Online or onsite, instructor-led live OpenMP training courses demonstrate through interactive hands-on practice how to develop parallel applications for platforms from desktops to non-shared memory systems.
OpenMP training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Glasgow onsite live OpenMP trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Glasgow
69 Buchanan St, Glasgow, united kingdom, G1 3HL
The Buchanan Street Centre is located in the heart of Glasgow, in Scotland's most famous shopping and retail district. A m...
The Buchanan Street Centre is located in the heart of Glasgow, in Scotland's most famous shopping and retail district. A major feature of this stunning large Victorian building is the impressive sandstone entrance archway giving access to four floors. Glasgow's fantastic array of shops, hotels, restaurants and bars are on the doorstep and Buchanan Street itself is home to a number of exclusive shops and recently won the Academy of Urbanism 'Great Street Award'. One of the fastest growing cities in the UK, Glasgow is Scotland's largest urban economy and is dominated by financial and business services, public administration, health and retail. It is one of Europe's 16 largest financial centres with a growing number of 'blue-chip' financial sector companies establishing significant operations or headquarters in the city. It also forms the western part of Scotland's 'Silicon Glen' hi-tech sector.
This instructor-led, live training in Glasgow (online or onsite) is aimed at software engineers who wish to develop parallel applications using OpenMP.
By the end of this training, participants will be able to:
Understand and use parallel programming with Fortran in OpenMP.
Calculate fractals in parallel to render multiple pixels and characters.
Implement vector programming with SIMD extensions for HPC systems.
Add parallel blocks for specifying shared memory parallelism.
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