Understanding Threads and CPUs per Task in Slurm

When submitting jobs to a Slurm-managed cluster, understanding the difference between threads and CPUs per task is crucial for optimizing your job's performance and efficient use of cluster resources.

Key Concepts

1. CPU (Core)

In Slurm terminology, a CPU typically refers to a physical core on a processor. Each CPU can execute a single thread of instructions at a time (ignoring hyperthreading for simplicity).

2. Task

A task in Slurm is essentially a process. It's a running instance of a program that may use one or more CPUs.

3. Thread

A thread is the smallest unit of processing that can be scheduled by an operating system. A single task (process) can have multiple threads, which can run concurrently on different CPUs.

Slurm Resource Allocation Options

Option	Description
--ntasks	Number of tasks (processes) to run
--cpus-per-task	Number of CPUs (cores) allocated to each task
--threads-per-core	Number of threads to use per core (relevant for hyperthreading)

Threads vs CPUs per Task

Using More Threads

When you increase the number of threads in your program:

It allows for more parallel execution within a single task (process).
Threads share the same memory space, making communication between threads faster.
Ideal for programs that are designed to use multi-threading (e.g., OpenMP programs).
The number of threads is typically controlled by the program itself or environment variables (e.g., OMP_NUM_THREADS).

Using More CPUs per Task

When you increase the number of CPUs per task in Slurm:

It allocates more physical cores to each task (process).
Allows for true parallel execution of threads on separate cores.
Necessary for multi-threaded programs to utilize multiple cores effectively.
Controlled by the --cpus-per-task option in Slurm.

Examples

1. Single-threaded Program

#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1

./my_single_threaded_program

This allocates one CPU for a single task, suitable for a program that doesn't use threading.

2. Multi-threaded Program (e.g., OpenMP)

#SBATCH --ntasks=1
#SBATCH --cpus-per-task=4

export OMP_NUM_THREADS=4
./my_openmp_program

This allocates 4 CPUs for a single task, allowing an OpenMP program to use 4 threads effectively.

3. MPI Program with Threading

#SBATCH --ntasks=2
#SBATCH --cpus-per-task=4

export OMP_NUM_THREADS=4
mpirun ./my_hybrid_mpi_openmp_program

This runs 2 MPI tasks, each with 4 CPUs, suitable for a hybrid MPI/OpenMP program.

Key Considerations

Match --cpus-per-task to the number of threads your program will use for optimal performance.
Be aware of the total resources you're requesting (ntasks * cpus-per-task) to ensure it doesn't exceed node capabilities.
For programs that don't control their own threading, you may need to set environment variables (like OMP_NUM_THREADS) to match --cpus-per-task.
Some programs may benefit more from multiple tasks (MPI) rather than multiple threads, depending on their design.

Understanding these concepts allows you to efficiently allocate resources for your specific computational needs, optimizing both performance and cluster utilization.