Defiant Quick-Start Guide

Warning

The Defiant documentation is still under construction. If you have anything you would like to add please reach out to help@olcf.ornl.gov.

System Overview

“Defiant2” is the all new hardware refresh of the original “Defiant” system; however, the name of the cluster will remain Defiant. Defiant is an NCCS open access system and is one of the Advanced Computing Ecosystem (ACE) testbeds that provides a sandbox for testing advances in IRI workflows and patterns, emerging compute architectures and techniques for HPC, emerging storage architectures and techniques for HPC, emerging network architectures and techniques, cloudification of traditional HPC architectures. The system consists of 20 CPU, 2 GPU nodes and 1 login node for user code compilations.

Defiant Nodes

The system consists of 20 HPE Cray XD220 CPU nodes along with 2 HPE Cray XD670 Nvidia H200 GPU nodes. It also has 1 login node with an Nvidia H100 for user code compilations.

GPU Compute Nodes

Each Defiant GPU compute node consists of [2x] 32-core INTEL(R) XEON(R) PLATINUM 8562Y+ CPUs with 2 hardware threads per physical core. Both CPUs have access to 4TB of DDR5 memory and [8x] 144GB NVIDIA H200 GPUs. The GPU nodes have [8x] 400g Infiniband interconnects. Additionally, there are [2x] 100g connections for Data Streaming. Each node also contains [4x] 3.5TB NVME devices (SSDs).

Note

There are 2 NUMA domains per node, that are defined as follows:

NUMA 0: hardware threads 0-31, 64-95
NUMA 1: hardware threads 32-63, 96-127

CPU Compute Nodes

Each Defiant CPU compute node consists of [2x] 32-core INTEL(R) XEON(R) PLATINUM 8462Y+ CPUs with 2 hardware threads per physical core. Both CPUs have access to 512GB of DDR5 memory. The nodes have [1x] 400g Infiniband interconnect per node. Additionally, there are [1x] 25g connections for Data Streaming.

Note

There are 2 NUMA domains per node, that are defined as follows:

NUMA 0: hardware threads 0-31, 64-95
NUMA 1: hardware threads 32-63, 96-127

Login Node

The Defiant login node consists of [2x] 32-core INTEL(R) XEON(R) PLATINUM 8462Y+ CPUs with 2 hardware threads per physical core. Both CPUs have access to 384GB of DDR5 memory and [1x] 80GB NVIDIA H100 GPU. The login node has [1x] 400g Infiniband interconnect. Additionally, there are [2x] 100g connections for Data Streaming. The login node also contains a 890GB NVME device (SSD).

System Interconnect

The Defiant nodes are connected with 400g NDR Mellanox ConnectX-7 NIC Infiniband interconnects in a Leaf & Spine configuration.

File Systems

Defiant is connected to the Lustre Polis filesystem providing ~3.6 PB of usable namespace (/lustre/polis/).

Defiant also has access to the center-wide NFS-based filesystem (which provides user and project home areas) in /ccsopen/home/<username>

GPUs

Defiant contains a total of 16 NVIDIA H200 GPUs. Based on the Hopper Architecture, the NVIDIA H200 GPU has a peak performance of up to 4 PFLOPS in FP8. Each GPU contains 144 GB HBM3e Memory which can be accessed at speeds of 4.8 TB/s.

Connecting

To connect to Defiant, first ssh to the jump server: flux.op.ccs.ornl.gov and then to the login node: login1.defiant.ccs.ornl.gov. For example:

$ ssh username@flux.op.ccs.ornl.gov
$ ssh username@login1.defiant.ccs.ornl.gov

Data and Storage

For more detailed information about center-wide file systems and data archiving available on Defiant, please refer to the pages on Data Storage and Transfers, but the two subsections below give a quick overview of NFS and GPFS storage spaces.

NFS Filesystem

NFS Filesystem
Area	Path	Type	Permissions	Quota	Backups	Purge	Retention	On Compute Nodes
User Home	`/ccsopen/home/[userid]`	NFS	User set	50 GB	Yes	No	90 days	yes

Note

Please not that this /ccsopen location is not the same NFS filesystem as found in other Open enclave systems like Odo. So files created on Defiant will not be available on Odo.

Lustre Filesystem (Polis)

Polis
Area	Path	Type	Permissions	Quota	Backups	Purge	Retention	On Compute Nodes
Member Work	`/lustre/polis/[projid]/scratch/[userid]`	Lustre HPE ClusterStor	700	50 TB	No	90 days	N/A	yes
Project Work	`/lustre/polis/[projid]/proj-shared`	Lustre HPE ClusterStor	770	50 TB	No	90 days	N/A	yes
World Work	`/lustre/polis/[projid]/world-shared`	Lustre HPE ClusterStor	770	50 TB	No	90 days	N/A	yes

Programming Environment

OLCF provides Defiant users many pre-installed software packages and scientific libraries. To facilitate this, environment management tools are used to handle necessary changes to the shell.

Environment Modules (Lmod)

Environment modules are provided through Lmod, a Lua-based module system for dynamically altering shell environments. By managing changes to the shell’s environment variables (such as PATH, LD_LIBRARY_PATH, and PKG_CONFIG_PATH), Lmod allows you to alter the software available in your shell environment without the risk of creating package and version combinations that cannot coexist in a single environment.

General Usage

The interface to Lmod is provided by the module command:

Command	Description
`module -t list`	Shows a terse list of the currently loaded modules
`module avail`	Shows a table of the currently available modules
`module help <modulename>`	Shows help information about `<modulename>`
`module show <modulename>`	Shows the environment changes made by the `<modulename>` modulefile
`module spider <string>`	Searches all possible modules according to `<string>`
`module load <modulename> [...]`	Loads the given `<modulename>`(s) into the current environment
`module use <path>`	Adds `<path>` to the modulefile search cache and `MODULESPATH`
`module unuse <path>`	Removes `<path>` from the modulefile search cache and `MODULESPATH`
`module purge`	Unloads all modules
`module reset`	Resets loaded modules to system defaults
`module update`	Reloads all currently loaded modules

Searching for Modules

Modules with dependencies are only available when the underlying dependencies, such as compiler families, are loaded. Thus, module avail will only display modules that are compatible with the current state of the environment. To search the entire hierarchy across all possible dependencies, the spider sub-command can be used as summarized in the following table.

