprise High Performance Computing x • Technology previews xi 1 Introduction 1 1.1 Components provided 1 1.2 Hardware platform support 2 1.3 Support -
Version Control Graphical Interface for Open Ondemand
VERSION CONTROL GRAPHICAL INTERFACE FOR OPEN ONDEMAND by HUAN CHEN Submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Electrical Engineering and Computer Science CASE WESTERN RESERVE UNIVERSITY AUGUST 2018 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Huan Chen candidate for the degree of Master of Science Committee Chair Chris Fietkiewicz Committee Member Christian Zorman Committee Member Roger Bielefeld Date of Defense June 27, 2018 TABLE OF CONTENTS Abstract CHAPTER 1: INTRODUCTION ............................................................................ 1 CHAPTER 2: METHODS ...................................................................................... 4 2.1 Installation for Environments and Open OnDemand .............................................. 4 2.1.1 Install SLURM ................................................................................................. 4 2.1.1.1 Create User .................................................................................... 4 2.1.1.2 Install and Configure Munge ........................................................... 5 2.1.1.3 Install and Configure SLURM ......................................................... 6 2.1.1.4 Enable Accounting ......................................................................... 7 2.1.2 Install Open OnDemand .................................................................................. 9 2.2 Git Version Control for Open OnDemand -
Extending SLURM for Dynamic Resource-Aware Adaptive Batch
1 Extending SLURM for Dynamic Resource-Aware Adaptive Batch Scheduling Mohak Chadha∗, Jophin John†, Michael Gerndt‡ Chair of Computer Architecture and Parallel Systems, Technische Universitat¨ Munchen¨ Garching (near Munich), Germany Email: [email protected], [email protected], [email protected] their resource requirements at runtime due to varying computa- Abstract—With the growing constraints on power budget tional phases based upon refinement or coarsening of meshes. and increasing hardware failure rates, the operation of future A solution to overcome these challenges and efficiently utilize exascale systems faces several challenges. Towards this, resource awareness and adaptivity by enabling malleable jobs has been the system components is resource awareness and adaptivity. actively researched in the HPC community. Malleable jobs can A method to achieve adaptivity in HPC systems is by enabling change their computing resources at runtime and can signifi- malleable jobs. cantly improve HPC system performance. However, due to the A job is said to be malleable if it can adapt to resource rigid nature of popular parallel programming paradigms such changes triggered by the batch system at runtime [6]. These as MPI and lack of support for dynamic resource management in batch systems, malleable jobs have been largely unrealized. In resource changes can either increase (expand operation) or this paper, we extend the SLURM batch system to support the reduce (shrink operation) the number of processors. Malleable execution and batch scheduling of malleable jobs. The malleable jobs have shown to significantly improve the performance of a applications are written using a new adaptive parallel paradigm batch system in terms of both system and user-centric metrics called Invasive MPI which extends the MPI standard to support such as system utilization and average response time [7], resource-adaptivity at runtime. -
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. -
Distributed HPC Applications with Unprivileged Containers Felix Abecassis, Jonathan Calmels GPUNVIDIA Computing Beyond Video Games
Distributed HPC Applications with Unprivileged Containers Felix Abecassis, Jonathan Calmels GPUNVIDIA Computing Beyond video games VIRTUAL REALITY SCIENTIFIC COMPUTING MACHINE LEARNING AUTONOMOUS MACHINES GPU COMPUTING 2 Infrastructure at NVIDIA DGX SuperPOD GPU cluster (Top500 #20) x96 3 NVIDIA Containers Supports all major container runtimes We built libnvidia-container to make it easy to run CUDA applications inside containers We release optimized container images for each of the major Deep Learning frameworks every month We use containers for everything on our HPC clusters - R&D, official benchmarks, etc Containers give us portable software stacks without sacrificing performance 4 Typical cloud deployment e.g. Kubernetes Hundreds/thousands of small nodes