Distributed Computing Concepts

Last updated on 2026-08-27 | Edit this page

Estimated time: 15 minutes

Overview

Questions

  • When do I need distributed-memory computing?
  • What is MPI and how does it work?
  • How do I run MPI programs on Sagehen HPC?

Objectives

  • Understand when MPI is appropriate
  • Learn MPI core concepts: rank, communicator, send/recv
  • Understand SLURM directives for MPI jobs

When Do You Need Distributed Computing?

Callout

Use MPI (distributed computing) when ALL of these apply: 1. Problem is too large for one node (> 128 cores or 512GB memory) 2. Tight communication loop between processors 3. Iterative algorithms with frequent synchronization 4. Computation time far exceeds communication time

Don’t use MPI if: - Tasks are embarrassingly parallel (use job arrays) - Problem fits on single node (use multiprocessing) - Communication overhead would exceed speedup benefit

MPI makes sense for: Large-scale physics simulations, molecular dynamics, coupled multi-physics problems.

MPI is overkill for: Parameter sweeps, processing independent files, single-node analyses.

MPI Core Concepts

Rank: Each process has a unique ID. Communicator: Group of processes that can communicate (default: COMM_WORLD). Send/Recv: Explicit message passing between ranks.

Basic Operations

PYTHON 

from mpi4py import MPI

comm = MPI.COMM_WORLD
rank = comm.Get_rank()     # What's my process ID?
size = comm.Get_size()     # How many total processes?

comm.send(data, dest=1)           # Send to rank 1
data = comm.recv(source=0)        # Receive from rank 0
data = comm.bcast(data, root=0)   # Rank 0 sends to all
total = comm.reduce(my_value, op=MPI.SUM, root=0)  # Sum across all
comm.Barrier()                    # Wait for all to reach this point

SLURM Directives for MPI Jobs

Single Node, Multiple Ranks

BASH 

#!/bin/bash
#SBATCH --ntasks=8           # 8 MPI processes
#SBATCH --cpus-per-task=1
#SBATCH --nodes=1
#SBATCH --mem=16G

srun python3 my_mpi_program.py

Multiple Nodes

BASH 

#!/bin/bash
#SBATCH --ntasks=32          # 32 MPI processes total
#SBATCH --nodes=4            # Across 4 nodes
#SBATCH --ntasks-per-node=8  # 8 processes per node
#SBATCH --mem-per-cpu=2G

srun python3 my_mpi_program.py

Key: Use srun to launch MPI programs (not mpirun). SLURM manages distribution.

InfiniBand Advantage

Sagehen has InfiniBand 100Gb/s interconnect: - 100 Gb/s bandwidth (vs. 1-10 Gb/s Ethernet) - Sub-microsecond latency - MPI programs scale much better on fast interconnects

Challenge

Challenge

When to Use MPI?

For each scenario, decide: job arrays, multiprocessing, or MPI?

  1. 500 independent simulations, 1 hour each
  2. 3D fluid simulation on a grid with neighbor dependencies, needs 256 cores
  3. Processing 10,000 images independently
  1. Job arrays – embarrassingly parallel, no communication needed
  2. MPI – tightly coupled, needs more than one node (256 > 128 cores/node)
  3. Job arrays – each image is independent
Key Points
  • Distributed-memory systems have separate memory per node
  • MPI enables explicit message passing between processes
  • Use MPI only for tightly-coupled algorithms across multiple nodes
  • Launch MPI with srun under SLURM
  • InfiniBand makes MPI practical on Sagehen