Command	Description
`module spider`	Shows the entire possible graph of modules
`module spider <modulename>`	Searches for modules named `<modulename>` in the graph of possible modules
`module spider <modulename>/<version>`	Searches for a specific version of `<modulename>` in the graph of possible modules
`module spider <string>`	Searches for modulefiles containing `<string>`

Compilers

Cray, NVIDIA, Intel, and GCC compilers are provided through modules on Defiant. The system GCC (version 11.5.0) compiler is also located in /usr/bin. The table below lists details about each of the module-provided compilers.

Note

It is highly recommended to use the Cray compiler wrappers (cc, CC, and ftn) whenever possible. See the next section for more details.

Vendor	Programming Environment	Compiler Module	Language	Compiler Wrapper	Compiler
Cray	`PrgEnv-cray`	`cce`	C	`cc`	`craycc`
			C++	`CC`	`craycxx` or `crayCC`
			Fortran	`ftn`	`crayftn`
NVIDIA	`PrgEnv-nvidia`	`nvidia`	C	`cc`	`nvc`
			C++	`CC`	`nvc++`
			Fortran	`ftn`	`nvfortran`
Intel	`PrgEnv-intel`	`intel`	C	`cc`	`N/A`
			C++	`CC`	`N/A`
			Fortran	`ftn`	`N/A`
GCC	`PrgEnv-gnu`	`gcc`	C	`cc`	`$GCC_PATH/bin/gcc`
			C++	`CC`	`$GCC_PATH/bin/g++`
			Fortran	`ftn`	`$GCC_PATH/bin/gfortran`

Cray Programming Environment and Compiler Wrappers

Cray provides PrgEnv-<compiler> modules (e.g., PrgEnv-cray) that load compatible components of a specific compiler toolchain. The components include the specified compiler as well as MPI, LibSci, and other libraries. Loading the PrgEnv-<compiler> modules also defines a set of compiler wrappers for that compiler toolchain that automatically add include paths and link in libraries for Cray software. Compiler wrappers are provided for C (cc), C++ (CC), and Fortran (ftn).

Note

Use the -craype-verbose flag to display the full include and link information used by the Cray compiler wrappers. This must be called on a file to see the full output (e.g., CC -craype-verbose test.cpp).

MPI

The MPI implementation available on Defiant is Cray’s MPICH, which is “GPU-aware” so GPU buffers can be passed directly to MPI calls. Currently, Defiant has MPICH versions 8.1.32 as default and 9.0.0 as an additional module.

Starting from the 8.1.26 release, Cray MPICH supports the Intel Sapphire Rapids CPU HBM processor architecture

Compiling

This section covers how to compile for different programming models using the different compilers covered in the previous section.

MPI

Implementation	Module	Compiler	Header Files & Linking
Cray MPICH	`cray-mpich`	`cc`, `CC`, `ftn` (Cray compiler wrappers)	MPI header files and linking is built into the Cray compiler wrappers
Cray MPICH	`cray-mpich`	`nvcc`	`-I$(MPICH_DIR)/include` `-L$(MPICH_DIR)/lib` `-lmpi`

GPU-Aware MPI

1. Compiling with the Cray compiler wrappers, `cc` or `CC`

To use GPU-aware Cray MPICH with the Cray compiler wrappers, users must load specific modules, set some environment variables, and include appropriate headers and libraries. The following modules and environment variables must be set:

#Load your Programming Environment of Choice
module load [PrgEnv-cray, PrgEnv-nvidia, PrgEnv-gnu]

module load cuda/12.6
module load craype-accel-nvidia90

## These must be set before running
export MPICH_GPU_SUPPORT_ENABLED=1

In addition, the HPE Cray MPI compiler wrappers are not aware of the HPE Cray MPI GTL libraries for use with GPU codes, they can be simply used to compile for use with GPUs. Relative symlinks are provided in the library directories pointing to the location of the gtl libraries within a given version of HPE Cray MPI. Simply link the appropriate gtl library for your intended GPU.

-lmpi_gtl_cuda

You will also likely need #include <cuda_runtime_api.h> included in the source file.

OpenMP

This section shows how to compile with OpenMP using the different compilers covered above.

Vendor	Module	Language	Compiler	OpenMP flag (CPU thread)
Cray	`cce`	C, C++	`cc` `CC`	`-fopenmp`
Cray	`cce`	Fortran	`ftn`	`-homp` `-fopenmp` (alias)
NVIDIA	`nvidia`	C C++ Fortran	`nvc` `nvc++` `nvfortan`	`-fopenmp`
GCC	`gcc`	C C++ Fortran	`$GCC_PATH/bin/gcc` `$GCC_PATH/bin/g++` `$GCC_PATH/bin/gfortran`	`-fopenmp`

OpenMP GPU Offload

This section shows how to compile with OpenMP Offload using the different compilers covered above.

Note

Make sure the craype-accel-nvidia90 & cuda modules are loaded when using OpenMP offload.

Vendor	Module	Language	Compiler	OpenMP flag (GPU)
Cray	`cce`	C C++	`cc` `CC`	`-fopenmp`
Cray	`cce`	Fortran	`ftn`	`-homp` `-fopenmp` (alias)
NVIDIA	`nvidia`	C C++ Fortran	`nvc` `nvc++` `nvfortan`	`-fopenmp`

Running Jobs

This section describes how to run programs on the Defiant compute nodes, including a brief overview of Slurm and also how to map processes and threads to CPU cores and GPUs.

Slurm Workload Manager

Slurm is the workload manager used to interact with the compute nodes on Defiant. In the following subsections, the most commonly used Slurm commands for submitting, running, and monitoring jobs will be covered, but users are encouraged to visit the official documentation and man pages for more information.

Batch Scheduler and Job Launcher

Slurm provides 3 ways of submitting and launching jobs on Defiant’s compute nodes: batch scripts, interactive, and single-command. The Slurm commands associated with these methods are shown in the table below and examples of their use can be found in the related subsections.

