How can i run multiple computers as one?
i.e. one "master" which issues commands and one or more slaves who do what they are told to do.
also, How do the distributed computingsystems in supercomputers do this?
EDIT:
I found this, this and this and now i wonder, is there something similar that will run parallel programs like hash cracking? Mostly a software designed for these types of cloud computing systems.
In distributed computing systems, broadly speaking, there is no concept of master-slave, the way you describe.
It is a set of distinct autonomous machines (or to define it differently a set of HW or SW modules running at different computers) that work "together" to the same goal.
They achieve this by network communication.
It is as if you had a single software running (through all the machines) and the various processing modules of this software "running" in separate machines (as opossed to separate threads or processes in the same machine).
Parallel computing is not the same concept as distributed computing, a difference being that in distributed systems each machine uses its own memory.
Supercomputer is a term usually refering to hardware capabilities.
Related
I need to compare the performance of MATLAB and an open-source alternative (eg numpy/scipy) on particular matrices problems on both Windows and Linux. It has been expressly asked that the comparison is executed strictly on the same hardware platform - for obvious comparison reasons.
My question is, would using containers (Windows and Linux distro images) satisfy this requirement?
I believe setting two VMs would satisfy the requirement, but using containers would be much less of a hassle and make the tests easily reproducible on any machine, but I'm not too familiar with their architecture or how they access their host's hardware.
Thanks in advance for any help.
Essentially, when talking about performance tests, you'd want your test machines to be as close as possible to the final end-use machine.
While virtualization and containerization might offer different advantages in terms of ease of setup, reusability and whatever else, they also introduce a degree of distortion in the metrics obtained in such tests due to their architecture and implementation.
I don't know the exact degree to which a performance test on a Windows container would differ from a performance test run on a Windows machine, and if such difference is trivial or not, but the first paragraph answers my question.
The other day I needed to archive a lot of data on our network and I was frustrated I had no immediate way to harness the power of multiple machines to speed-up the process.
I understand that creating a distributed job management system is a leap from a command-line archiving tool.
I'm now wondering what the simplest solution to this type of distributed performance scenario could be. Would a custom tool always be a requirement or are there ways to use standard utilities and somehow distribute their load transparently at a higher level?
Thanks for any suggestions.
One way to tackle this might be to use a distributed make system to run scripts across networked hardware. This is (or used to be) an experimental feature of (some implementations of) GNU Make. Solaris implements a dmake utility for the same purpose.
Another, more heavyweight, approach might be to use Condor to distribute your archiving jobs. But I think you wouldn't install Condor just for the twice-yearly archiving runs, it's more of a system for regularly scavenging spare cycles from networked hardware.
The SCons build system, which is really a Python-based replacement for make, could probably be persuaded to hand work off across the network.
Then again, you could use scripts to ssh to start jobs on networked PCs.
So there are a few ways you could approach this without having to take up parallel programming with all the fun that that entails.
Are there analogs of Intel Cluster OpenMP? This library simulates shared-memory machine (like SMP or NUMA) while running on distributed memory machine (like Ethernet-connected cluster of PC's).
This library allows to start openmp programs directly on cluster.
I search for
libraries, which allow multithreaded programms run on distributed cluster
or libraries (replacement of e.g. libgomp), which allow OpenMP programms run on distributed cluster
or compilers, capable of generating cluster code from openmp programms, besides Intel C++
The keyword you want to be searching for is "distributed shared memory"; there's a Wikipedia page on the subject. MOSIX, which became openMOSIX, which is now being developed as part of LinuxPMI, is the closest thing I'm aware of; but I don't have much experience with the current LinuxPMI project.
One thing you need to be aware of is that none of these systems work especially well, performance-wise. (Maybe a more optimistic way of saying it is that it's a tribute to the developers that these things work at all). You can't just abstract away the fact that accessing on-node memory is very very different from memory on some other node over a network. Even making local memory systems fast is difficult and requires a lot of hardware; you can't just hope that a little bit of software will hide the fact that you're now doing things over a network.
