hpx

elerate climate and weather codes on accelerated architectures - Quantum chemical and material science applications used for the prediction of novel materials - Optimising and wrapping sparse linear algebra and mesh codes for our finite element and finite volume users - Combining low-level optimization with task-based parallelism at extreme scale to perform fast linear algebra on the world's largest systems - Communication-avoiding parallel algorithms Our projects are open source whenever possible. Here are some of the projects we develop and contribute to: https://github.com/arbor-sim/arbor, https://github.com/electronic-structure/SIRIUS, https://github.com/STEllAR-GROUP/hpx. Also have a look at the profiles of some of our team members: https://github.com/bcumming/, https://github.com/biddisco/, https://github.com/halfflat/, https://github.com/havogt/, https://github.com/msimberg/, https://github.com/noraabiakar/. We require you to have great C++ skills and a master in computer science, computational science, mathematics or natural sciences. Ideally you would also have experience in one or more of the following: - Materials science, weather and climate, or linear algebra - Development of numerical or scientific simulation software - Development of GPU-accelerated applications using CUDA or ROCm - Release management - Performance modeling Tell us what project catches your eye and and why you'd be the right candidate in your application: https://emea2.softfactors.com/job-opening/rgum-ZPX9izjHtrEbP.... Due to the migration regulations in Switzerland the process for non-EU residents is more challenging.

e examples of projects you might work on are: - Template meta-programming and domain specific languages to accelerate climate and weather codes on accelerated architectures - Quantum chemical and material science applications used for the prediction of novel materials - Optimising and wrapping sparse linear algebra and mesh codes for our finite element and finite volume users - Combining low-level optimization with task-based parallelism at extreme scale to perform fast linear algebra on the world's largest systems - Communication-avoiding parallel algorithms Our projects are open source whenever possible. Here are some of the projects we develop and contribute to: https://github.com/arbor-sim/arbor, https://github.com/electronic-structure/SIRIUS, https://github.com/STEllAR-GROUP/hpx. Also have a look at the profiles of some of our team members: https://github.com/bcumming/, https://github.com/biddisco/, https://github.com/halfflat/, https://github.com/havogt/, https://github.com/msimberg/, https://github.com/noraabiakar/. We require you to have great C++ skills and a master in computer science, computational science, mathematics or natural sciences. Ideally you would also have experience in one or more of the following: materials science, weather and climate, or linear algebra; development of numerical or scientific simulation software; development of GPU-accelerated applications using CUDA or ROCm; release management; or performance modeling. Tell us what project catches your eye and and why you'd be the right candidate in your application: https://apply.refline.ch/845721/6509/pub/1/index.html. Due to the migration regulations in Switzerland the process for non-EU residents is more challenging.

performant (ha, used the word :) ). People know iostream isn't that performant. Could it be better? Perhaps. Is it safe? Yes! If you want perf, use the C interface directly. Is that safe to use, probably not for the general careless user. >To handle the type of speed that is being delivered in droves by the technology companies, C++ cannot be used anymore because it is inherently serial, even in massively multithreaded systems like GPUs. Well, yes but so is just about every language. People are trained to write sequentially (left to right, top to bottom), with many exceptions...but none the less, sequentially. There are very few languages that do multithreading natively. There are lots of additions/libraries to C++ that enable very nice ways to consider parallelism (including w/in standard: std::thread), outside of standard (raftlib.io,hpx(https://github.com/STEllAR-GROUP/hpx),kokkos (https://github.com/kokkos), etc.). There are lots, some are quite easy to use. C++ is inherently serial, but there is no better way to write. It is fairly easy to pull out "parallel" pieces of code to execute. It is even easier if the programmer gets quick feedback (like the icc loop profiler,etc.) on things like ambiguous references and loop bounds that can be fixed quickly. Interesting read, but don't agree at all.