`sbatch`	Used to submit a batch script to allocate a Slurm job allocation. The script contains options preceded with `#SBATCH`. (see Batch Scripts section below)
`salloc`	Used to allocate an interactive Slurm job allocation, where one or more job steps (i.e., `srun` commands) can then be launched on the allocated resources (i.e., nodes). (see Interactive Jobs section below)
`srun`	Used to run a parallel job (job step) on the resources allocated with sbatch or `salloc`. If necessary, srun will first create a resource allocation in which to run the parallel job(s). (see Single Command section below)

Batch Scripts

A batch script can be used to submit a job to run on the compute nodes at a later time. In this case, stdout and stderr will be written to a file(s) that can be opened after the job completes. Here is an example of a simple batch script:

#!/bin/bash
#SBATCH -A <project_id>
#SBATCH -J <job_name>
#SBATCH -o %x-%j.out
#SBATCH -t 00:05:00
#SBATCH -p <partition>
#SBATCH -N 2

srun -n4 --ntasks-per-node=2 ./a.out

The Slurm submission options are preceded by #SBATCH, making them appear as comments to a shell (since comments begin with #). Slurm will look for submission options from the first line through the first non-comment line. Options encountered after the first non-comment line will not be read by Slurm. In the example script, the lines are:

Line	Description
1	[Optional] shell interpreter line
2	OLCF project to charge
3	Job name
4	stdout file name ( `%x` represents job name, `%j` represents job id)
5	Walltime requested (`HH:MM:SS`)
6	Batch queue
7	Number of compute nodes requested
8	Blank line
9	`srun` command to launch parallel job (requesting 4 processes - 2 per node)

Interactive Jobs

To request an interactive job where multiple job steps (i.e., multiple srun commands) can be launched on the allocated compute node(s), the salloc command can be used:

$ salloc -A <project_id> -J <job_name> -t 00:05:00 -p <partition> -N 2
salloc: Granted job allocation 4258
salloc: Waiting for resource configuration
salloc: Nodes defiant[10-11] are ready for job

$ srun -n 4 --ntasks-per-node=2 ./a.out
<output printed to terminal>

$ srun -n 2 --ntasks-per-node=1 ./a.out
<output printed to terminal>

Here, salloc is used to request an allocation of compute nodes for 5 minutes. Once the resources become available, the user is granted access to the compute nodes (defiant10 and defiant11 in this case) and can launch job steps on them using srun.

Single Command (non-interactive)

$ srun -A <project_id> -t 00:05:00 -p <partition> -N 2 -n 4 --ntasks-per-node=2 ./a.out
<output printed to terminal>

The job name and output options have been removed since stdout/stderr are typically desired in the terminal window in this usage mode.

Common Slurm Submission Options

The table below summarizes commonly-used Slurm job submission options:

`A <project_id>`	Project ID to charge
`-J <job_name>`	Name of job
`-p <partition>`	Partition / batch queue
`-t <time>`	Wall clock time <`HH:MM:SS`>
`-N <number_of_nodes>`	Number of compute nodes
`-o <file_name>`	Standard output file name
`-e <file_name>`	Standard error file name

For more information about these and/or other options, please see the sbatch man page.

Other Common Slurm Commands

The table below summarizes commonly-used Slurm commands:

`sinfo`	Used to view partition and node information. E.g., to view user-defined details about the caar queue: `sinfo -p caar -o "%15N %10D %10P %10a %10c %10z"`
`squeue`	Used to view job and job step information for jobs in the scheduling queue. E.g., to see all jobs from a specific user: `squeue -l -u <user_id>`
`sacct`	Used to view accounting data for jobs and job steps in the job accounting log (currently in the queue or recently completed). E.g., to see a list of specified information about all jobs submitted/run by a users since 1 PM on January 4, 2021: `sacct -u <username> -S 2021-01-04T13:00:00 -o "jobid%5,jobname%25,user%15,nodelist%20" -X`
`scancel`	Used to signal or cancel jobs or job steps. E.g., to cancel a job: `scancel <jobid>`
`scontrol`	Used to view or modify job configuration. E.g., to place a job on hold: `scontrol hold <jobid>`

Slurm Compute Node Partitions

Defiant’s compute nodes are separated into 2 Slurm partitions (queues): 1 for CPU jobs and 1 for GPU. Please see the tables below for details.

PARTITION	NODELIST
batch-cpu	defiant[01-20]
batch-gpu	defiant-nv[01-02]

Process and Thread Mapping

This section describes how to map processes (e.g., MPI ranks) and process threads (e.g., OpenMP threads) to the CPUs and GPUs on Defiant. The defiant-compute-nodes diagram will be helpful when reading this section to understand which hardware threads your processes and threads run on.

CPU Mapping

In this sub-section, a simple MPI+OpenMP “Hello, World” program (hello_mpi_omp) will be used to clarify the mappings. Slurm’s Interactive Jobs method was used to request an allocation of 1 compute node for these examples: salloc -A <project_id> -t 30 -p <parition> -N 1

The srun options used in this section are (see man srun for more information):

`-c, --cpus-per-task=<ncpus>`	Request that `ncpus` be allocated per process (default is 1). (`ncpus` refers to hardware threads)
`--threads-per-core=<threads>`	In task layout, use the specified maximum number of threads per core (default is 1; there are 2 hardware threads per physical CPU core).
`--cpu-bind=threads`	Bind tasks to CPUs. `threads` - Automatically generate masks binding tasks to threads. (Although this option is not explicitly used in these examples, it is the default CPU binding.)

Note

In the srun man page (and so the table above), threads refers to hardware threads.

2 MPI ranks - each with 2 OpenMP threads

In this example, the intent is to launch 2 MPI ranks, each of which spawn 2 OpenMP threads, and have all of the 4 OpenMP threads run on different physical CPU cores.

First (INCORRECT) attempt

To set the number of OpenMP threads spawned per MPI rank, the OMP_NUM_THREADS environment variable can be used. To set the number of MPI ranks launched, the srun flag -n can be used.

$ export OMP_NUM_THREADS=2
$ srun -n2 ./hello_mpi_omp | sort

WARNING: Requested total thread count and/or thread affinity may result in
oversubscription of available CPU resources!  Performance may be degraded.
Explicitly set OMP_WAIT_POLICY=PASSIVE or ACTIVE to suppress this message.
Set CRAY_OMP_CHECK_AFFINITY=TRUE to print detailed thread-affinity messages.
WARNING: Requested total thread count and/or thread affinity may result in
oversubscription of available CPU resources!  Performance may be degraded.
Explicitly set OMP_WAIT_POLICY=PASSIVE or ACTIVE to suppress this message.
Set CRAY_OMP_CHECK_AFFINITY=TRUE to print detailed thread-affinity messages.

