Michela heads the Global Computing Lab (GCLab), which focuses on high performance computing (HPC) and its application to the sciences.
Her research interests include software applications and their advanced programmability in heterogeneous computing (i.e., multi-core platforms and GPUs); cloud computing and volunteer computing; and performance analysis, modeling and optimization of multi-scale applications.
The following is an excerpt from our interview (read the complete Spotlight here).
NVIDIA: Michela, what is the mission of the Global Computing Lab at the University of Delaware?
Michela: We are engaged in the design and testing of efficient computational algorithms and adaptive scheduling policies for scientific computing on GPUs, the Cloud, and Volunteer Computing.
Interdisciplinary research with scientists and engineers in fields such as chemistry and chemical engineering, pharmaceutical sciences, seismology, and mathematics is at the core of our activities and philosophy.
NVIDIA: Tell us about your work with GPUs.
Michela: My team’s work is all about rethinking application algorithms to fit on the GPU architecture in order to get the most out of its computing power, while preserving the scientific accuracy of the simulations. This has resulted in many exciting achievements!
NVIDIA: Can you provide an example?
Michela: My group and I were the first to propose a completely-on-GPU PME (Particle Mesh Ewald) code for MD (molecular dynamics) simulations. We achieved that goal by changing the traditional way researchers algorithmically look at charges in long-range electrostatics and their interactions.
With our code empowered with the PME components, we could move the traditional scale for studying membranes like DMPC lipid bilayers from membranes on the order of 72 lipid molecules (17,004 atoms) to 16-times larger membranes of 1,152 lipid molecules (27,3936 atoms) in explicit solvent [see Figure 1].