Pinning

Last updated on 2025-10-31 | Edit this page

Overview

Questions

  • What is “pinning” of job resources?
  • How can pinning improve the performance?
  • How can I see, if pinning resources would help?
  • What requirement hints can I give to the scheduler?

Objectives

After completing this episode, participants should be able to …

  • Define the concept of “pinning” and how it can affect job performance.
  • Name Slurms options for memory- and cpu- binding.
  • Use hints to tell Slurm how to optimize their job allocation.

Binding / pinning:

  • --mem-bind=[{quiet|verbose},]<type>
  • -m, --distribution={*|block|cyclic|arbitrary|plane=<size>}[:{*|block|cyclic|fcyclic}[:{*|block|cyclic|fcyclic}]][,{Pack|NoPack}]
  • --hint=: Hints for CPU- (compute_bound) and memory-bound (memory_bound), but also multithread, nomultithread
  • --cpu-bind=[{quiet|verbose},]<type> (srun)
  • Mapping of application <-> job resources

Motivation

Challenge

Exercise

Case 1: 1 thread per rank mpirun -n 8 ./raytracer -width=512 -height=512 -spp=128 -threads=1 -alloc_mode=3 -png=snowman.png

Case 2: 2 thread per rank mpirun -n 8 ./raytracer -width=512 -height=512 -spp=128 -threads=2 -alloc_mode=3 -png=snowman.png

Questions: - Do you notice any difference in runtime between the two cases? - Is the increase in threads providing a speedup as expected?

  • Observation: The computation times are almost the same.
  • Expected behavior: Increasing threads should ideally reduce runtime.
  • Hypothesis: Additional threads do not contribute.

How to investigate?

You can verify the actual core usage in two ways: 1. Use --report-bindings with mpirun 2. Use htopcommand on the compute node

Challenge

Exercise

Follow any one of the option above and run for 2 threads per rank mpirun -n 8 ./raytracer -width=512 -height=512 -spp=128 -threads=2 -alloc_mode=3 -png=snowman.png

Questions: - Did you find any justification for the hypothesis we made?

Only 8 cores are active instead of 16

Explanation:

  • Eventhough we requested 2 threads per MPI rank, both threads are pinned to the same core.
  • The second thread waits for the first thread to finish, so no actual thread-level parallelization is achieved.

How to achieve?

Exercise: Understanding Process and Thread Binding

Pinning (or binding) means locking a process or thread to a specific hardware resource such as a CPU core, socket, or NUMA region. Without pinning, the operating system may move tasks between cores, which can reduce cache reuse and increase memory latency, directly diminishes performance.

In this exercise we will explore how MPI process and thread binding works. We will try binding to core, socket, and numa, and observe timings and bindings.

Challenge

Exercise

Case 1: --bind-to numa mpirun -n 8 --bind-to numa ./raytracer -width=512 -height=512 -spp=128 -threads=12 -alloc_mode=3 -png=snowman.png

Case 2: --bind-to socket mpirun -n 4 --bind-to socket /raytracer -width=512 -height=512 -spp=128 -threads=48 -alloc_mode=3 -png=snowman.png

Questions: - What is difference between Case 1 and Case 2. Any difference in performance? How many workers? - How could you adjust process/thread counts to better utilize the hardware in Case 2?

  • MPI and thread pinning is hardware-aware.
  • If the number of processes matches the number of domains (socket or NUMA), then the number of threads should equal the cores per domain to fully utilize the node.
  • No speedup in Case 2: Oversubscription occurs because we requested 4 processes on a system with only 2 sockets.
  • Threads compete for the same cores → OpenMPI queues threads and waits until other processes finish.

Best Practices for MPI Process and Thread Pinning

Difference between Binding and Mapping

Mapping is about distributing MPI ranks across hardware hierarchy which tells where your processes will be placed.

**Binding* is locking your MPI processes/threads to a specific resource which prevents from moving it around from one to another.

Mapping vs. Binding Analogy

Think of running MPI processes and threads like booking seats for a group of friends:

  • Mapping is like planning where your group will sit in the theatre or on a flight.
    • Example: You decide some friends sit in Economy, some in Premium Economy, and some in Business.
    • Similarly, --map-by distributes MPI ranks across nodes, sockets, or NUMA regions.
  • Binding is like reserving the exact seats for each friend in the planned area.
    • Example: Once the seating area is chosen, you assign specific seat numbers to each friend.
    • Similarly, --bind-to pins each MPI process or thread to a specific core or hardware unit to avoid movement.

This analogy helps illustrate why mapping defines placement and binding enforces it.

We will use --bind-to core (the smallest hardware unit) and --map-by to distribute MPI processes across sockets or NUMA or node regions efficiently.

Choosing the Smallest Hardware Unit

Binding processes to the smallest unit (core) is recommended because:

  1. Exclusive use of resources
    Each process or thread is pinned to its own core, preventing multiple threads or processes from competing for the same CPU.

  2. Predictable performance
    When processes share cores, execution times can fluctuate due to scheduling conflicts. Binding to cores ensures consistent timing across runs.

  • Best practice: Always bind processes to the smallest unit (core) and spread processes evenly across the available hardware using --map-by.
  • Example options:
    • --bind-to core → binds each process to a dedicated core (avoids oversubscription).
    • --map-by socket:PE=<threads> → spreads given number of threads as a processing element across the socket
    • --map-by numa:PE=<threads> → spreads processes across NUMA domains, assigning <threads> cores per process.
    • similarly --map-by numa:PE=<threads>
    • --cpus-per-rank <n>→ Assigns <n> cores (hardware threads) to each MPI rank - ensuring that all threads within a rank occupy separate cores.
Challenge

Exercise

Use the given best practices above for case 1: -n 8, -threads=1 and case 2: -n 8, -threads=4 and answer following questions

Questions: - How many cores does the both jobs use? - Did you get more workers than you requested? - Did you see the scaling when running with 4 threads?

  • 8 and 32
  • No.
  • Yes

Summary

Key Points

  • Always check how pinning works
    Use verbose reporting (e.g., --report-bindings) to see how MPI processes and threads are mapped to cores and sockets.

  • Documentation is your friend
    For OpenMPI with mpirun, consult the manual: https://www.open-mpi.org/doc/v4.1/man1/mpirun.1.php

  • Know your hardware
    Understanding the number of sockets, cores per socket, and NUMA regions on your cluster helps you make effective binding decisions.

  • Avoid oversubscription
    Assigning more threads or processes than available cores hurts performance — it causes contention and idle waits.

  • Recommended practice for OpenMPI
    Use --bind-to core along with --map-by to control placement and threads per process to maximize throughput.