Linux clustering is popular in many industries these days. With the advent of clustering technology and the growing acceptance of open source software, supercomputers can now be created for a fraction of the cost of traditional high-performance machines.
This two-part article introduces the concepts of High Performance Computing HPC with Linux cluster technology and shows you how to set up clusters and write parallel programs. This part introduces the different types of clusters, uses of clusters, some fundamentals of HPC, the role of Linux, and the reasons for the growth of clustering technology.Ceph Intro \u0026 Architectural Overview
Part 2 covers parallel algorithms, how to write parallel programs, how to set up clusters, and cluster benchmarking. Most HPC systems use the concept of parallelism. In vector processors, the CPU is optimized to perform well with arrays or vectors; hence the name. Vector processor systems deliver high performance and were the dominant HPC architecture in the s and early s, but clusters have become far more popular in recent years.
These days it is common to use a commodity workstation running Linux and other open source software as a node in a cluster. This article focuses on three types of clusters:. The simplest fail-over cluster has two nodes: one stays active and the other stays on stand-by but constantly monitors the active one. In case the active node goes down, the stand-by node takes over, allowing a mission-critical system to continue functioning. Load-balancing clusters are commonly used for busy Web sites where several nodes host the same site, and each new request for a Web page is dynamically routed to a node with a lower load.
These clusters are used to run parallel programs for time-intensive computations and are of special interest to the scientific community. They commonly run simulations and other CPU-intensive programs that would take an inordinate amount of time to run on regular hardware.
Figure 1 illustrates a basic cluster. Part 2 of this series shows you how to create such a cluster and write programs for it. Grid computing is a broad term that typically refers to a service-oriented architecture SOA with collaboration among loosely coupled systems. Cluster-based HPC is a special case of grid computing in which the nodes are tightly coupled.
A successful, well-known project in grid computing is SETI home, the Search for Extraterrestrial Intelligence program, which used the idle CPU cycles of a million home PCs via screen savers to analyze radio telescope data.
A similar successful project is the Folding Home project for protein-folding calculations. Almost every industry needs fast processing power.
With the increasing availability of cheaper and faster computers, more companies are interested in reaping the technological benefits. Proteins molecules are long flexible chains that can take on a virtually infinite number of 3D shapes.
One way scientists try to understand protein folding is by simulating it on computers. In nature, folding occurs quickly in about one millionth of a secondbut it is so complex that its simulation on a regular computer could take decades. This focus area is a small one in an industry with many more such areas, but it needs serious computational power. Other focus areas from this industry include pharmaceutical modeling, virtual surgery training, condition and diagnosis visualizations, total-record medical databases, and the Human Genome Project.
Seismograms convey detailed information about the characteristics of the interior of the Earth and seafloors, and an analysis of the data helps in detecting oil and other resources.
Terabytes of data are needed to reconstruct even small areas; this analysis obviously needs a lot of computational power. The demand for computational power in this field is so great that supercomputing resources are often leased for this work.Linux Clusters Overview.
Proposal from California Digital Corporationa small local company, was accepted. Peak performance was For more information about Thunder see: "Using Thunder" tutorial - computing. Appro www. Peloton clusters were built in 5. Atlas Peloton Cluster. Read the announcement HERE. The primary difference is that TLCC clusters are quad-core instead of dual-core. Frames - TLCC2. Frames - CTS Intel Broadwell Click for larger image. Image source: Intel. QLogic 1st and 2nd Stage Switches back.
This section only provides a summary of the software and development environment for LC's Linux clusters. Please see the Introduction to LC Resources tutorial for details. List available modules: module avail Load a module: module add load modulefile Unload a module: module rm unload modulefile List loaded modules: module list Read module help info: module Display module contents: module display show modulefile. Reduce code size and execution time, without performing any optimizations that take a great deal of compilation time.
Optimize even more. Note: you may need to load a module for the desired MPI implementation, as discussed previously. Failing to do this will result in getting the default implementation. Designated for serial and single node parallel jobs only.
A job cannot span more than one node. Compute nodes are NOT shared with other users or jobs. When your job runs, the allocated nodes are dedicated to your job alone. Multiple users and their jobs can run on the same node simultaneously. Can result in competition for resources such as CPU and memory.
This section only provides a quick summary of batch usage on LC's clusters.Shop now. Linux clusters have escaped. As the general popularity of the Linux operating system increases, more complex solutions built with it are becoming increasingly common in the "traditional" more conservative IT world.
