DARPA

CASE STUDY

Accelerating Research Excellence

Using Julia to Address DARPA’s Technical Computing Challenges

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Quicker iterations and larger dataset exploration
Improved model reuse and cross-domain collaboration
Integrating Machine Learning with Traditional Physics

JuliaHub’s contributions to DARPA’s research programs have provided significant advancements in high-performance computing, modeling, and simulation, empowering DARPA to achieve its mission of maintaining technological superiority in defense. These solutions have enhanced DARPA’s ability to handle complex data and simulation tasks, driving faster innovation and more effective decision-making.

The Mission

The Defense Advanced Research Projects Agency (DARPA) drives breakthrough technologies for national security. Through cutting-edge research in artificial intelligence, machine learning, and high-performance computing, DARPA ensures the U.S. maintains its technological edge in defense.

DARPA has launched programs like ASKEM, DITTO, and TRIAD to tackle complex defense challenges using advanced modeling and simulation. JuliaHub has been a key contributor to these programs, delivering advanced tools and computational frameworks built on the Julia programming language.

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Challenges

In the context of the ASKEM, DITTO, and TRIAD programs, DARPA is confronted with several technical computing challenges.

Data Complexity and Scalability

Managing and analyzing vast, multidomain datasets—from biological systems to space weather—demands cutting-edge high-performance computing solutions.

Solver Optimization

With hundreds of solvers available, DARPA needs tools to automatically identify and deploy the best solvers for optimal performance in diverse scenarios.

Modeling and Simulation

Simulations for viral epidemics, semiconductor design, and more rely on complex models like ODEs, PDEs, and stochastic systems, requiring efficient and powerful computational tools.

Composability and Reusability

Modular, adaptable frameworks are crucial for reusing models across domains, reducing development time, and addressing domain-specific challenges effectively.

Solution

JuliaHub has delivered several advanced solutions to DARPA’s technical challenges across the ASKEM, DITTO, and TRIAD programs, leveraging the capabilities of the Julia programming language:

DARPA ASKEM Program

Proteus Sim for Automated Simulator Construction

Built on Julia’s SciML stack, ProteusSim streamlines simulator generation across domains, enhancing composability and reusability.

DARPA DITTO Program

Differentiable Circuit Simulation

Under the DITTO program, JuliaHub developed tools for differentiable electronic circuit simulation. These tools enable more accurate simulation of semiconductor circuits by integrating differentiation into electronic circuit simulations, delivering higher accuracy for complex semiconductor designs using Julia’s SciML stack.

Automated Solver Selection

Optimizes performance by selecting the best solvers based on model attributes like stiffness and hardware configurations, crucial for complex simulations.

Performance Optimizations

Julia-based simulators created for DITTO deliver significant speedups, outperforming MATLAB and Python tools through advanced parallelism, solver optimization, and GPU acceleration.

Differentiable Programming Integration

Enables efficient backcasting, model calibration, and sensitivity analysis, advancing simulation accuracy and Advanced Circuit Simulation.

DARPA TRIAD Program

SciML

Juliahub developed an ML-powered framework designed to enhance the productivity, performance, and flexibility of advanced phased array RF systems. Using Julia’s powerful ecosystem, the solution introduced a novel RF stack integrating software-defined radio technology and GPU computing. This enables rapid experimentation, complex signal processing, machine learning, and calibration-free communication solutions.

Results

Transforming DARPA’s Research with JuliaHub

Faster Simulations

JuliaHub solutions deliver simulations at unmatched speeds, enabling quicker iterations and analysis of larger datasets. For example, simulators for circuit analysis and biological systems outperformed traditional tools by orders of magnitude, enabling faster iterations and explorations of larger datasets.

Enhanced Model Flexibility and Reusability

DARPA streamlined their development process, reduced redundancy and improved collaboration between research teams leveraging JuliaHub’s flexible and composable framework.

Optimized Solver Selection

JuliaHub’s automated tools provided DARPA the ability to select the most efficient solvers, leading to significant performance gains in complex simulations for viral epidemics, space weather, and semiconductor circuits.

Seamless Integration of Machine Learning

Integrating machine learning techniques like PINNs and symbolic regression improves DARPA’s ability to calibrate models and derive insights from complex data sets.

Scalability and Parallelism

JuliaHub tools empower DARPA to scale their simulations across large computational infrastructures, including GPUs and distributed clusters, enabling DARPA to handle larger, more complex simulations. This improves their ability to tackle national security challenges in real time.

Download the full case study here

Oct 22, 2024 | JuliaHub

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