Symbolic Parallelization of Nested Loop Programs, Softcover reprint of the original 1st ed. 2018

This book introduces new compilation techniques, using the polyhedron model for the resource-adaptive parallel execution of loop programs on massively parallel processor arrays. The authors show how to compute optimal symbolic assignments and parallel schedules of loop iterations at compile time, for cases where the number of available cores becomes known only at runtime. The compile/runtime symbolic parallelization approach the authors describe reduces significantly the runtime overhead, compared to dynamic or just?in-time compilation. The new, on?demand fault?tolerant loop processing approach described in this book protects loop nests for parallel execution against soft errors. 

Introduction.- Fundamentals and Compiler Framework.- Symbolic Parallelization.- Symbolic Multi‐level Parallelization.- On‐demand Fault‐tolerant Loop Processing.- Conclusions.

Alexandru-Petru Tanase is a researcher at the Department Of Computer Science, Friedrich Alexander University Erlangen-Nürnberg (FAU), since 2011. He defended his Phd Degree on the topic of “Symbolic Parallelization of Nested Loop Programs” in September, 2017. He received his Diploma Degree in Computer Engineering In 2006 and Master Degree in Parallel Processing in 2008 from ULBS University, Romania. His main research interests include high level synthesis, programmable hardware accelerators, the design of massively parallel architectures, mapping methodologies for domain-specific computing, and architecture/compiler co-design.

General Chair of DATE 2018