High-Performance software usually requires the use of a low-level language such as C or C++ with various message passing or shared memory paradigms such as MPI or OpenMP. With the popularity of Python in the world of Data Science a plethora of tools have evolved to make data analysis easier and reduce the mean time to discovery. A lot of these frameworks such as Python Numba can be used for Scientific Computing as well. However, when optimizing compute-intensive portions of the code a language such as C or C++ is desirable. This talk covers how high-performance mixed-language software can be enabled by seamlessly dispatching computationally intensive portions of the code to be executed using the Xtensor library in C++.
I am currently employed at Virginia Tech as a Computational Scientist since September 2014. My research interests lie in Numerical Methods, Computational Science and Machine Learning using High-Performance Computing with traditional and novel accelerator technologies. Since joining VT, I have also had a chance to hone my skills at Data Visualization. My background during my Doctoral work at the SimCenter: National Center for Computational Engineering, was in CFD and Electromagnetics using the Finite-Element Method.
I have a Doctorate in Computational Engineering from the University of Tennessee and a Masters in Electrical Engineering from the The Pennsylvania State University.
Please find more about me at srijithr.gitlab.io and a web resume at https://srijithr-newresume.netlify.com/
