ABOUT THIS AWARD

Endowed in memory of George Michael, one of the founding fathers of the SC Conference series, the ACM IEEE-CS George Michael Memorial Fellowships honor exceptional PhD students throughout the world whose research focus areas are in high performance computing, networking, storage, and large-scale data analysis. ACM, the IEEE Computer Society, and the SC Conference support this award.

Fellowship winners are selected each year based on overall potential for research excellence, the degree to which technical interests align with those of the HPC community, academic progress to date, recommendations by their advisor and others, and a demonstration of current and anticipated use of HPC resources. The Fellowship includes a $5,000 honorarium, plus travel and registration to receive the award at the annual SC conference.

Lifflander, Solomonik Awarded George Michael Memorial HPC Fellowships for 2013

Jonathan Lifflander and Edgar Solomonik Jonathan Lifflander was recognized for his project "Scalable Algorithms for Dynamic Large-Scale Systems" and Edgar Solomonik for his project "Communication-Optimal Parallel Algorithms for Solving Physical Equations."

Jonathan Lifflander is a fifth-year PhD candidate in Computer Science at the University of Illinois, advised by Laxmikant V. Kale. He researches scalable parallel algorithms in the context of dynamic behavior that lead to highly unstructured mappings: load imbalances in irregular applications, hard system faults, scheduling polices such as work stealing and energy and power constraints. These algorithms are demonstrated to be effective on modern supercomputers, reaching beyond 100K cores.

Edgar Solomonik is a PhD candidate working on parallel numerical algorithms at University of California, Berkeley. Together with his advisor, Prof. James Demmel,he works on developing algorithms that avoid communication traffic and scale on high-performance parallel computers. As a graduate student, Edgar developed 2.5D algorithms for numerical linear algebra, which asymptotically lower communication at the cost of limited data replication. Concurrently, he engineered a distributed-memory tensor contraction library which provides key numerical abstractions to the field of high-accuracy electronic structure calculations.