I am trying to write a PBS script.
I have to use 4 nodes in a cluster and to run 4 times an executable program. The difficulty is that the 4 executions have to run in parallel, one by node
For the moment, I have just allocated the nodes
#PBS -l nodes=4:ppn=1
The problem is I don't know how to be sure the programs run in parallel. How can I do it ?
If you don't need your program to communicate with other copies of itself, you don't need MPI. MPI stands for the Message Passing Interface (MPI Forum Website) and is the most popular way of writing a parallel program. There are lots of tutorials on the web that you can find with a quick search. I recommend taking a look at them. You can test things out on your own machines by installing an MPI library yourself. They're available in most package managers. The most popular open-source implementations are MPICH and Open MPI. If you're starting from scratch, try to get the most up to date versions as you'll get more help from the various communities that way.
You can execute a non-MPI program with mpiexec and it will launch across multiple nodes (assuming you've set things up correctly), but it won't do anything to make them communicate. That has to be done explicitly (again, see the MPI tutorials).
If you're not going to communicating between the processes (parallel computing), I have to wonder why you want to run multiple copies of the same program across the cluster to begin with. The only thing that comes to mind are simple cluster maintenance utilities, but there are much better ways of doing that than PBS/MPI setups and I'd imagine that if you were doing cluster maintenance, you'd already know what to use for that.
pbsdsh is a very simple way to accomplish what you're talking about.
# this will launch one copy of <executable> on each execution slot. For -l nodes=4:ppn=1
# it will launch it once per node.
pbsdsh <executable>
Sometimes you have to supply the full path to pbsdsh depending on your environment. It defaults to /usr/local/bin/pbsdsh.
If you aren't sure if your processes will start at the same time on multiple nodes, you can use the MPI_BARRIER function in your code. This will prevent all nodes from executing until they have all executed this function.
Related
I need to optimize some hyperparameters for a machine learning problem. This involves launching many jobs on the same input data and saving their outputs, completely independently of each other. On every job distribution system that I've ever used, this is a very common use case, which is handled with a few switches on the command line and/or a job config file. Now I'm on a cluster whose job distribution system is Hadoop/Yarn, which I haven't used before. Despite much searching, the only way to do this on Hadoop seems to be to submit each run as a separate job. This would incur the job submission overhead for each run, of which there can be 1000's. Is there a simple way around that? Maybe some kind of MR job without any R? (BTW, my ML code is in C++ so I guess I need to use Hadoop Streaming.) I'll learn Java if I have to, but it seems like a disproportionate amount of effort for something so simple.
I am new to cluster and PBS system. I am now wondering this, is it possible to schedule both parallel (MPI, OpenMP) and serial jobs on to one node in a cluster (the cluster has multiple nodes) with PBS system? Because in this way, it may improve the CPU utilization.
I've been told this is not possible but without detailed explanation. So, if this is really impossible, my question is why?
By default, all jobs are candidates to share nodes. In other words, without any configuration to the batch system parallel and serial jobs will share nodes.
Many sites decide to configure things so that this doesn't happen. Common reasons for segregating the jobs include:
It is an easy way to set policies for serial and parallel workloads.
It minimizes possibilities for one kind of workload to interfere with another.
Does anybody know how I could transform the code found on the Mahout in Action book, regarding the recommendation engines, so that it is consistent with a Ηadoop fully-distributed environment? My main difficulty is to transform my code (that currently reads and writes from a local disk) so that it runs in a pseudo-distributed environment (such Cloudera). Is the solution to my problem as simple as this one, or I should expect something more complex than that?
A truly distributed computation is quite different than a non-distributed computation, even when computing the same result. The structure is not the same, and the infrastructure it uses is not the same.
If you are just asking how the pseudo-distributed solution works regarding local files: you would ignore the Hadoop input/output mechanism and write a Mapper that reads your input from somewhere on HDFS and copies to local disk.
If you are asking how you actually distribute the computation, then you would have to switch to use the (completely-different) distributed implementations in the project. These actually use Hadoop to split up the computation. The process above is a hack that just runs many non-distributed tasks within a Hadoop container. These implementations are however completely off-line.
If you mean that you want a real-time recommender like in the Mahout .cf.taste packages, but also want to actually use Hadoop's distributed computing power, then you need more than Mahout. It's either one or the other in Mahout; there is code that does one or the other but they are not related.
This is exactly what Myrrix is, by the way. I don't mind advertising it here since it sounds like exactly what you may be looking for. It's an evolution of the work I began in this Mahout code. Among other things, it's a 2-tier architecture that has the real-time elements of Taste but can also transparently offload the computation to a Hadoop cluster.
At the company I work for, everyday we have to process a few thousands of files, which takes some hours. The operations are basically CPU intensive, like converting PDF to high resolution images and later creating many different sizes os such images.
Each one of those tasks takes a lot of CPU, and therefore we can't simply start many instances on the same machine because there won't be any processing power available for everything. Thus, it takes some hours to finish everything.
The most obvious thing to do, as I see it, is to partition the set of files and have them processed by more machines concurrently (5, 10, 15 machines, I don't know yet how many would be necessary).
I don't want to reinvent the wheel and create a manager for task (nor do I want the hassle), but I am not sure which tool should I use.
Although we don't have big data, I have looked at Hadoop for a start (we are running at Amazon), and its capabilities of handling the nodes seem interesting. However, I don't know if it makes sense to use it. I am looking at Hazelcast as well, but I have no experience at all with it or the concepts yet.
What would be a good approach for this task?
Hadoop is being used for a wide variety of data processing problems, some of them are related to image processing also. The problem mentioned in the OP can also be easily solved using Hadoop. Note that in some cases where the data to processed is small, then there is an overhead using Hadoop.
If you are new to Hadoop, would suggest a couple of things
Buy the Hadoop : The Definitive Guide book.
Go through the MapReduce resources.
Start going through the tutorials (1 and 2) and setup Hadoop on a single node and a cluster. There is no need for Amazon, if 1-2 machines can be spared for learning.
Run the sample programs and understand how they work.
Start migrating the problem area to Hadoop.
The advantage of Hadoop over other s/w is the ecosystem around Hadoop. As of now the ecosystem around Hadoop is huge and growing, I am not sure of Hazelcast.
You can use Hazelcast distributed queue.
First you can put your files (file references) as tasks to a distributed queue.
Then each node takes a task from the queue processes it and puts the result into another distributed queue/list or write it to DB/storage.
I'm currently working on a cluster using the ClusterVisionOS 3.1. This will be my first time working with a cluster, so I probably haven't tried the "obvious".
I can submit a single job to the cluster with the "qsub" command(this I got working properly)
But the problem starts when submitting multiple jobs at once. I could write a script sending them all at once, but then all nodes would be occupied with my jobs and there are more people here wanting to submit their job.
So here's the deal:
32 nodes (4 processors/slots each)
The best thing would be to tell the cluster to use 3 nodes (12 processors) and queue all my jobs on these nodes/processors, if this is even possible. If I could let the nodes use 1 processor for each job, then that would be perfect.
Ok, so i guess i found out, there is no solution to this problem. My personal solution is write a script that connects through ssh to the cluster and then just let the script check how many jobs are already running under your user name. The script checks if that number does not exceed, lets say, 20 jobs at the same time. As long as this number is not reached it keep submitting jobs.
Maybe its an ugly solution, but a working one!
About the processor thing, the jobs were already submitted to different single processors, fully utilizing the full extent of the nodes.