Linux computer clusters, whose provenance was originally universities and research institutions such as the U. National Laboratories, are showing up in increasing numbers as high-performance computing solutions within such areas as oil and gas exploration, computer-aided engineering, visualization, and software development.
Linux clusters providing highly-available web, mail, and other infrastructure services are also increasingly common. If past computing history is any indicator of future trends, widespread use of Linux clusters in the mainstream IT world cannot be far behind. But we need to ask the question, "Is mainstream IT or my organization ready for Linux clusters? Anyone who tells you differently is selling something. How can you make sure that a cluster experience is successful?
One good way is to walk into the cluster-building project with open eyes and realistic expectations, knowing the whywhatand how of the project. Before you start, have a good reason for building the cluster whyunderstand the integration of the required hardware and software components whatand apply good design and planning practices how that can minimize issues with your cluster. We must avoid what I call pile o ' hardware syndrome—the mistaken belief that buying all of the necessary components for a cluster and piling them on the floor in the computer room will spontaneously and miraculously generate a functional cluster.
Hope is not a strategy. See All Related Store Items. By Robert W. Lucke Mar 4, Why Linux for Clusters? In this three-part series, Rob Lucke attacks the why, what, and how of building a Linux cluster. He begins by showing how cluster computing can save bunches of money while simultaneously providing more power.
Tim Taylor would be very proud. Like this article? We recommend. We recommend Like this article? Related Resources Store Articles. Join Sign In. All rights reserved.The goal of the TOSS project has been to increase efficiencies in the ASC tri-lab community with respect to both the utility and the cost of a common computing environment.
TOSS provides a complete product with full lifecycle support. Since their deployment, the TLCC2 clusters have proven to be some of the most scalable, reliable, and cost-effective clusters that LLNL has ever brought into service. Users can run larger simulations with a higher job throughput than was previously possible on commodity systems. Delivery of the third generation of tri-lab clustersreferred to as Commodity Technology Systems, began in April By leveraging hardware and software designed for mass consumer markets, commodity systems can be constructed at a fraction of the cost of systems designed specifically for the much smaller HPC market.
One challenge tri-lab computer scientists have been tackling is degraded application performance due to resource contention with system management software. The objective of current efforts is to identify and reduce the interference or noise introduced by the system software on commodity clusters. By characterizing the major sources of noise and applying novel techniques to eliminate them or reduce their effect, these researchers aim to improve application performance and scalability.
Skip to main content. Lawrence Livermore National Laboratory.
Search form Search. Developing a standardized and optimized operating system and software to deploy across a series of Linux clusters. Facebook Twitter LinkedIn. Back to top Skip to navigation Skip to main content Skip to search.Under the CTS-1 contract, Penguin Computing—a Silicon Valley—based developer of high-performance Linux cluster computing systems—has furnished the labs with multiple systems, ranging in size from a few hundred to several thousand nodes.
These commodity technology systems are designed to run a large number of jobs simultaneously on a single system. This tri-lab procurement model reduces costs through economies of scale based on standardized hardware and software environments at the three labs. Delivery of the systems procurement began in April and will continue through at least Each system is built of scalable units SUsand each SU represents approximately teraflops of computing power.
This approach provides a flexible arrangement such that a vendor can seamlessly deliver both large and small machines. The new computing clusters run multiple jobs faster than previous commodity systems, in line with industry trends toward better throughput rather than faster per-core processing unit performance. Other features of the new machines are virtually invisible to users. They can be cooled with either water or air and have an option to use higher voltage power for greater power efficiency than past clusters.
Rather than having one power supply for each node as in many past clusters, the Penguin systems supply a whole cluster rack with one large power shelf.
Such systems enable investigations into technical issues related to aging weapons systems. Skip to main content. Lawrence Livermore National Laboratory. Search form Search. CTS-1 has delivered a two- to four-fold improvement in different performance areas over our past commodity systems. Matt Leininger, Livermore Computing. Facebook Twitter LinkedIn.
Linux Clusters Overview
Commodity Clusters. Back to top Skip to navigation Skip to main content Skip to search.Depending on the vendor and specific contexts, a cluster may refer to wide a variety of environments. In general a cluster refers to a set of computer systems connected together. For the purposes of this book a cluster is set of computers which are connected to each other, and are physically located close to each other, in order to solve problems more efficiently.
Another popular usage of the term cluster is to describe High Availability environments.
In this environment a computer system acts as the backup system to one or more primary systems. When there is a failure in a primary system, the critical applications running on that system are failed over to its designated backup system. Detailed usage and technology behind these types of clusters is outside the scope of this book. Nevertheless, we will touch upon specific usage of high availability technology within the context of compute clusters.