MPI 000 - OMP 000 - HWT 000 - Node defiant01
MPI 000 - OMP 001 - HWT 000 - Node defiant01
MPI 001 - OMP 000 - HWT 016 - Node defiant01
MPI 001 - OMP 001 - HWT 016 - Node defiant01

The first thing to notice here is the WARNING about oversubscribing the available CPU cores. Also, the output shows each MPI rank did spawn 2 OpenMP threads, but both OpenMP threads ran on the same hardware thread (for a given MPI rank). This was not the intended behavior; each OpenMP thread was meant to run on its own physical CPU core.

Second (CORRECT) attempt

By default, each MPI rank is allocated only 1 hardware thread, so both OpenMP threads only have that 1 hardware thread to run on - hence the WARNING and undesired behavior. In order for each OpenMP thread to run on its own physical CPU core, each MPI rank should be given 2 hardware thread (-c 2) - since, by default, only 1 hardware thread per physical CPU core is enabled (this would need to be -c 4 if --threads-per-core=2 instead of the default of 1. The OpenMP threads will be mapped to unique physical CPU cores unless there are not enough physical CPU cores available, in which case the remaining OpenMP threads will share hardware threads and a WARNING will be issued as shown in the previous example.

$ export OMP_NUM_THREADS=2
$ srun -n2 -c2 ./hello_mpi_omp | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant11
MPI 000 - OMP 001 - HWT 001 - Node defiant11
MPI 001 - OMP 000 - HWT 032 - Node defiant11
MPI 001 - OMP 001 - HWT 033 - Node defiant11

Now the output shows that each OpenMP thread ran on (one of the hardware threads of) its own physical CPU cores. More specifically (see the Defiant Compute Node diagram), OpenMP thread 000 of MPI rank 000 ran on hardware thread 000 (i.e., physical CPU core 00), OpenMP thread 001 of MPI rank 000 ran on hardware thread 001 (i.e., physical CPU core 01), OpenMP thread 000 of MPI rank 001 ran on hardware thread 032 (i.e., physical CPU core 32), and OpenMP thread 001 of MPI rank 001 ran on hardware thread 033 (i.e., physical CPU core 33) - as expected.

Note

There are many different ways users might choose to perform these mappings, so users are encouraged to clone the hello_mpi_omp program and test whether or not processes and threads are running where intended.

GPU Mapping

In this sub-section, an MPI+OpenMP+CUDA “Hello, World” program, hello_jobstep.cpp, will be used to clarify the GPU mappings.

 /**********************************************************
 "Hello World"-type program to test different srun layouts.

 Written by Tom Papatheodore
 **********************************************************/

 #include <stdlib.h>
 #include <stdio.h>
 #include <iostream>
 #include <iomanip>
 #include <iomanip>
 #include <string.h>
 #include <mpi.h>
 #include <sched.h>
 #include <cuda.h>
 #include <cuda_runtime_api.h>
 #include <omp.h>

 // Macro for checking errors in HIP API calls
 #define cudaErrorCheck(call)                                                                 \
 do{                                                                                         \
     cudaError_t cudaErr = call;                                                               \
     if(cudaSuccess != cudaErr){                                                               \
         printf("CUDA Error - %s:%d: '%s'\n", __FILE__, __LINE__, cudaGetErrorString(cudaErr)); \
         exit(0);                                                                            \
     }                                                                                       \
 }while(0)

 int main(int argc, char *argv[]){

         MPI_Init(&argc, &argv);

         int size;
         MPI_Comm_size(MPI_COMM_WORLD, &size);

         int rank;
         MPI_Comm_rank(MPI_COMM_WORLD, &rank);

         char name[MPI_MAX_PROCESSOR_NAME];
         int resultlength;
         MPI_Get_processor_name(name, &resultlength);

     // If CUDA_VISIBLE_DEVICES is set, capture visible GPUs
     const char* gpu_id_list;
     const char* cuda_visible_devices = getenv("CUDA_VISIBLE_DEVICES");
     if(cuda_visible_devices == NULL){
         gpu_id_list = "N/A";
     }
     else{
         gpu_id_list = cuda_visible_devices;
     }

         // Find how many GPUs HIP runtime says are available
         int num_devices = 0;
     cudaErrorCheck( cudaGetDeviceCount(&num_devices) );

         int hwthread;
         int thread_id = 0;

         if(num_devices == 0){
                 #pragma omp parallel default(shared) private(hwthread, thread_id)
                 {
                         thread_id = omp_get_thread_num();
                         hwthread = sched_getcpu();

             printf("MPI %03d - OMP %03d - HWT %03d - Node %s\n",
                     rank, thread_id, hwthread, name);

             }
     }
     else{

             char busid[64];

     std::string busid_list = "";
     std::string rt_gpu_id_list = "";

             // Loop over the GPUs available to each MPI rank
             for(int i=0; i<num_devices; i++){

                     cudaErrorCheck( cudaSetDevice(i) );

                     // Get the PCIBusId for each GPU and use it to query for UUID
                     cudaErrorCheck( cudaDeviceGetPCIBusId(busid, 64, i) );

                     // Concatenate per-MPIrank GPU info into strings for print
         if(i > 0) rt_gpu_id_list.append(",");
         rt_gpu_id_list.append(std::to_string(i));

         std::string temp_busid(busid);

         if(i > 0) busid_list.append(",");
         busid_list.append(temp_busid.substr(5,2));

             }

             #pragma omp parallel default(shared) private(hwthread, thread_id)
             {
         #pragma omp critical
         {
                     thread_id = omp_get_thread_num();
                     hwthread = sched_getcpu();

         printf("MPI %03d - OMP %03d - HWT %03d - Node %s - RT_GPU_ID %s - GPU_ID %s - Bus_ID %s\n",
                 rank, thread_id, hwthread, name, rt_gpu_id_list.c_str(), gpu_id_list, busid_list.c_str());
        }
             }
     }

     MPI_Finalize();

     return 0;
 }

Makefile

 COMP   = CC

 CFLAGS = -std=c++11 -fopenmp
 LFLAGS = -fopenmp

 INCLUDES  = -I${MPICH_DIR}/include
 LIBRARIES = -L${MPICH_DIR}/lib

 hello_jobstep: hello_jobstep.o
         ${COMP} ${LFLAGS} ${LIBRARIES} hello_jobstep.o -o hello_jobstep

 hello_jobstep.o: hello_jobstep.cpp
         ${COMP} ${CFLAGS} ${INCLUDES} -c hello_jobstep.cpp

 .PHONY: clean

 clean:
         rm -f hello_jobstep *.o

Again, Slurm’s Interactive Jobs method was used to request an allocation of 2 compute node for these examples: salloc -A <project_id> -t 30 -p <parition> -N 2. The CPU mapping part of this example is very similar to the example used above in the CPU Mapping sub-section, so the focus here will be on the GPU mapping part.

