I can't comment on how the Mathematica book (do you mean Elementary Introduction to the Wolfram Language?) presents how to use the command line, but yes, this method has been around since Mathematica's inception.

I have seen the method I presented above carried out on a SLURM managed cluster, and I used a Torque/PBS managed cluster in my previous employment. From a user's point-of-view I am familiar with how managed clusters work. However, I have not used all cluster managers, thus why I requested community support. My goal was to have instructions in how to use Mathematica with the most common cluster managers, documented in one place.

As a follow up, one common-use scenario is to

1. log in to the cluster's head node with X-windows forwarding enabled
2. submit an "interactive session" to the cluster
3. wait for the resources to be provided and control of the terminal returned to the client
4. launch a Mathematica front end

Keep in mind, though, that all commands must be sent back-and-forth from the remote cluster to your screen. This can have a pretty annoying delay. The benefit is that most clusters have nodes with more CPU cores than your desktop, and each CPU core may be more powerful than those in your desktop.

The downside (besides the lag) is that if you've requested multiple nodes, then you don't know which node is acting as the head node. And if you use LaunchKernels[], then only the local kernels (on the head node) will launch. Because interactive jobs are intended as debugging sessions, one approach is to just exclusively request a single node.

Alternatively, you can query the environment variables of the job session and launch remote kernels like I showed earlier.

Hi Kevin,

I'm using a linux cluster of my insititute, we recently installed gridMathematica on it, and I'm pretty sure the first assumption you mentioned is well satisfied. However, our cluster has a dedicated login node as well as about 40 dedicated computing nodes, we users are only allowed to SSH to the login node, but don't have the previlage to SSH to the computing nodes, so the second assumption you mentioned is not satisfied in our case. The cluster is managed with SLURM, and mpi-based parallelization works well without the necessity of freely SSH among the nodes. So I just wonder if Mathematica has some mechanism to support large-scale parallelization if freely SSH is not allowed.

Many thanks for your help!

Zhe

Hi Kevin,

Thank you for your prompt reply! The attached file contains some of my previous test results. Since the cluster is very busy now, I will test the commands you suggest later on.

I confirmed with our cluster manager, and was told that normal users cannot SSH to the computing nodes, only root users can.

As to why MPI could still work in our cluster, I found some relevant information from the official website of SLURM: https://slurm.schedmd.com/quickstart.html.

It is stated that:

MPI use depends upon the type of MPI being used. There are three fundamentally different modes of operation used by these various MPI implementation.

Slurm directly launches the tasks and performs initialization of communications through the PMI2 or PMIx APIs. (Supported by most modern MPI implementations.)

Slurm creates a resource allocation for the job and then mpirun launches tasks using Slurm's infrastructure (older versions of OpenMPI).

Slurm creates a resource allocation for the job and then mpirun launches tasks using some mechanism other than Slurm, such as SSH or RSH. These tasks initiated outside of Slurm's monitoring or control. Slurm's epilog should be configured to purge these tasks when the job's allocation is relinquished. The use of pamslurmadopt is also strongly recommended.

I think your implementation of Mathematica working with SLURM follows the third approach, while MPICH we are using in the cluster follows the first approach and does not require SSH. Now I wonder if Mathematica could be somehow configured to work with SLURM using the first two approaches so that SSH is not necessary.

Your further comments and suggestions are highly appreciated!

Best,

Zhe

Attachments:

test_note_slurm_...docx

Hi，

I was granted SSH access privilege from the cluster manager and successfully ran parallel computing through SLURM. The attached file is copied from my test note then.

Note: By the time I was trying to solve this issue, Mathematica working with SLURM follows this approach "Slurm creates a resource allocation for the job and then mpirun launches tasks using some mechanism other than Slurm, such as SSH or RSH", and alternative approaches that do not require SSH access was not supported.

I think supporting alternative approaches working w/ SLURM w/o requiring SSH access would be a very nice feature to be added to Mathematica's capability, just like being already available in MATLAB.

Hopefully, this could be helpful to you.

Attachments:

mathematica_slur...docx

Kindly Help:

I access mathematica (through MobaTerm), which is installed in my user area on Torque managed HPC, to connect multiple nodes assigned to my Job submitted in Queue.

PROBLEM

Following is the program to connect 2 nodes, and each node consists of 40 cores.

Everything looks fine, expect the time taken by ParallelTable at the end of program, which is much higher than time taken by single node (without launching remote kernels)

Note that allotted two nodes atulya095 and atulya049 appear 40 times each in the output.

PROGRAM

nodes=ReadList[Environment["PBS_NODEFILE"], "String"];
Print["alloted node are  ", nodes];    (*get association of resources, name of local host and remove local host from available resources*)
hosts = Counts[nodes];                                             
local = First[StringSplit[Environment["HOSTNAME"],"."]];
Print["local node is ", local];
hosts[local]--;
Needs["SubKernels`RemoteKernels`"];               
Map[If[hosts[#] > 0, LaunchKernels[RemoteMachine[#, "ssh -x -f -l `3` `1` wolfram  -wstp -linkmode Connect `4` -linkname '`2`' -subkernel -noinit", hosts[#]]]]&,    Keys[hosts]];                
Print["kernel count is  ",$KernelCount];           
Print[" machine name is  ", ParallelEvaluate[$MachineName]];
Print[" kernel id is  ",ParallelEvaluate[$KernelID]];
Print["processor count is  ",$ProcessorCount];
Print[AbsoluteTiming[ParallelTable[Exp[Sin[x]]^Sin[x],{x,0.1,200000,0.0001}];][[1]]];   
CloseKernels[];
Quit[];

OUTPUT

alloted node are  {atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049}
local node is atulya095
kernel count is  79
machine name is  {atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya095, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049, atulya049}
kernel id is  {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79}
processor count is  40
189.309181

I will be thankful to your help here.

Dear Kevin,

Thanks for your reply.

Yes, there are 40 cores on atulya049 as well.

Ok, I will do one heavy calculation and get back to you.

With thanks, Sachin.

PS- In your First post, You suggested to remove the Wolfram controlling kernel from the list of available resources. So in my program, Out of requested cores (80), 79 are going into the calculation.

Could you tell us why do we need to remove the controlling Wolfram kernel ?