Tag Archives: applications


Agriculture – Self-driving tractors, harvesters and other agricultural vehicles could help solve several challenges facing farmers. For instance, they could help address shortages of farm labor by performing some dangerous or repetitive tasks. Self-driving equipment may also allow for more precise applications of seeds, water and chemicals which could boost crop yields while reducing costs, waste and environmental impacts. Autonomous greenhouses and farms may even one day produce year-round crops and address issues like food insecurity in some regions.

Warehousing and logistics – The controlled, indoor environments of warehouses and distribution centers are actually very well-suited for autonomous vehicles to shuttle goods between storage areas and loading docks. Self-driving forklifts, carts and trucks could help address labor shortages, improve efficiency by reducing wait times, and offer scheduling flexibility beyond human limitations. They may lower operating costs by reducing accident risks and allowing warehouses to operate 24/7 without fatigue or safety issues. Self-driving could optimize routes and space utilization to squeeze more capacity out of existing warehouse footprints.

Manufacturing – Factory floors represent another controlled environment where autonomous vehicles and mobile robots could take over material handling, transporting workpieces between machines and assembly stations. This application of self-driving could significantly boost production outputs while minimizing human exposure to unhealthy, monotonous or physically demanding tasks. Precision positioning and navigation could make assembly and manufacturing more consistent and reliable. Management of inventory would also become more optimized. In many ways, modern factories already demonstrate what high levels of autonomy may look like.

Mining – Hazardous or difficult environments underground like mines could see major benefits from autonomous vehicles and robots to move materials, inspect tunnels and make deliveries of supplies/tools. This application would help protect human workers from dangers like tunnel collapses, explosive gases, contamination and fatigue that are inherent challenges in mining work. Productivity may be increased and costs reduced by continuous 24/7 operations unhindered by shifts or human work hour limits. Remote operation technologies could even allow some mining activities from the surface without any need to send people underground at all.

Defense and security – Military forces already deploy a wide range of autonomous systems from missile defense to drones and are likely to incorporate more self-driving capabilities for patrols, transport, bomb disposal robots and other hazardous duties. Autonomous vehicles also offer significant advantages for security tasks like perimeter monitoring, area surveillance/detection and responding rapidly to emergencies on large sites or campuses. They could help address threats while minimizing risks to human personnel. Autonomous guards and sentries may even help secure infrastructure in risky areas or situations where deploying people may not be feasible.

Space exploration – The ability for high levels of autonomous sensing, navigation and decision making will perhaps prove most pivotal for space travel and operations. Robotic and self-driving vehicles will likely play a huge role in construction, maintenance and science work on the moon, Mars or other planetary surfaces where round trip communication times are too long to rely solely on human teleoperation. Their capabilities to perform basic functions without direct control opens up the potential for cooperative human-machine exploration farther into the solar system than would otherwise be possible.

These represent just some of the major opportunity areas where self-driving technologies could significantly improve current processes and working conditions if safety, regulations and public acceptance can be adequately addressed. Their common themes tie back to addressing labor challenges, improving productivity and efficiency gains, minimizing human exposure to safety risks and expanding what can be achieved remotely or in hazardous locations. As autonomy improves, new applications will surely also emerge that have not yet even been conceived. The impact of these technologies promises to ripple throughout many sectors of the economy and society.


AI is fundamentally transforming drug discovery and development. By analyzing massive datasets and identifying patterns too complex for humans to see, AI technologies like machine learning, deep learning, and natural language processing are accelerating every step of the drug development process from target identification to clinical trials. Here are some key examples:

Target Identification – AI can analyze genomic, proteomic, clinical, and molecular data to discover new biological targets for drug development. By finding previously unknown correlations in massive datasets, AI identifies novel targets that may help treat diseases. One example is using deep learning to analyze gene expression patterns and identify new target genes linked to cancer subtypes.

Virtual Screening – Companies use deep neural networks to screen huge virtual libraries of chemical structures to predict whether they may bind to and activate/inhibit specific biological targets linked to diseases. This enables in silico screening of millions of potential drug candidates without costly wet-lab experiments. It helps researchers prioritize actual compounds to synthesize and test in the lab.

De Novo Drug Design – Going beyond screening existing chemical structures, AI can also generate entirely new chemical structures designed to target specific proteins from scratch. Deep learning models are trained on properties of chemicals known to hit or avoid targets. They can then generate novel designed molecules predicted to engage disease targets in ways worth pursuing through synthesis and testing.

Toxicity Prediction – Predicting potential toxicity of drug candidates early in development could eliminate many unsafe or ineffective compounds from consideration before wasting resources on prolonged clinical trials. AI models analyze patterns in datasets correlating molecular structure to toxicity outcomes. Their predictions help researchers focus on potentially safer lead candidates.

Synthesis Planning – Given a desired molecular structure, AI planning tools can map feasible chemical reaction routes and multistep syntheses to produce that target molecule in the lab. Deep learning models trained on published synthetic methods find highest probability pathways for chemists to pursue in their work. This accelerates drug candidate synthesis.

Clinical Trial Optimisation – AI helps plan clinical trials more efficiently. Machine learning algorithms analyze data from past trials to predict the best treatment regimens, biomarker strategies, likely adverse events, and optimal trial population enrichment approaches to give new candidates their best chance of success.

Predicting Drug Responses – Using huge datasets correlating genetic profiles, clinical metadata, and treatment outcomes, AI models predict how individual patients may clinically respond to different drugs, personalized regimens like optimal dosing, and likelihood of adverse reactions or acquired resistance. This enables more targeted, predictive “precision medicine.”

Side Effect Discovery – Natural language processing of clinical literature and FDA records for existing drugs builds knowledge graphs mapping drugs to observed side effects along with their severity and population impacts. Comparison to drugs with similar structures helps AI systems hypothesize potential side effects during development for mitigation strategies.

Repurposing Existing Drugs – AI powered analyses detect previously unknown relationships between biological targets, diseases and existing drugs. Their indications reveal unforeseen therapeutic opportunities for already-approved drug candidates. This shortcuts years of development and gets potentially life-saving treatments to patients much faster through lower-risk trials validating new uses.

While drug discovery has long been an empirical, trial-and-error process, AI is now enabling a transformation towards data-driven discovery and development. By finding novel patterns in ever-growing biomedical datasets, AI technologies have the potential to drastically accelerate each step from target identification to clinical use, helping more new therapies reach patients sooner to alleviate disease burdens worldwide. Of course, as with any new approach there remain obstacles to widespread implementation still requiring ongoing collaborations between technology developers, researchers and regulators. But the transformative impacts of AI on pharmaceutical R&D are already abundantly clear, promising to revolutionize how new treatments are discovered and delivered to those in medical need.


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.