The following srun options will be used in the examples below. See man srun for a complete list of options and more information.

`--gpus-per-task`	Specify the number of GPUs required for the job on each task to be spawned in the job’s resource allocation.
`--gpu-bind=map_gpu:<list>`	Bind tasks to specific GPUs by setting GPU masks on tasks (or ranks) as specified where `<list>` is `<gpu_id_for_task_0>,<gpu_id_for_task_1>,...`. If the number of tasks (or ranks) exceeds the number of elements in this list, elements in the list will be reused as needed starting from the beginning of the list. To simplify support for large task counts, the lists may follow a map with an asterisk and repetition count. (For example `map_gpu:04,14`)
`--gpu-bind=closest`	Bind all GPUs to all tasks
`--ntasks-per-gpu=<ntasks>`	Request that there are ntasks tasks invoked for every GPU.
`--distribution=<value>[:<value>][:<value>]`	Specifies the distribution of MPI ranks across compute nodes, sockets (NUMA domains on Defiant), and cores, respectively. The default values are `block:cyclic:cyclic`

Note

In general, GPU mapping can be accomplished in different ways. For example, an application might map MPI ranks to GPUs programmatically within the code using, say, cudaSetDevice. In this case, since all GPUs on a node are available to all MPI ranks on that node by default, there might not be a need to map to GPUs using Slurm (just do it in the code). However, in another application, there might be a reason to make only a subset of GPUs available to the MPI ranks on a node. It is this latter case that the following examples refer to.

Mapping 1 task per GPU

In the following examples, each MPI rank (and its OpenMP threads) will be mapped to a single GPU.

Example 1: 4 MPI ranks - each with 2 OpenMP threads and 1 GPU (single-node)

This example launches 4 MPI ranks (-n4), each with 2 physical CPU cores (-c2) to launch 2 OpenMP threads (OMP_NUM_THREADS=2) on. In addition, each MPI rank (and its 2 OpenMP threads) should have access to only 1 GPU. To accomplish the GPU mapping, one new srun options will be used:

--gpus-per-task specifies the number of GPUs required for the job on each task

Note

To further clarify, --gpus-per-task does not actually bind GPUs to MPI ranks. It allocates GPUs to the job step. The default GPU map is what actually maps a specific GPU to each rank. (see the defiant-compute-nodes section).

$ export OMP_NUM_THREADS=2
$ srun -N1 -n4 -c2 --gpus-per-task=1 ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 000 - OMP 001 - HWT 001 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 001 - OMP 000 - HWT 002 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 001 - OMP 001 - HWT 003 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 002 - OMP 000 - HWT 004 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 002 - OMP 001 - HWT 005 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 003 - OMP 000 - HWT 006 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 003 - OMP 001 - HWT 007 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A

The output contains different IDs associated with the GPUs so it is important to first describe these IDs before moving on. GPU_ID is the node-level (or global) GPU ID, which is labeled as one might expect from looking at a node diagram: 0, 1, 2, 3. RT_GPU_ID is the CUDA runtime GPU ID, which can be thought of as each MPI rank’s local GPU ID numbering (with zero-based indexing). So in the output above, each MPI rank has access to 1 unique GPU - where MPI 000 has access to GPU 0, MPI 001 has access to GPU 1, etc., but all MPI ranks show a CUDA runtime GPU ID of 0. The reason is that each MPI rank only “sees” one GPU and so the CUDA runtime labels it as “0”, even though it might be global GPU ID 0, 1, 2, or 3. The GPU’s bus ID is included to definitively show that different GPUs are being used.

Here is a summary of the different GPU IDs reported by the example program:

GPU_ID is the node-level (or global) GPU ID read from CUDA_VISIBLE_DEVICES. If this environment variable is not set (either by the user or by Slurm), the value of GPU_ID will be set to N/A.
RT_GPU_ID is the CUDA runtime GPU ID (as reported from, say cudaGetDevice).
Bus_ID is the physical bus ID associated with the GPUs. Comparing the bus IDs is meant to definitively show that different GPUs are being used.

So the job step (i.e., srun command) used above gave the desired output. Each MPI rank spawned 2 OpenMP threads and had access to a unique GPU. The --gpus-per-task=1 allocated 1 GPU for each MPI rank and the default binding bound each GPU to the respective task.

Example 2: 8 MPI ranks - each with 2 OpenMP threads and 1 GPU (multi-node)

This example will extend Example 1 to run on 2 nodes. As the output shows, it is a very straightforward exercise of changing the number of nodes to 2 (-N2) and the number of MPI ranks to 8 (-n8).

$ export OMP_NUM_THREADS=2
$ srun -N2 -n8 -c2 --gpus-per-task=1 ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 000 - OMP 001 - HWT 001 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 001 - OMP 000 - HWT 002 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 001 - OMP 001 - HWT 003 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 002 - OMP 000 - HWT 004 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 002 - OMP 001 - HWT 005 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 003 - OMP 000 - HWT 006 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 003 - OMP 001 - HWT 007 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 004 - OMP 000 - HWT 000 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 004 - OMP 001 - HWT 001 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 005 - OMP 000 - HWT 002 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 005 - OMP 001 - HWT 003 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 006 - OMP 000 - HWT 004 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 006 - OMP 001 - HWT 005 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 007 - OMP 000 - HWT 006 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 007 - OMP 001 - HWT 007 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A

Example 3: 4 MPI ranks - each with 2 OpenMP threads and 1 *specific* GPU (single-node)

This example will be very similar to Example 1, but instead of using default binding --gpu-bind=map_gpu will be used to map each MPI rank to a specific GPU. The map_gpu option takes a comma-separated list of GPU IDs to specify how the MPI ranks are mapped to GPUs, where the form of the comma-separated list is <gpu_id_for_task_0>, <gpu_id_for_task_1>,....

