This is the second post in our series of articles about the results of checking open-source software working with the RDP protocol. Today we are going to take a look at the rdesktop client and xrdp server.
In the era of ubiquitous AI applications there is an emerging demand of the compiler accelerating computation-intensive machine-learning code for existing hardware. Such code usually does mathematical computation like matrix transformation and manipulation and it is usually in the form of loops. The SIMD extension of OpenMP provides users an effortless way to speed up loops by explicitly leveraging the vector unit of modern processors. We are proud to start offering C/C++ OpenMP SIMD vectorization in Visual Studio 2019.
The OpenMP C/C++ application program interface was originally designed to improve application performance by enabling code to be effectively executed in parallel on multiple processors in the 1990s. Over the years the OpenMP standard has been expanded to support additional concepts such as task-based parallelization, SIMD vectorization, and processor offloading. Since 2005, Visual Studio has supported the OpenMP 2.0 standard which focuses on multithreaded parallelization. As the world is moving into an AI era, we see a growing opportunity to improve code quality by expanding support of the OpenMP standard in Visual Studio. We continue our journey in Visual Studio 2019 by adding support for OpenMP SIMD.
Visual Studio 2019 Preview 3 introduces a new feature to reduce the binary size of C++ exception handling (try/catch and automatic destructors) on x64. Dubbed FH4 (for __CxxFrameHandler4, see below), I developed new formatting and processing for data used for C++ exception handling that is ~60% smaller than the existing implementation resulting in overall binary reduction of up to 20% for programs with heavy usage of C++ exception handling.
I am Shalitha Suranga from Sri Lanka. I started Neutralinojs project with other two members as our research project at university.
These frameworks are being used to create numerous cross-platform applications. Whereas the community pointed out several unseen drawbacks of these frameworks. Large bundled application size, high memory consumption and long development workflow are the key things which were criticized through internet forums and websites , , , , . Table 1.1 shows the advantages and disadvantages of Electron/NW.js.
Table 1.1: Advantages and Disadvantages of Electron/NW,js
|Advantages of Electron and NW.js||Disadvantages of Electron and NW.js|
|Access native functions via node runtimeSingle codebase for all supported platforms Linux, Windows and macOS||High memory consumption and slowness|
|Many Node modules need to be installed|