Open-MPI Over MOSIX Parallel Computing in a Clustered World
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Clustering with Openmosix
Clustering with openMosix Maurizio Davini (Department of Physics and INFN Pisa) Presented by Enrico Mazzoni (INFN Pisa) Introduction • What is openMosix? – Single-System Image – Preemptive Process Migration – The openMosix File System (MFS) • Application Fields • openMosix vs Beowulf • The people behind openMosix • The openMosix GNU project • Fork of openMosix code 12/06/2003 HTASC 2 The openMosix Project MileStones • Born early 80s on PDP-11/70. One full PDP and disk-less PDP, therefore process migration idea. • First implementation on BSD/pdp as MS.c thesis. • VAX 11/780 implementation (different word size, different memory architecture) • Motorola / VME bus implementation as Ph.D. thesis in 1993 for under contract from IDF (Israeli Defence Forces) • 1994 BSDi version • GNU and Linux since 1997 • Contributed dozens of patches to the standard Linux kernel • Split Mosix / openMosix November 2001 • Mosix standard in Linux 2.5? 12/06/2003 HTASC 3 What is openMOSIX • Linux kernel extension (2.4.20) for clustering • Single System Image - like an SMP, for: – No need to modify applications – Adaptive resource management to dynamic load characteristics (CPU intensive, RAM intensive, I/O etc.) – Linear scalability (unlike SMP) 12/06/2003 HTASC 4 A two tier technology 1. Information gathering and dissemination – Support scalable configurations by probabilistic dissemination algorithms – Same overhead for 16 nodes or 2056 nodes 2. Pre-emptive process migration that can migrate any process, anywhere, anytime - transparently – Supervised by adaptive -
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. -
Load Balancing Experiments in Openmosix”, Inter- National Conference on Computers and Their Appli- Cations , Seattle WA, March 2006 0 0 5 4 9
Machine Learning Approach to Tuning Distributed Operating System Load Balancing Algorithms Dr. J. Michael Meehan Alan Ritter Computer Science Department Computer Science Department Western Washington University Western Washington University Bellingham, Washington, 98225 USA Bellingham, Washington, 98225 USA [email protected] [email protected] Abstract 2.6 Linux kernel openMOSIX patch was in the process of being developed. This work concerns the use of machine learning techniques (genetic algorithms) to optimize load 2.1 Load Balancing in openMosix balancing policies in the openMosix distributed The standard load balancing policy for openMosix operating system. Parameters/alternative algorithms uses a probabilistic, decentralized approach to in the openMosix kernel were dynamically disseminate load balancing information to other altered/selected based on the results of a genetic nodes in the cluster. [4] This allows load information algorithm fitness function. In this fashion optimal to be distributed efficiently while providing excellent parameter settings and algorithms choices were scalability. The major components of the openMosix sought for the loading scenarios used as the test load balancing scheme are the information cases. dissemination and migration kernel daemons. The information dissemination daemon runs on each 1 Introduction node and is responsible for sending and receiving The objective of this work is to discover ways to load messages to/from other nodes. The migration improve the openMosix load balancing algorithm. daemon receives migration requests from other The approach we have taken is to create entries in nodes and, if willing, carries out the migration of the the /proc files which can be used to set parameter process. Thus, the system is sender initiated to values and select alternative algorithms used in the offload excess load. -
HPC with Openmosix
HPC with openMosix Ninan Sajeeth Philip St. Thomas College Kozhencheri IMSc -Jan 2005 [email protected] Acknowledgements ● This document uses slides and image clippings available on the web and in books on HPC. Credit is due to their original designers! IMSc -Jan 2005 [email protected] Overview ● Mosix to openMosix ● Why openMosix? ● Design Concepts ● Advantages ● Limitations IMSc -Jan 2005 [email protected] The Scenario ● We have MPI and it's pretty cool, then why we need another solution? ● Well, MPI is a specification for cluster communication and is not a solution. ● Two types of bottlenecks exists in HPC - hardware and software (OS) level. IMSc -Jan 2005 [email protected] Hardware limitations for HPC IMSc -Jan 2005 [email protected] The Scenario ● We are approaching the speed and size limits of the electronics ● Major share of possible optimization remains with software part - OS level IMSc -Jan 2005 [email protected] Hardware limitations for HPC IMSc -Jan 2005 [email protected] How Clusters Work? Conventional supe rcomputers achieve their speed using extremely optimized hardware that operates at very high speed. Then, how do the clusters out-perform them? Simple, they cheat. While the supercomputer is optimized in hardware, the cluster is so in software. The cluster breaks down a problem in a special way so that it can distribute all the little pieces to its constituents. That way the overall problem gets solved very efficiently. - A Brief Introduction To Commodity Clustering Ryan Kaulakis IMSc -Jan 2005 [email protected] What is MOSIX? ● MOSIX is a software solution to minimise OS level bottlenecks - originally designed to improve performance of MPI and PVM on cluster systems http://www.mosix.org Not open source Free for personal and academic use IMSc -Jan 2005 [email protected] MOSIX More Technically speaking: ● MOSIX is a Single System Image (SSI) cluster that allows Automated Load Balancing across nodes through preemptive process migrations. -
