SBIR Phase I: Enhancing the Performance of Scientific Applications Through Intelligent Advice

Crestone Computing Llc, Monument CO

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to allow new hardware platforms to be more quickly deployed and useful software products to be more readily made available on a wide variety of hardware to satisfy the needs of their users. Additionally, researchers in science and engineering can reduce the runtime of their applications by hours or days on the machines inside high performance computing or data centers, enabling more scientific modeling and simulations to be completed within these centers, while allowing each individual scientist to concentrate more on his/her research, which in turn may more quickly benefit society. For example, in the defense industry, many contracting companies develop Monte Carlo simulation software to predict outcomes from disasters. Increasing the throughput of these simulations would allow for more simulation results to be analyzed and tested, thereby resulting in better predictions and handling of disastrous situations. This Small Business Innovation Research Phase I project is unique in its automated connection between existing performance modeling and prediction tools, and pattern-driven compiler optimization. Novel runtime monitoring and modeling techniques will be developed to automatically map performance bottlenecks discovered by existing performance analysis tools to potential opportunities of source code optimizations. Such opportunities again will be used to guide pattern-driven compiler optimizations, particularly to enhance the performance of finite element methods on both GPGPUs and multi-core/many-core CPUs. Deep learning neural networks will be used to automate the runtime behavior classification of computations. The new pattern-driven specialization of compiler optimizations will enable general-purpose compiler techniques to be aware of the higher-level semantics of library abstractions (e.g., data structures and algorithm abstractions) and allow them to be collectively customized and coordinated to attain the best performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.