Eduardo D’Azevedo has developed and optimized algorithms and application software for advanced high performance computing systems for over 25 years. He is currently the task lead for optimizing the XGC fusion application code for GPU as part of the Center for Accelerated Application Readiness (CAAR) program for ORNL’s Summit system and for Intel Xeon Phi as part of NERSC Exascale Science Applications Program (NESAP).
He has developed efficient block update algorithms for QMCPACK, DCA++, and Kronecker product methods in DMRG++. He developed the parallel external memory algorithms for dense matrix computations that take advantage of GPU acceleration. Previously, he has developed mathematical libraries and solvers such as the out-of-core and compact storage in ScaLAPACK library in support of the DOE fusion and materials program. His activities include parallel algorithm design and implementation, application performance optimization, algorithms development, and application performance modeling and prediction.