The performance ramifications are especially important when you consider that OpenMP programs you might want to run are almost always going to be written assuming that memory accesses are local and thus cheap, because, well, that's what OpenMP is for. False sharing is bad enough when you're talking about different sockets accessing a common cache line - page-based false sharing across a network is just disasterous.
Now, it could well be that you have a very simple program with very little actual shared state, and a distributed shared memory system wouldn't be so bad -- but in that case I've got to think you'd be better off in the long run just migrating the problem away from a shared-memory-based model like OpenMP towards something that'll work better in a cluster environment anyway.
Does anyone know how a computer cluster can be used for everyday applications, like for example video games?
I would like to build a computer cluster that can run applications over the cluster that were not specifically designed for computer clusters and still see the performance increase. One use would be for video games, but I would also like to utilize the increased computing power for running a large network of virtualized machines.
It won't help, especially in the case of video games. You have to build around the cluster; the cluster does not work around you.
At any rate, video games require sub-50ms response time on input and response,and network propagation would just destroy any performance gains you might see. Video processing, on the other hand, benefits GREATLY from the cluster as the task is inherently geared toward parallelization. It does not require user input, and output is only measured in terms of the batch process.
If you have a program written for a single core, running it on a four-core processor won't help you (except that one core can be devoted to that program). For example, I have Visual Studio compiling on multiple cores on this machine, but linking is done on one core (and is annoyingly slow). In order to get use out of multiple cores, I have to either run something that can use multiple cores or run several separate programs.
Clusters are like that, only more so. All communication between the machines is explicit and must be programmed in. There are things you can do with a cluster (see Google's map-reduce algorithm), but they do require special programming and work.
Typical clusters are used either to specialize machines (one might be a database server and one a web server, for example), or to run large numbers of programs simultaneously.
You will not be able to easily run a video game on a cluster, unless it was already designed to work on multiple machines. I have not heard of such a game. You may have some luck creating a virtual server farm, but I doubt it will be easy to get it working perfectly. If you are interested in this, one example would be amazon's EC2 service. They offer virtual machines for "rent" by the hour. Behind the scenes, I assume they have a giant cluster that is supplying all of these virtual machines.
Unfortunately, unless you have some pretty clever operating system / software design in mind - simply connecting programs together via a cluster and hoping to get increased performance is not likely to work - especially not for video games. In order to get increased performance from running things in a cluster you have to program for it otherwise there is a good change you'd see a decrease in performance rather than an increase.
How can i connect two or more machines to form a network grid and how can i distribute work load to the two machines?
What operating systems do i need to run on the machines, and what application should i use to manage the load balancing?
NB: I read somewhere that google uses cheap machines to perform this fete, how do they connect two network cards( 'Teaming' ) and distribute load across the machines?
Good practical examples would serve me good, with actual code samples.
Pointers to some good site i might read this stuff will be highly appreciated.
An excellent place to start is with the Beowulf project. Basically an opensource cluster built on the Linux OS.
There are several software solutions in this expanding market. The term "cloud computing" is certainly gaining traction to describe what you want to do. Are you wanting a service, or do you want to run it in house?
I'm most familiar with Appistry EAF - Runs on commodity based hardware. Its available as a free download. Runs on windows or linux.
Another is GoGrid - I believe this is only available as a service, but I'm not as familiar with it.
There are many different approaches to parallel processing, and many types of system architectures you could use.
For commodity systems, there are clusters and grids, or you can even form a single system image from several pieces of commodity hardware. There is of course also load balancing, high availability, failover, etc.
It's pretty much impossible to answer this question without more detail. What exactly do you want to with these systems? The answer is very highly dependent on the application.
You might want to have a look at some of the stuff to do with Folding At Home and the SETI project, and some of their participants blogs, here is a pretty amazing cluster that a guy built in an IKEA cabinet:
http://helmer.sfe.se/
Might give you some ideas.
The question is too abstract.
One of the (imaginable) ways is to use MPI - a framework for parallel programming, the Wikipedia page includes examples in C++, and there are bindings for other languages.