Lawrence Livermore National Laboratory (LLNL) scientists and engineers have earned three awards among the top 100 inventions worldwide.
The trade journal R&D World Magazine recently announced the winners of the awards, often called the “Oscars of innovation,” recognizing new commercial products, technologies and materials that are available for sale or license for their technological significance.
With this year’s results, the Laboratory has now collected a total of 182 R&D 100 awards since 1978. Submitted through LLNL’s Innovation and Partnerships Office (IPO), these awards recognize the impact that Livermore innovation, in collaboration with industry partners, can have on the U.S. economy as well as globally. They will be showcased at the 62nd R&D 100 black-tie awards gala on Nov. 21 in Palm Springs, California.
This year’s LLNL R&D 100 awards include a spectral beam combining optic that enables a single, high-power beam with unparalleled compactness and damage resistance; an open-source memory-mapping library with increased power and flexibility; and a user-level file system for high performance computing systems.
“The R&D 100 awards highlight the most innovative, game-changing technologies, and it is wonderful to see LLNL teams being recognized,” Lab Director Kim Budil said. “Having three projects selected for this honor shows clearly the high degree of excellence and ingenuity our scientists and engineers bring to creating impactful solutions to important challenges.”
Optics breakthrough improves laser performance
Demand for high-power laser sources with diffraction-limited beam quality is increasing as material processing techniques such as marking, cutting, welding and drilling often require a laser beam that can transmit over distance while maintaining excellent quality, to minimize undesired beam spreading.
The increased demand is leading to a significant scaling effort for laser systems’ output power to reach hundreds of kilowatts, and even megawatts. However, challenges to scaling the output power include removing heat waste, maintaining beam quality and avoiding damage to output optics.
The Extreme-power, Ultra-low-loss, Dispersive Element (EXUDE) Elite optical element addresses these challenges by concentrating light from multiple lasers with different wavelengths into a single, high-power beam with unparalleled compactness and damage-resistance.
EXUDE Elite significantly improves upon the original EXUDE technology (winner of a 2014 R&D 100 award), which made it possible to use Spectral Beam Combining (SBC) for near-diffraction-limited quality laser systems with first-ever output powers approaching megawatt levels. EXUDE Elite combines fiber laser beams via transmission through a fused silica optic, which is achieved in a less expensive, more compact system with a 100-fold improvement to the damage threshold.
EXUDE Elite is a breakthrough that suggests a paradigm shift in high-power laser technology. The lower price and improved overall optics performance of EXUDE Elite offer the opportunity for wider access to SBC for industrial laser systems, ushering the laser power-scaling effort forward on a larger scale than ever before.
EXUDE Elite was developed by a team of LLNL researchers led by Diffractive Optics Group Leader Hoang Nguyen and includes Michael Rushford, Brad Hickman, Candis Jackson, James Nissen and Sean Tardif.
High-performance memory mapping library
Supercomputing and high-performance computing (HPC) are critical for enabling and accelerating scientific research. Accompanying the exponential growth in compute power is a complex and deep memory hierarchy, which creates inefficiencies when moving data from its storage location to the processor. Because of this, supercomputer applications face large, complex data problems from both the computer system (complex hierarchy, memory, and storage) and the application workload.
To address this, a team of LLNL computer scientists developed the UMap user-level library as part of the U.S. Department of Energy’s Exascale Computing Project, specifically the Argo Project, in which UMap is a key solution for deep and complex memory and storage systems.
UMap offers a high-performance, application-configurable, unified memory-like interface to diverse datastores located across memory-storage hierarchical levels or even across a network. It is purpose-built for high performance through a highly optimized and configurable design and is recognized as a leading solution for memory mapping diverse and large datastores.
Freely available as open source, UMap is significantly aiding the scientific community and has been adopted in many high-impact scientific applications in industry, academia and national labs.
The UMap team is led by LLNL computer scientist Maya Gokhale, a distinguished member of the technical staff (DMST), and includes current team members Marty McFadden, Elena Green, Roger Pearce, Keita Iwabuchi and Karim Youssef as well as former Lab employee Ivy Peng.
Speeding simulation modeling for impact
When HPC applications are used to simulate real-world phenomena, the results from predictive models can be used by policy makers to inform important, life-saving decisions. However, scientific simulations can take hours, days or even weeks to compute due to slowdowns in the dynamic input/output (I/O) communication between the supercomputer and the file.
The UnifyFS file system enables HPC science applications to perform I/O operations many times faster than they could with traditional methods.
UnifyFS is a temporary file system that uses fast storage tiers on supercomputers to quickly store and access application data, so that applications can produce their results in less time. In addition to providing high performance I/O operations, UnifyFS is easy to use and teams can leverage it without an administrator’s help or changing their code to accommodate different types of HPC systems. Also available as open source and production-ready, UnifyFS makes I/O fast and easy so users can focus on their science.
UnifyFS collaborators are Lawrence Livermore and Oak Ridge national laboratories as well as the National Center for Supercomputing Applications at the University of Illinois.
The LLNL part of the UnifyFS team is led by Lab computer scientist Kathryn Mohror, also a DMST, and includes current team members Cameron Stanavige, Chen Wang, Hariharan Devarajan, Ned Bass and Tony Hutter as well as former Lab employees Adam Moody and Danielle Sikich.
–Melissa Lewelling
