search for: vechev

...pload the entire source to GitHub (both our new/modified files as well as the rest of the LLVM files). What would be the best way (legal w.r.t to LLVM license, which is NCSA) to publish the entire source ? Can we simply distribute the entire source under Apache ? thanks a lot, Martin ---- Martin Vechev Assistant Professor, Department of Computer Science, ETH Zurich Head, Software Reliability Lab: http://www.srl.inf.ethz.ch/ ----

...er, libquantum, and various > linear-algebra kernels (we use gemm-specific optimizations). The > first two require additional flags to be enabled. On the topic of performance, this paper might be of interest: Fast Polyhedral Abstract Domain Gagandeep Singh, Markus Püschel, Martin Vechev http://www.srl.inf.ethz.ch/papers/POPL17-Polyhedra.pdf > Our experimental results demonstrate massive gains in both space and > time: we show end-to-end speedups of two to five orders of magnitude > compared to state-of-the-art Polyhedra implementations as well as > significant memory...

[add subject] Dear LLVM community, hope all of you had a good start into 2018 and a quiet branching of LLVM 6.0. With the latest LLVM release out of the way and a longer development phase starting, we would like to restart the process of including Polly and isl into core LLVM to bring changes in early on before the next LLVM release. Short summary: * Today Polly is already part of each LLVM

...gt; linear-algebra kernels (we use gemm-specific optimizations). The >> first two require additional flags to be enabled. > > On the topic of performance, this paper might be of interest: > > Fast Polyhedral Abstract Domain > Gagandeep Singh, Markus Püschel, Martin Vechev > http://www.srl.inf.ethz.ch/papers/POPL17-Polyhedra.pdf > >> Our experimental results demonstrate massive gains in both space and >> time: we show end-to-end speedups of two to five orders of magnitude >> compared to state-of-the-art Polyhedra implementations as well as &...

...show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers. Bio: Pavol is a 3rd year PhD student at the ETH Zurich advised by Martin Vechev. His research is spanning the areas of programming languages, program analysis and machine learning. In particular, he focuses on creating new kinds of techniques and tools based on probabilistic learning from large codebases consisting of millions lines of code. Such tools can help us to solve i...