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. -
Introduction Course Outline Why Does This Fail? Lectures Tutorials
Course Outline • Prerequisites – COMP2011 Data Organisation • Stacks, queues, hash tables, lists, trees, heaps,…. Introduction – COMP2021 Digital Systems Structure • Assembly programming • Mapping of high-level procedural language to assembly COMP3231/9201/3891/9283 language – or the postgraduate equivalent (Extended) Operating Systems – You are expected to be competent Kevin Elphinstone programmers!!!! • We will be using the C programming language – The dominant language for OS implementation. – Need to understand pointers, pointer arithmetic, explicit 2 memory allocation. Why does this fail? Lectures • Common for all courses (3231/3891/9201/9283) void func(int *x, int *y) • Wednesday, 2-4pm { • Thursday, 5-6pm *x = 1; *y = 2; – All lectures are here (EE LG03) – The lecture notes will be available on the course web site } • Available prior to lectures, when possible. void main() – The lecture notes and textbook are NOT a substitute for { attending lectures. int *a, *b; func(a,b); } 3 4 Tutorials Assignments • Assignments form a substantial component of • Start in week 2 your assessment. • A tutorial participation mark will • They are challenging!!!! – Because operating systems are challenging contribute to your final assessment. • We will be using OS/161, – Participation means participation, NOT – an educational operating system attendance. – developed by the Systems Group At Harvard – Comp9201/3891/9283 students excluded – It contains roughly 20,000 lines of code and comments • You will only get participation marks in your enrolled tutorial. 5 6 1 Assignments Assignments • Assignments are in pairs • Don’t under estimate the time needed to do the – Info on how to pair up available soon assignments. • We usually offer advanced versions of the – ProfQuotes: [About the midterm] "We can't keep you working assignments on it all night, it's not OS.“ Ragde, CS341 – Available bonus marks are small compared to amount of • If you start a couple days before they are due, you will be late. -
The Utility Coprocessor: Massively Parallel Computation from the Coffee Shop
The Utility Coprocessor: Massively Parallel Computation from the Coffee Shop John R. Douceur, Jeremy Elson, Jon Howell, and Jacob R. Lorch Microsoft Research Abstract slow jobs that take several minutes without UCop be- UCop, the “utility coprocessor,” is middleware that come interactive (15–20 seconds) with it. Thanks to makes it cheap and easy to achieve dramatic speedups the recent emergence of utility-computing services like of parallelizable, CPU-bound desktop applications using Amazon EC2 [8] and FlexiScale [45], which rent com- utility computing clusters in the cloud. To make UCop puters by the hour on a moment’s notice, anyone with a performant, we introduced techniques to overcome the credit card and $10 can use UCop to speed up his own low available bandwidth and high latency typical of the parallel applications. networks that separate users’ desktops from a utility One way to describe UCop is that it effectively con- computing service. To make UCop economical and easy verts application software into a scalable cloud service to use, we devised a scheme that hides the heterogene- targeted at exactly one user. This goal entails five re- ity of client configurations, allowing a single cluster to quirements. Configuration transparency means the ser- serve virtually everyone: in our Linux-based prototype, vice matches the user’s application, library, and con- the only requirement is that users and the cluster are us- figuration state. Non-invasive installation means UCop ing the same major kernel version. works with a user’s existing file system and application This paper presents the design, implementation, and configuration. -
Legoos: a Disseminated, Distributed OS for Hardware Resource
LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation Yizhou Shan, Yutong Huang, Yilun Chen, and Yiying Zhang, Purdue University https://www.usenix.org/conference/osdi18/presentation/shan This paper is included in the Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18). October 8–10, 2018 • Carlsbad, CA, USA ISBN 978-1-939133-08-3 Open access to the Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX. LegoOS: A Disseminated, Distributed OS for Hardware Resource Disaggregation Yizhou Shan, Yutong Huang, Yilun Chen, Yiying Zhang Purdue University Abstract that can fit into monolithic servers and deploying them in datacenters is a painful and cost-ineffective process that The monolithic server model where a server is the unit often limits the speed of new hardware adoption. of deployment, operation, and failure is meeting its lim- We believe that datacenters should break mono- its in the face of several recent hardware and application lithic servers and organize hardware devices like CPU, trends. To improve resource utilization, elasticity, het- DRAM, and disks as independent, failure-isolated, erogeneity, and failure handling in datacenters, we be- network-attached components, each having its own con- lieve that datacenters should break monolithic servers troller to manage its hardware. This hardware re- into disaggregated, network-attached hardware compo- source disaggregation architecture is enabled by recent nents. Despite the promising benefits of hardware re- advances in network technologies [24, 42, 52, 66, 81, 88] source disaggregation, no existing OSes or software sys- and the trend towards increasing processing power in tems can properly manage it. -
Architectural Review of Load Balancing Single System Image
Journal of Computer Science 4 (9): 752-761, 2008 ISSN 1549-3636 © 2008 Science Publications Architectural Review of Load Balancing Single System Image Bestoun S. Ahmed, Khairulmizam Samsudin and Abdul Rahman Ramli Department of Computer and Communication Systems Engineering, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia Abstract: Problem statement: With the growing popularity of clustering application combined with apparent usability, the single system image is in the limelight and actively studied as an alternative solution for computational intensive applications as well as the platform for next evolutionary grid computing era. Approach: Existing researches in this field concentrated on various features of Single System Images like file system and memory management. However, an important design consideration for this environment is load allocation and balancing that is usually handled by an automatic process migration daemon. Literature shows that the design concepts and factors that affect the load balancing feature in an SSI system are not clear. Result: This study will review some of the most popular architecture and algorithms used in load balancing single system image. Various implementations from the past to present will be presented while focusing on the factors that affect the performance of such system. Conclusion: The study showed that although there are some successful open source systems, the wide range of implemented systems investigated that research activity should concentrate on the systems that have already been proposed and proved effectiveness to achieve a high quality load balancing system. Key words: Single system image, NOWs (network of workstations), load balancing algorithm, distributed systems, openMosix, MOSIX INTRODUCTION resources transparently irrespective of where they are available[1].The load balancing single system image Cluster of computers has become an efficient clusters dominate research work in this environment. -
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. -
SEMINARIS DOCENTS DE CASO 03/04 - 2Q NÚM GRUP: Facultat D´Informàtica De Barcelona - Departament AC - UPC
SEMINARIS DOCENTS DE CASO 03/04 - 2Q NÚM GRUP: Facultat d´Informàtica de Barcelona - Departament AC - UPC Fitxa corresponent al treball sobre un tema d´actualitat dins del tema 6 del curs. Aquest treball també portarà associada una presentació obligatoria del mateix (d’uns 10’) i forma part de la nota NS tal com es va indicar a principi de curs. Aquest treball es pot fer en grups d’1, 2 o 3 persones. Cada grup serà responsable de supervisar l’adequació de la presentació d’un altre grup. El fet de lliurar aquest treball implica l´acceptació de fer-ne una presentació d’un màxim de 10’ a classe a partir de les transparències powerpoint que podran ser distribuïdes com a documentació docent als alumnes de CASO i formar part dels coneixements evaluables de l´assignatura . ELECCIÓ TREBALL: Cal que envieu un mail (amb el format especificat a l´avís del Racó) amb títol de la presentació, els noms, preferència matí/tardai breu descripció a [email protected] . Tot això abans del dimarts 18 de Maig 2004. Després d´aquesta data ja no s'acceptaran. LLIURAMENT: El termini de lliurament dels treballs és fins diumenge 23 de Maig 2004. Els treballs es lliuraran per correu elect rònic a l´adreça [email protected] preferiblement en format PDF. Tamb é es pot enviar en powerpoint usant la plantilla que podeu trobar a http://docencia.ac.upc.es/FIB/CASO/seminaris/TransSEMcaso.ppt o OpenOffice. Tot treball no lliurat dins del termini establert es considerarà no lliurat. PRESENTACIONS ANTERIORS: http://docencia.ac.upc.es/FIB/CASO/seminaris/SeminarisCASO.htm MEMBRES -
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.