Climate and weather modeling: Some of the most well-known MPI applications are used for modeling global and regional climate patterns as well as forecasting weather. Examples include NCAR’s Community Atmosphere Model (CAM), NASA’s Goddard Earth Observing System Model (GEOS), NOAA’s Weather Research and Forecasting (WRF) model, and EC-Earth used by European climate institutes. These models break the global domain into sections that can be run simultaneously across many nodes, with MPI used to pass boundary data between sections during runtime. Accurate climate and weather prediction is crucial and requires using massive supercomputing clusters with tens of thousands or more cores.
Computational fluid dynamics (CFD): Simulating fluid flows around objects is important for engineering applications like aircraft and vehicle design. CFD codes that use MPI include OpenFOAM, ANSYS Fluent, and Star-CCM+. These break the simulation domain into subdomains that can be computed in parallel. Core tasks like calculating pressures, velocities, and temperatures across mesh points require frequent inter-process communication with MPI. Applications include modeling aerodynamics, combustion, heat transfer, and more. CFD simulations can utilizes massive core counts on today’s largest supercomputers.
Materials modeling: Understanding material properties and behavior at an atomic level drives research in materials science, physics, and chemistry. Popular molecular dynamics codes that employ MPI include LAMMPS, GROMACS, NAMD, and VMD. These simulate collections of atoms and molecules over time using inter-atomic potentials. The simulation box containing atoms is split among processes, with MPI used to handle interactions across process boundaries. This allows modeling extremely large systems with billions of atoms for long time periods to capture phenomena like phase changes, self-assembly, and protein folding. Understanding new materials often relies on national-scale HPC resources.
Astrophysics simulations: Modeling phenomena in astrophysics and cosmology requires extreme computational capabilities. Examples of MPI-based codes include Enzo for cosmological simulations, FLASH for astrophysical hydrodynamics, and GADGET for cosmological structure formation. These divide the spatial domain into smaller subvolumes assigned to processes. As the simulation progresses, processes bordering subvolumes must coordinate across inter-process boundaries with MPI to handle gravity calculations, fluid interactions, and other physics. Following the evolution of the universe and modeling astronomical phenomena demands exascale machines with immense parallelism.
NuComputational genomics: As genome sequencing abilities advance, analyzing and understanding the massive amounts of genomic and genetic data produced requires supercomputing. BWA-MEM and Bowtie2 use MPI to align DNA sequences to a reference genome across many nodes to accelerate this core bioinformatics task. Similarly, simulations exploring protein-folding, molecular interactions, and other genetic phenomena employ MPI frameworks like GROMACS to enable exascale-level biomolecular modeling. Genomics and personalized medicine continue to drive enormous data growth and computational demands across biomedicine.
The above are just a sampling of major HPC application domains that leverage MPI for its ability to partition large parallel workloads and coordinate processes across many thousands or more processing elements. MPI enables solving problems at massive scale in fields as diverse as weather/climate modeling, materials development, biological and biomedical discoveries, and advancing fundamental science. With exascale supercomputing now on the horizon, these kinds of MPI-based applications are poised to make even greater strides by pushing the limits of extreme-scale simulation.
MPI has emerged as an indispensable tool enabling high performance computing and the large-scale scientific and engineering simulations that drive innovation across numerous important domains. Whether modeling aspects of our planet, designing new materials and technologies, or advancing our understanding of nature at the most minute and vast of scales, MPI underpins some of our most computationally intensive and impactful work. This makes it a cornerstone technology propelling discovery and progress through academic research as well as applications with direct benefits to society, the economy and national interests.