Openhpc (V2.0) Cluster Building Recipes
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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 -
Literature Review and Implementation Overview: High Performance
LITERATURE REVIEW AND IMPLEMENTATION OVERVIEW: HIGH PERFORMANCE COMPUTING WITH GRAPHICS PROCESSING UNITS FOR CLASSROOM AND RESEARCH USE APREPRINT Nathan George Department of Data Sciences Regis University Denver, CO 80221 [email protected] May 18, 2020 ABSTRACT In this report, I discuss the history and current state of GPU HPC systems. Although high-power GPUs have only existed a short time, they have found rapid adoption in deep learning applications. I also discuss an implementation of a commodity-hardware NVIDIA GPU HPC cluster for deep learning research and academic teaching use. Keywords GPU · Neural Networks · Deep Learning · HPC 1 Introduction, Background, and GPU HPC History High performance computing (HPC) is typically characterized by large amounts of memory and processing power. HPC, sometimes also called supercomputing, has been around since the 1960s with the introduction of the CDC STAR-100, and continues to push the limits of computing power and capabilities for large-scale problems [1, 2]. However, use of graphics processing unit (GPU) in HPC supercomputers has only started in the mid to late 2000s [3, 4]. Although graphics processing chips have been around since the 1970s, GPUs were not widely used for computations until the 2000s. During the early 2000s, GPU clusters began to appear for HPC applications. Most of these clusters were designed to run large calculations requiring vast computing power, and many clusters are still designed for that purpose [5]. GPUs have been increasingly used for computations due to their commodification, following Moore’s Law (demonstrated arXiv:2005.07598v1 [cs.DC] 13 May 2020 in Figure 1), and usage in specific applications like neural networks. -
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. -
Arm HPC Software Stack and HPC Tools
Arm HPC Software Stack and HPC Tools Centre for Development of Advanced Computing (C-DAC) / National Supercomputing Mission (NSM) Arm in HPC Course Phil Ridley [email protected] 2nd March 2021 © 2021 Arm Agenda • HPC Software Ecosystem • Overview • Applications • Community Support • Compilers, Libraries and Tools • Open-source and Commercial – Compilers – Maths Libraries • Profiling and Debugging 2 © 2021 Arm Software Ecosystem © 2021 Arm HPC Ecosystem Applications Performance Open-source, owned, coMMercial ISV codes, … Engineering … PBS Pro, Altair LSF, IBM SLURM, ArM Forge (DDT, MAP), Rogue Wave, HPC Toolkit, Schedulers Containers, Interpreters, etc. Management Cluster Scalasca, VaMpir, TAU, … Singularity, PodMan, Docker, Python, … HPE CMU, Bright, Middleware Mellanox IB/OFED/HPC-X, OpenMPI, MPICH, MVAPICH2, OpenSHMEM, OpenUCX, HPE MPI xCat Compilers Libraries Filesystems , Warewulf OEM/ODM’s ArM, GNU, LLVM, Clang, Flang, ArMPL, FFTW, OpenBLAS, BeeGFS, Lustre, ZFS, Cray-HPE, ATOS-Bull, Cray, PGI/NVIDIA, Fujitsu, … NuMPy, SciPy, Trilinos, PETSc, HDF5, NetCDF, GPFS, … Fujitsu, Gigabyte, … Hypre, SuperLU, ScaLAPACK, … , … Silicon OS RHEL, SUSE, CentOS, Ubuntu, … Suppliers Marvell, Fujitsu, Arm Server Ready Platform Mellanox, NVIDIA, … Standard firMware and RAS 4 © 2021 Arm General Software Ecosystem Workloads Drone.io Language & Library CI/CD Container & Virtualization Operating System Networking 5 © 2021 Arm – Easy HPC stack deployment on Arm https://developer.arm.com/solutions/hpc/hpc-software/openhpc Functional Areas Components include OpenHPC is a community effort to provide a Base OS CentOS 8.0, OpenSUSE Leap 15 common, verified set of open source packages for Administrative Tools Conman, Ganglia, Lmod, LosF, Nagios, pdsh, pdsh-mod- slurm, prun, EasyBuild, ClusterShell, mrsh, Genders, HPC deployments Shine, test-suite Arm and partners actively involved: Provisioning Warewulf Resource Mgmt. -
