A few words about SObjectizer and its history

SObjectizer is a rather small C++ framework that simplifies the development of multithreaded applications. SObjectizer allows a developer to use approaches from Actor, Publish-Subscribe and Communicating Sequential Processes (CSP) models. It's an OpenSource project that is distributed under BSD-3-CLAUSE license.

SObjectizer has a long history. SObjectizer itself was born in 2002 as SObjectizer-4 project. But it was based on ideas from previous SCADA Objectizer that was developed between 1995 and 2000. SObjectizer-4 was open-sourced in 2006, but its evolution was stopped soon after that. A new version of SObjectizer with the name SObjectizer-5 was started in 2010 and was open-sourced in 2013. The evolution of SObjectizer-5 is still in progress and SObjectizer-5 has incorporated many new features since 2013.

SObjectizer is more or less known in the Russian segment of the Internet, but almost unknown outside of the exUSSR. It's because the SObjectizer was mainly used for local projects in exUSSR-countries and many articles, presentations, and talks about SObjectizer are in Russian.

A niche for SObjectizer and similar tools

Multithreading is used in Parallel computing as well as in Concurrent computing. But there is a big difference between Parallel and Concurrent computing. And, as a consequence, there are tools targeted Parallel computing, and there are tools for Concurrent computing, and they are different.

Introduction

There is a story about an experience of using Actor Model in one interesting project of developing an automatic control system for a theatre. Below I'll tell my impressions, no more than that.

Ever since we announced Template IntelliSense, you all have given us great suggestions. One very popular suggestion was to have the Template Bar auto-populate candidates based on instantiations in your code. In Visual Studio 2019 version 16.1 Preview 2, we’ve added this functionality via an “Add All Existing Instantiations” option in the Template Bar dropdown menu. The following examples are from the SuperTux codebase. 

Introduction

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.

Introduction

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.

Cross-platform application development is extremely useful among software development organizations because a large end-user audience can be targeted. Earlier there were several approaches, such as writing multiple codebases per each platform, writing a single codebase using conditionals for platform selection, or using a programming language which has a cross-platform virtual machine at run-time. There were drawbacks of each like complexity of design, limited low-level accessibility and slow learning rate. Cross-platform application development with web technologies came [1] after. Electron and NW.js are most popular frameworks which allow developers to make cross-platform applications using Javascript. Basically, these popular frameworks combine embedded chromium browser and node run-time [2], [3].

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 [4], [5], [6], [7], [8]. Table 1.1 shows the advantages and disadvantages of Electron/NW.js.

Table 1.1: Advantages and Disadvantages of Electron/NW,js