$ export OMP_NUM_THREADS=2
$ srun -N1 -n4 -c2 --gpus=8 --gpu-bind=map_gpu:0,2,4,6 ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 000 - OMP 001 - HWT 001 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 001 - OMP 000 - HWT 032 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 001 - OMP 001 - HWT 033 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 002 - OMP 000 - HWT 002 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 4 - Bus_ID 87
MPI 002 - OMP 001 - HWT 003 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 4 - Bus_ID 87
MPI 003 - OMP 000 - HWT 034 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 6 - Bus_ID BE
MPI 003 - OMP 001 - HWT 035 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 6 - Bus_ID BE

While this level of control over mapping MPI ranks to GPUs might be useful for some applications, it is always important to consider the implication of the mapping. For example, if the order of the GPU IDs in the map_gpu option is reversed, the MPI ranks and the GPUs they are mapped to would be in different NUMA domains, which could potentially lead to poorer performance.

$ export OMP_NUM_THREADS=2
$ srun -N1 -n4 -c2 --gpus=8 --gpu-bind=map_gpu:6,4,2,0 ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 6 - Bus_ID BE
MPI 000 - OMP 001 - HWT 001 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 6 - Bus_ID BE
MPI 001 - OMP 000 - HWT 032 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 4 - Bus_ID 87
MPI 001 - OMP 001 - HWT 033 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 4 - Bus_ID 87
MPI 002 - OMP 000 - HWT 002 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 002 - OMP 001 - HWT 003 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 003 - OMP 000 - HWT 034 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 003 - OMP 001 - HWT 035 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A

Here, notice that MPI 000 now maps to GPU 6, MPI 001 maps to GPU 4, etc., so the MPI ranks are not in the same NUMA domains as the GPUs they are mapped to.

Example 4: 8 MPI ranks - each with 2 OpenMP threads and 1 *specific* GPU (multi-node)

Extending Examples 2 and 3 to run on 2 nodes is also a straightforward exercise by changing the number of nodes to 2 (-N2) and the number of MPI ranks to 8 (-n8).

$ export OMP_NUM_THREADS=2
$ srun -N2 -n8 -c2 --gpus=16 --gpu-bind=map_gpu:0,2,4,7 ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 000 - OMP 001 - HWT 001 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 001 - OMP 000 - HWT 032 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 001 - OMP 001 - HWT 033 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 002 - OMP 000 - HWT 002 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 4 - Bus_ID 87
MPI 002 - OMP 001 - HWT 003 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 4 - Bus_ID 87
MPI 003 - OMP 000 - HWT 034 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 7 - Bus_ID C7
MPI 003 - OMP 001 - HWT 035 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 7 - Bus_ID C7
MPI 004 - OMP 000 - HWT 000 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 004 - OMP 001 - HWT 001 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 005 - OMP 000 - HWT 032 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 005 - OMP 001 - HWT 033 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 006 - OMP 000 - HWT 002 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 4 - Bus_ID 87
MPI 006 - OMP 001 - HWT 003 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 4 - Bus_ID 87
MPI 007 - OMP 000 - HWT 034 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 7 - Bus_ID C7
MPI 007 - OMP 001 - HWT 035 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 7 - Bus_ID C7

Mapping multiple MPI ranks to a single GPU

In the following examples, 2 MPI ranks will be mapped to 1 GPU. For the sake of brevity, OMP_NUM_THREADS will be set to 1, so -c1 will be used unless otherwise specified.

Example 5: 8 MPI ranks - where 2 ranks share a GPU (round-robin, single-node)

This example launches 8 MPI ranks (-n8), each with 1 physical CPU core (-c1) to launch 1 OpenMP thread (OMP_NUM_THREADS=1) on. The MPI ranks will be assigned to GPUs in a round-robin fashion so that each of the 4 GPUs on the node are shared by 2 MPI ranks. To accomplish this GPU mapping, a new srun option will be used:

--ntasks-per-gpu specifies the number of MPI ranks that will share access to a GPU.
--gpu-bind=map_gpu Bind tasks to specific GPUs by setting GPU masks on tasks (or ranks) as specified where <list> is <gpu_id_for_task_0>,<gpu_id_for_task_1>,…

$ export OMP_NUM_THREADS=1
$ srun -N1 -n8 -c1  --ntasks-per-gpu=2 --gpu-bind=map_gpu:0,1,2,3 ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 001 - OMP 000 - HWT 001 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 002 - OMP 000 - HWT 002 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 003 - OMP 000 - HWT 003 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 004 - OMP 000 - HWT 004 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 005 - OMP 000 - HWT 005 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 006 - OMP 000 - HWT 006 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 007 - OMP 000 - HWT 007 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A

Example 6: 16 MPI ranks - where 2 ranks share a GPU (round-robin, multi-node)

This example is an extension of Example 5 to run on 2 nodes.

$ module load craype-network-ucx
$ module load cray-mpich-ucx/8.1.32
$ export OMP_NUM_THREADS=1
$ srun -N2 -n16 -c1 --ntasks-per-gpu=2 ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 001 - OMP 000 - HWT 001 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 002 - OMP 000 - HWT 002 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 003 - OMP 000 - HWT 003 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 004 - OMP 000 - HWT 004 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 005 - OMP 000 - HWT 005 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 006 - OMP 000 - HWT 006 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 007 - OMP 000 - HWT 007 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 008 - OMP 000 - HWT 000 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 009 - OMP 000 - HWT 001 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 010 - OMP 000 - HWT 002 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 011 - OMP 000 - HWT 003 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 012 - OMP 000 - HWT 004 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 013 - OMP 000 - HWT 005 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 014 - OMP 000 - HWT 006 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 015 - OMP 000 - HWT 007 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A

Example 7: 8 MPI ranks - where 2 ranks share a GPU (packed, single-node)

This example launches 8 MPI ranks (-n8), each with 8 physical CPU cores (-c8) to launch 1 OpenMP thread (OMP_NUM_THREADS=1) on. The MPI ranks will be assigned to GPUs in a packed fashion so that each of the 4 GPUs on the node are shared by 2 MPI ranks. Packed block distribution appears to be the default for GPU binding; however, the distribution flag does impact the CPU/Thread binding.

$ export OMP_NUM_THREADS=1
$ srun -N1 -n8 -c8 --ntasks-per-gpu=2 --distribution=block:block ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 001 - OMP 000 - HWT 008 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 002 - OMP 000 - HWT 016 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 003 - OMP 000 - HWT 024 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 004 - OMP 000 - HWT 032 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 005 - OMP 000 - HWT 040 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 006 - OMP 000 - HWT 048 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 007 - OMP 000 - HWT 056 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A

Example 8: 16 MPI ranks - where 2 ranks share a GPU (packed, multi-node)

This example is an extension of Example 7 to use 2 compute nodes. With the appropriate changes put in place in Example 7, it is a straightforward exercise to change to using 2 nodes (-N2) and 16 MPI ranks (-n16).