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 -
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]. -
Schedule: Sunday
Schedule: Sunday page 2 Main tracks and lightning talks page 3 Devrooms in AW building page 4-5 Devrooms in H building page 5-6 Devrooms in K building page 6-7 Devrooms in U building Latest schedule and mobile apps on https://fosdem.org/schedule/ Compiled on 26/01/2016, see fosdem.org for updates. Page 1 of 12, Sunday Main tracks Main tracks Lightning Talks J.Janson K.1.105 (La Fontaine) H.2215 (Ferrer) 10:00 A New Patchwork Stephen Finucane 10:15 Re-thinking Linux Distributions Free communications with Free Software Buildtime Trend : visualise what’s trending in 10:30 Langdon White Daniel Pocock your build process – Dieter Adriaenssens Learning about software development with 10:45 Kibana dashboards – Jesus M. Gonzalez- Barahona 11:00 coala – Code Analysis Made Simple Lasse Schuirmann 11:15 Building a peer-to-peer network for Real-Time Beyond reproducible builds Communication How choosing the Raft consensus algorithm 11:30 Holger Levsen saved us 3 months of development time – Adrien Béraud, Guillaume Roguez Robert Wojciechowski Keeping your files safe in the post-Snowden 11:45 era with SXFS – Robert Wojciechowski 12:00 Spiffing – Military grade security Dave Cridland 12:15 illumos at 5 Mainflux Layers Box 12:30 Dan McDonald Drasko Draskovic Istvàn Koren FAI – The Universal Installation Tool 12:45 Thomas Lange 13:00 Knot DNS Resolver Ondrejˇ Surý 13:15 RocksDB Storage Engine for MySQL How containers work in Linux Prometheus – A Next Generation Monitoring 13:30 Yoshinori Matsunobu James Bottomley System Brian Brazil Going cross-platform – how htop was made 13:45 portable Hisham Muhammad 14:00 Ralph – Data Center Asset Management Sys- tem and DCIM, 100% Open Source. -
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. -
Intelligent Colocation of HPC Workloads?,??
Intelligent colocation of HPC workloads?;?? Felippe Vieira Zacariasa,b,c, Vinicius Petruccia,e, Rajiv Nishtalad, Paul Carpenterc, Daniel Mosse´e aUniversidade Federal da Bahia bUniversitat Polit`ecnicade Catalunya cBarcelona Supercomputing Center dCoop, Norway/Norwegian University of Science and Technology, Norway eUniversity of Pittsburgh Abstract Many HPC applications suffer from a bottleneck in the shared caches, instruction execution units, I/O or memory bandwidth, even though the remaining resources may be underutilized. It is hard for developers and runtime systems to ensure that all critical resources are fully exploited by a single application, so an attractive technique for increasing HPC system utilization is to colocate multiple applications on the same server. When applications share critical resources, however, contention on shared resources may lead to reduced application performance. In this paper, we show that server efficiency can be improved by first modeling the expected performance degradation of colocated applications based on measured hardware performance counters, and then exploiting the model to determine an optimized mix of colocated applications. This paper presents a new intelligent resource manager and makes the following contributions: (1) a new machine learning model to predict the performance degradation of colocated applications based on hardware counters and (2) an intelligent scheduling scheme deployed on an existing resource manager to enable application co-scheduling with minimum performance degradation. Our results show that our approach achieves performance improvements of 7 % (avg) and 12 % (max) compared to the standard policy commonly used by existing job managers. Keywords: Resource manager, HPC systems, Machine learning, Colocation, Performance Characterization, Performance counters 1. Introduction is key to keeping up with increased computational demands.