Clusters are becoming increasingly popular as computational resources both in research and commercial organizations. These organizations are favoring clusters over single large servers We will be referring to the set of processing elements within the boundaries of single instantiation of an operating system as comprising a single server or a shared-memory system for a wide variety of problems.
The increasing popularity of the Linux operating system is adding fuel to this fire. Linux provides a very cost effective and open environment to build a cluster. Since its conception in the early nineties, Linux has been very popular with researchers. The open-source code of Linux allows researchers to customize the system to best suit their specific problems, and to do advanced computing research without getting tied to any specific system vendor.
Starting in many system vendors started offering full production-level support on Linux platforms. This took Linux systems, and their clusters, out of the realm of early adopters into mainstream usage.
In this chapter we will begin by delineating various approaches to building parallel computing environments and compare and contrast them with clusters.
We will go over benefits of deploying clusters as well as expose their downsides. We will then discuss two broad categories of compute clusters and their usage to solve problems from various fields. We will end the chapter by giving a high level description of the architecture of a cluster and introduce terminology to be used throughout the book. Approaches in building a parallel computing environment Before discussing clusters in detail, let us examine different ways of putting various computational resources together to solve problems.
As explained in a subsection below, the boundaries between various types of parallel systems have been blurred significantly, both because of new technologies as well as vendor marketing efforts. The simplified taxonomy in next sections will help us discuss various trade-offs between different technologies. Design emphasis is put on scaling the OS to increasing number of CPUs, and providing an environment which allows a single application instance to scale on multiple CPUs. All CPUs within the system can directly access the physical memory as well as any peripherals installed in the system.
MPP systems have been designed to go up to hundreds, in some cases thousands, of processors. Processing elements sometimes referred to as nodes are connected together using a proprietary interconnect.
Each node runs its own copy of the OS kernel or microkernel. Programmers view an MPP as having distributed memory.
A processor cannot directly access the physical memory located in a remote node. The programmer or the compiler has to instruct the machine to transfer data from one node to another node on need basis.Commodity computing also known as commodity cluster computing involves the use of large numbers of already-available computing components for parallel computingto get the greatest amount of useful computation at low cost.
Commodity computers are computer systems - manufactured by multiple vendors - incorporating components based on open standards. Standardization and decreased differentiation lower the switching or exit cost from any given vendor, increasing purchasers' leverage and preventing lock-in. A governing principle of commodity computing is that it is preferable to have more low-performance, low-cost hardware working in parallel scalar computing e.
At some point, the number of discrete systems in a cluster will be greater than the mean time between failures MTBF for any hardware platform [ dubious — discuss ]no matter how reliable, so fault tolerance must be built into the controlling software.
The first computers were large, expensive and proprietary. This was a computer that was relatively small and inexpensive enough that a department could purchase one without convening a meeting of the board of directors.
The entire minicomputer industry sprang up to supply the demand for 'small' computers like the PDP Unfortunately, each of the many different brands of minicomputers had to stand on its own because there was no software and very little hardware compatibility between the brands. When the first general purpose microprocessor was introduced in it immediately began chipping away at the low end of the computer market, replacing embedded minicomputers in many industrial devices.
This process accelerated in with the introduction of the first commodity-like microcomputer, the Apple II. With the development of the VisiCalc application inmicrocomputers broke out of the factory and began entering office suites in large quantities, but still through the back door.
More and more PC-compatible microcomputers began coming into big companies through the front door and commodity computing was well established. During the s microcomputers began displacing larger computers in a serious way. At first, price was the key justification but by the late s and early s, VLSI semiconductor technology had evolved to the point where microprocessor performance began to eclipse the performance of discrete logic designs.
These traditional designs were limited by speed-of-light delay issues inherent in any CPU larger than a single chip, and performance alone began driving the success of microprocessor-based systems. By the mids, nearly all computers made were based on microprocessors, and the majority of general purpose microprocessors were implementations of the x86 instruction set architecture.
Although there was a time when every traditional computer manufacturer had its own proprietary micro-based designs there are only a few manufacturers of non-commodity computer systems today. Today, there are fewer and fewer general business computing requirements that cannot be met with off-the-shelf commodity computers.
It is likely that the low-end of the supermicrocomputer genre will continue to be pushed upward by increasingly powerful commodity microcomputers.
From Wikipedia, the free encyclopedia. Retrieved The purpose of commodity cluster computing is to utilize large numbers of readily available computing components for parallel computing to obtaining the greatest amount of useful computations for the least cost.