$ export OMP_NUM_THREADS=1
$ srun -N2 -n16 -c8 --ntasks-per-gpu=2 --distribution=*:block ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 001 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 001 - OMP 000 - HWT 008 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 002 - OMP 000 - HWT 016 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 003 - OMP 000 - HWT 024 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 004 - OMP 000 - HWT 032 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 005 - OMP 000 - HWT 040 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 006 - OMP 000 - HWT 049 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 007 - OMP 000 - HWT 056 - Node defiant-nv01 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 008 - OMP 000 - HWT 001 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 009 - OMP 000 - HWT 008 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 0 - Bus_ID 0A
MPI 010 - OMP 000 - HWT 016 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 011 - OMP 000 - HWT 024 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 1 - Bus_ID 18
MPI 012 - OMP 000 - HWT 034 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 013 - OMP 000 - HWT 040 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 2 - Bus_ID 41
MPI 014 - OMP 000 - HWT 048 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A
MPI 015 - OMP 000 - HWT 056 - Node defiant-nv02 - RT_GPU_ID 0 - GPU_ID 3 - Bus_ID 4A

Example 9: Multiple GPUs per MPI rank

This example launches one MPI rank with all available GPUs on the node visible to the rank.

$ export OMP_NUM_THREADS=1
$ srun -N1 -n1 -c1 --gpus-per-task=8 ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv01 - RT_GPU_ID 0,1,2,3,4,5,6,7 - GPU_ID 0,1,2,3,4,5,6,7 - Bus_ID 0A,18,41,4A,87,90,BE,C7

Example 10: All MPI Tasks to All GPUs

This example show the usage of --gpu-bind=closest which will bind all GPUs to all MPI tasks.

$ export OMP_NUM_THREADS=1
$ srun -N1 -n4 -c1 --gpus-per-task=2 --gpu-bind=closest ./hello_jobstep | sort

MPI 000 - OMP 000 - HWT 000 - Node defiant-nv01 - RT_GPU_ID 0,1,2,3,4,5,6,7 - GPU_ID 0,1,2,3,4,5,6,7 - Bus_ID 0A,18,41,4A,87,90,BE,C7
MPI 001 - OMP 000 - HWT 032 - Node defiant-nv01 - RT_GPU_ID 0,1,2,3,4,5,6,7 - GPU_ID 0,1,2,3,4,5,6,7 - Bus_ID 0A,18,41,4A,87,90,BE,C7
MPI 002 - OMP 000 - HWT 001 - Node defiant-nv01 - RT_GPU_ID 0,1,2,3,4,5,6,7 - GPU_ID 0,1,2,3,4,5,6,7 - Bus_ID 0A,18,41,4A,87,90,BE,C7
MPI 003 - OMP 000 - HWT 033 - Node defiant-nv01 - RT_GPU_ID 0,1,2,3,4,5,6,7 - GPU_ID 0,1,2,3,4,5,6,7 - Bus_ID 0A,18,41,4A,87,90,BE,C7

Note

There are many different ways users might choose to perform these mappings, so users are encouraged to clone the hello_jobstep program and test whether or not processes and threads are running where intended.

NVMe Usage

..warning::: NVME usage is currently not setup on Defiant.

Each Defiant compute node has [3x] 3.5 TB NVMe devices (SSDs). To use the NVMe, users must request access during job allocation using the -C nvme option to sbatch, salloc, or srun. Once the devices have been granted to a job, users can access them at /mnt/bb/<userid>. Users are responsible for moving data to/from the NVMe before/after their jobs. Here is a simple example script:

#!/bin/bash
#SBATCH -A <projid>
#SBATCH -J nvme_test
#SBATCH -o %x-%j.out
#SBATCH -t 00:05:00
#SBATCH -p batch-gpu
#SBATCH -N 1
#SBATCH -C nvme

date

# Change directory to user scratch space (/lustre/polis)
cd /lustre/polis/<projid>/scratch/<userid>

echo " "
echo "*****ORIGINAL FILE*****"
cat test.txt
echo "***********************"

# Move file from Lustre to SSD
mv test.txt /mnt/bb/<userid>

# Edit file from compute node
srun -n1 hostname >> /mnt/bb/<userid>/test.txt

# Move file from SSD back to GPFS
mv /mnt/bb/<userid>/test.txt .

echo " "
echo "*****UPDATED FILE******"
cat test.txt
echo "***********************"

And here is the output from the script:

$ cat nvme_test-<jobid>.out
Mon May 17 12:28:18 EDT 2021

*****ORIGINAL FILE*****
This is my file. There are many like it but this one is mine.
***********************

*****UPDATED FILE******
This is my file. There are many like it but this one is mine.
defiant19
***********************

Container Usage

..warning::: Container usage is currently not setup on Defiant.

Defiant provides Apptainer v1.2.5 installed for building and running containers. Defiant also provides Podman to build container images if you only have the Dockerfile formats and can’t convert to the Apptainer format. Currently the containers that can be built with Podman is very limited, so it is recommended that you convert your Dockerfiles to the Apptainer Definition format. See documentation for that here .

Note

The container docs will continue to evolve and change as we identify better practices and more user friendly methods for using containers on Defiant to best suit the needs of the users. If something you’re trying no longer works, be sure to come back and check the docs to see if anything has changed.

Setup before Building

Users will need to set up a file in their home directory /ccsopen/home/<username>/.config/containers/storage.conf with the following content:

[storage]
driver = "overlay"
graphroot = "/tmp/containers/<user>"

[storage.options]
additionalimagestores = [
]

[storage.options.overlay]
ignore_chown_errors = "true"
mount_program = "/usr/bin/fuse-overlayfs"
mountopt = "nodev,metacopy=on"

[storage.options.thinpool]

<user> in the graphroot = "/tmp/containers/<user>" in the above file should be replaced with your username. This will ensure that Podman will use the NVMe mounted in /tmp/containers for storage during container image builds.

Build and Run Workflow

As an example, let’s build and run a very simple container image to demonstrate the workflow.

Building a Simple Image

Create a directory called simplecontainer on home or GPFS and cd into it.

Create a file named simple.def with the following contents.

