26TH Open Systems Days
Croatian Linux Users’ Conference

18 - 19 APRIL 2019

FER, Zagreb, Croatia

High performance cross platform libraries with .NET Core - dvorana A-211


.NET Core in cloud and on the Edge (mobile and IoT) forced Microsoft and Xamarin to invest in performance aspects of .NET Core. Recent changes in .NET Core added few new managed classes that wrap pointers and memory management with thin layer and expose those low level APIs to managed .NET Core world. First experiences show extreme gains in speed and low memory usage without loosing the comfort of managed world. C# and other .NET Core CLR languages are getting (performance wise) closer to C and C++.

- Attendees should bring their own laptops (Linux with .NET Core installed)
- There will be no power outlets available so please charge your laptop
- Duration: 2 hours

Desirable installation/configuration:
- Linux (mandatory)
- .NET Core (mandatory)
2.2, https://dotnet.microsoft.com/download/dotnet-core/2.2
3.0-preview, https://dotnet.microsoft.com/download/dotnet-core/3.0
- IDE - MonoDevelop (nice to have)
- IDE - IntelliJ Rider (for rich kids)
- editor[s] (Visual Studio Code - recommended, vi, emacs,...)






Saša Bajtl

Currently working in Danieli Systec in Labin on Xamarin forms IOT app which have responsibility to manage smart devices for company Gorenje d.d. Have some expirience with Android. Dotnet developer on xplat library (with @moljac) and xamarin developer in free time. Fall in love with Linux :), basic linux user.


Miljenko Cvjetko

XPlat freak. Working with Mono on Linux since 2005. Today building and deploying ASP.net Core Web Apps mostly
on Linux in Azure. From time to time to relax does compiling/building Visual Studio on Linuxes/Linuces/Linuxii.


Darko Katović

Assistant Professor of Quantitative Methods at the Department of Kinesiological Anthropology and Methodology at the Faculty of Kinesiology, University of Zagreb. More recent research interests ranging from statistical methods for complex and high-dimensional data and machine learning to interdisciplinary research in Kinesiology and Bioinformatics.


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