All applications are containerized, for security reasons Many small applications running per node (e.g. microservices) Traffic to/from the outside world Not used for interactive applications or development Advanced features: rolling updates with rollback, load balancing, service discovery 5 GPU Computing at NVIDIA HPC-like 10-100 very large nodes “Trusted” users Not all applications are containerized Few applications per node (often just a single one) Large multi-node jobs with checkpointing (e.g. Deep Learning training) Little traffic to the outside world, or air-gapped Internal traffic is mostly RDMA 6 Slurm Workload Manager https://slurm.schedmd.com/slurm.html Advanced scheduling algorithms (fair-share, backfill, preemption, hierarchical quotas) Gang scheduling: scheduling and -
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. -
Cluster Deployment
INAF-OATs Technical Report 222 - INCAS: INtensive Clustered ARM SoC - Cluster Deployment S. Bertoccoa, D. Goza, L. Tornatorea, G. Taffonia aINAF-Osservatorio Astronomico di Trieste, Via G. Tiepolo 11, 34131 Trieste - Italy Abstract The report describes the work done to deploy an Heterogeneous Computing Cluster using CPUs and GPUs integrated on a System on Chip architecture, in the environment of the ExaNeSt H2020 project. After deployment, this cluster will provide a testbed to validate and optimize several HPC codes developed to be run on heterogeneous hardware. The work done includes: a technology watching phase to choose the SoC best fitting our requirements; installation of the Operative System Ubuntu 16.04 on the choosen hardware; cluster layout and network configuration; software installation (cluster management software slurm, specific software for HPC programming). Keywords: SoC, Firefly-RK3399, Heterogeneous Cluster Technology HCT, slurm, MPI, OpenCL, ARM 1. Introduction The ExaNeSt H2020 project aims at the design and development of an exascale ready supercomputer with a low energy consumption profile but able to support the most demanding scientific and technical applica- tions. The project will produce a prototype based on hybrid hardware (CPUs+accelerators). Astrophysical codes are playing a fundamental role to validate this exascale platform. A preliminary work has been done to Email addresses: [email protected] ORCID:0000-0003-2386-623X (S. Bertocco), [email protected] ORCID:0000-0001-9808-2283 (D. Goz), [email protected] ORCID:0000-0003-1751-0130 (L. Tornatore), [email protected] ORCID:0000-0001-7011-4589 (G. Taffoni) Preprint submitted to INAF August 3, 2018 port on the heterogeneous platform a state-of-the-art N-body code (called ExaHiGPUs) [4]. -
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. -
Sansa - the Supercomputer and Node State Architecture
SaNSA - the Supercomputer and Node State Architecture Neil Agarwal∗y, Hugh Greenberg∗, Sean Blanchard∗, and Nathan DeBardeleben∗ ∗ Ultrascale Systems Research Center Los Alamos National Laboratory High Performance Computing Los Alamos, New Mexico, 87545 Email: fhng, seanb, [email protected] y University of California, Berkeley Berkeley, California, 94720 Email: [email protected] Abstract—In this work we present SaNSA, the Supercomputer vendor, today there exists relatively poor fine-grained informa- and Node State Architecture, a software infrastructure for histor- tion about the state of individual nodes of a cluster. Operators ical analysis and anomaly detection. SaNSA consumes data from and technicians of data centers have dashboards which actively multiple sources including system logs, the resource manager, scheduler, and job logs. Furthermore, additional context such provide real time feedback about coarse-grained information as scheduled maintenance events or dedicated application run such as whether a node is up or down. times for specific science teams can be overlaid. We discuss how The Slurm Workload Manager, which runs on most of the this contextual information allows for more nuanced analysis. U.S. Department of Energy’s clusters, has one view of the SaNSA allows the user to apply arbitrary attributes, for instance, state of the nodes in the system. Slurm uses this to know positional information where nodes are located in a data center. We show how using this information we identify anomalous whether nodes are available for scheduling, jobs are running, behavior of one rack of a 1,500 node cluster. We explain the and when they complete. However, there are other views such design of SaNSA and then test it on four open compute clusters as the node’s own view as expressed via system logs it reports at LANL.