Bootstrap: docker
From: opensuse/leap:15.4

%post
zypper install -y wget sudo git gzip gcc-c++ openssh hostname

Build the container image with apptainer build simple.sif simple.def.
- Apptainer builds the container image in the SIF file format. Unlike Podman, Apptainer gives you a single file for your image that you can later run as your container.

Note

Using opensuse as your From image is preferred as it does not run into issues when installing packages with the zypper (until we get to a point where all users have mappings in the /etc/subuid files which is currently a work in progress).

Running a Simple Container in a Batch Job

As a simple example, we will run hostname with the Apptainer container.

Create a file submit.sl with the contents below.

#!/bin/bash
#SBATCH -t00:10:00
#SBATCH -A csc266
#SBATCH -N2
#BATCH -P batch
#SBATCH -J simple_container_job
#SBATCH -o %x_%j.out
#SBATCH -e %x_%j.out


srun  -N2 --tasks-per-node=1 apptainer exec  simple.sif hostname

Submit the job with sbatch submit.sl. This should produce an output that looks like:
```
defiant14
defiant12
```

Note that if you are running multiple tasks per node, for example with srun -N1 --tasks-per-node=2 apptainer exec simple.sif hostname, Apptainer is running an instance of the runtime for each task i.e. the same running container is NOT shared between multiple tasks running on the same node.

Running an MPI program with an MPI image

For running a program that uses MPI, you will need to build your container image with MPICH 3.4.2 installed (and also install ROCm if you need GPU functionality). We have a prebuilt image with with MPICH 3.4.2 and ROCm 5.3.3 in /lustre/polis/stf007/world-shared/containers/opensusempich342rocm533.sif. The definition files that are used to build this image can be found on this code.ornl.gov repo . Let’s look at an example where we build a container that runs an MPI example based on this image.

Create a new directory mpiexample.

Create a file mpiexample.c with the following contents.

#include <stdio.h>
#include <mpi.h>

int main (int argc, char *argv[])
{
int rank, size;
MPI_Comm comm;

comm = MPI_COMM_WORLD;
MPI_Init (&argc, &argv);
MPI_Comm_rank (comm, &rank);
MPI_Comm_size (comm, &size);

printf("Hello from rank %d\n", rank);

MPI_Barrier(comm);
MPI_Finalize();
}

Create a file named mpiexample.def with the following contents

Bootstrap: localimage
From: /lustre/polis/stf007/world-shared/containers/opensusempich342rocm533.sif

%files
mpiexample.c /app/mpiexample.c


%post
cd /app && mpicc -o mpiexample mpiexample.c

Build the container image with apptainer build mpiexample.sif mpiexample.def.

Create a submit script submit.sl with the following contents. The submit script will launch four apptainer tasks across two nodes with MPI running, and prints their rank id the same as if the program was running on bare metal.

#!/bin/bash
#SBATCH -t00:10:00
#SBATCH -A csc266
#SBATCH -N2
#SBATCH -J mpiexample
#SBATCH -o %x_%j.out
#SBATCH -e %x_%j.out

module  load amd-mixed
module load craype-accel-amd-gfx908
module load cray-mpich-abi

export MPICH_SMP_SINGLE_COPY_MODE=NONE

export APPTAINERENV_LD_LIBRARY_PATH="$CRAY_MPICH_DIR/lib-abi-mpich:$CRAY_MPICH_ROOTDIR/gtl/lib:/opt/rocm/lib:/opt/rocm/lib64:$CRAY_LD_LIBRARY_PATH:$LD_LIBRARY_PATH:/opt/cray/pe/lib64:/usr/lib64/libibverbs"
export APPTAINER_CONTAINLIBS="/usr/lib64/libjansson.so.4,/usr/lib64/libcxi.so.1,/usr/lib64/libjson-c.so.3,/usr/lib64/libdrm_amdgpu.so.1,/usr/lib64/libdrm.so.2,/lib64/libtinfo.so.6,/usr/lib64/libnl-3.so.200,/usr/lib64/librdmacm.so.1,/usr/lib64/libibverbs.so.1,/usr/lib64/libibverbs/libmlx5-rdmav34.so,/usr/lib64/libnl-route-3.so.200"
export APPTAINERENV_LD_PRELOAD=$CRAY_MPICH_ROOTDIR/gtl/lib/libmpi_gtl_hsa.so.0:
export APPTAINER_BIND=/usr/share/libdrm,/var/spool/slurm,/opt/cray,${PWD},/etc/libibverbs.d,/usr/lib64/libibverbs/



srun  -N2 -n4 --tasks-per-node 2 apptainer exec  --workdir `pwd` mpiexample.sif /app/mpiexample

You should get output that looks like

<several INFO messages. Can be ignored>
...
Hello from rank 1
Hello from rank 0
Hello from rank 2
Hello from rank 3

You can view the definition files used to build the base image at the code.ornl.gov repository in the defiant directory. You can build these yourself (if you want slightly modify it) by cloning the repository and running ./build in the individual directories in the repository.

Getting Help

If you have problems or need helping running on Defiant, please submit a ticket by emailing help@olcf.ornl.gov.

Known Issues

NVME usage is not setup currently.
Container usage is not setup currently.

Defiant Quick-Start Guide

System Overview

Defiant Nodes

GPU Compute Nodes

CPU Compute Nodes

Login Node

System Interconnect

File Systems

GPUs

Connecting

Data and Storage

NFS Filesystem

Lustre Filesystem (Polis)

Programming Environment

Environment Modules (Lmod)

General Usage

Searching for Modules

Compilers

Cray Programming Environment and Compiler Wrappers

MPI

Compiling

MPI

GPU-Aware MPI

1. Compiling with the Cray compiler wrappers, cc or CC

OpenMP

OpenMP GPU Offload

Running Jobs

Slurm Workload Manager

Batch Scheduler and Job Launcher

Batch Scripts

Interactive Jobs

Single Command (non-interactive)

Common Slurm Submission Options

Other Common Slurm Commands

Slurm Compute Node Partitions

Process and Thread Mapping

CPU Mapping

2 MPI ranks - each with 2 OpenMP threads

GPU Mapping

Mapping 1 task per GPU

Mapping multiple MPI ranks to a single GPU

NVMe Usage

Container Usage

Setup before Building

Build and Run Workflow

Building a Simple Image

Running a Simple Container in a Batch Job

Running an MPI program with an MPI image

Getting Help

Known Issues

1. Compiling with the Cray compiler wrappers, `cc` or `CC`