#Multithreading in C

[removeload-academy]

Multithreading in C#: Best Practices for .NET Core & Removeload

Explore Multithreading in C# with Removeload and .NET Core. Learn how to optimize performance, manage threads efficiently, and implement best practices for concurrent programming.

[yazilimveteknoloji]

Şu yayına göz atın… "Multithreading in C#:".

[lavendertowerarchives]

i think its hilarious how much of a hold FORTRAN still has on coding today. like yeah some legacy systems are never updated cuz IT support is "unaffordable" or some shit but thats not what i mean.

a lot of interfaces and standardizations are based off having backwards compatibility, and were created when FORTRAN was aging out, so C and C++ were polite as fuck and said "yknow what we'll play by your rules for a lil"

so now C style languages are the standard for lower-level languages and FORTRAN and Pascal and BASIC and shit are dead but MPI still fucks everything up by pretending people still use it on old systems. like i know its just an interface but hell let me not pass by reference for once. c std lib? pass by reference. MPI? pass by reference. pthread library? pass by my middle finger on the way to your reference. FORTRAN is dead and buried but we still play by its rules because it couldnt fucking return a value. eat my entire ass please

[sharmanilesh]

Locking in C#

C# Multithreading allows us to leverage shared spaces among various instances running same lines of code and sharing same memory location. This itself comes along with its retrospective effects, where its management is quite a headache for programmers.

To solve the management scenarios as mentioned, we use Locks. Locks are of various types and they themselves come along with their positives and negatives.

To understand the locking, we have to know what a Deadlock is, for the same consider a scenario where two kids wanted to play with the same toy and approach it together and starts fighting for the same, some important points related to this are

Since both are kids, we cannot console them after getting deadlocked for the toys as with the Threads we can’t.

Both will not be able to use that after the above scenario.

Wastage of resource (toy) as nobody is able to play.

Wastage of Parents time to console them, as the Processor time with Threads to resolve.

By looking at the above cases you must understood that we should avoid a deadlock and implement the various locking mechanism at our exposure from C#.

There are multiple things at our disposal such as Mutex, Lock objects waits etc. Hence in order to perform thing asynchronously and efficiently using Threads we always strive to avoid these condition and use optimum strategy as per our need.

[adafruit]

🎄💾🗓️ Day 10: Retrocomputing Advent Calendar - BeOS 🎄💾🗓️

BeOS, developed by Be Inc. in the mid-1990s, was an operating system for multimedia applications, multitasking, and multithreading. Initially designed for the BeBox—a proprietary hardware platform featuring dual PowerPC processors—BeOS was later ported to the Macintosh and x86 architectures. Its kernel was monolithic, and the system was primarily written in C++. It featured a 64-bit journaling Be File System (BFS), which supported metadata and allowed rapid file queries and fast performance for multimedia tasks. Despite its technical strengths, including support for symmetric multiprocessing and a responsive graphical user interface, BeOS struggled to gain market traction. Development ceased in 2001 after Palm Inc. acquired Be Inc.'s assets. BeOS continues through Haiku, an open-source project aiming for binary compatibility with BeOS R5.

A bunch of resources and links for this one!

🖥️💾🎨📀

Have first computer memories? Post’em up in the comments, or post yours on socialz’ and tag them #firstcomputer #retrocomputing – See you back here tomorrow!

[swordfright]

I'd love to hear your structural hot takes

I already rambled a lot about this here, but alright okay yes I got more. I am here to talk about bullshit and nonsense after all 😤

If we're still talking about how DSMP is a prime example of New Media, and various frameworks you might use to understand it as an example of New Media, then I think it could be useful to bring up the concept of the violence hub.

Before I get into all that I need to define another term: rhizomatic storytelling, which means a narrative that is decentralized, nonlinear, and intricately networked. Does DSMP fit into this category? I would say so, even though it doesn't necessarily fulfill all criteria. It's definitely decentralized (I've already talking a bit about how it's difficult to pin down the parameters of the "core text" when it comes to MCYTRP) and it's definitely a network of little stories coming together to form a cohesive(ish) narrative. But one thing DSMP isn't is nonlinear. It's a very linear story, in the same way that professional wrestling (which is famously fictionalized) is very linear. It's been pointed out by fans that the storylines in pro wrestling pass at the same rate as time passes in the real world - which is pretty unique! You can't say that about most stories, even most forms of serial fiction! But, interestingly, DSMP is the same: time in the story passes at more or less the same rate as time passes IRL, with the exception of periods spent in limbo. This raises some interesting questions about who determines linearity in a prosumptive (co-created by writers and audiences) piece of media. If you're looking at DSMP from the perspective of the CCs, it's a linear narrative in the ways I've just described. But if you look at it from the perspective of the audience (who are given the option to switch between streams, watch live, watch VODs afterwards, watch VODs out-of-order, or refuse to watch some streams/VODs altogether), then it can be - and often is - a very non linear story. In a straightforward narrative where the creators create and the consumers consume, this isn't even a question. But in the case of something like DSMP, which is a collaboration by creators and audiences, those lines of authority blur and we're left wondering whose timeline we're on.

So let's say, for the sake of this argument, that DSMP is a rhizomatic story. It doesn't check off all the boxes, but I would still say it qualifies for reasons I hope I've outlined above in at least a semi-comprehensible way. Well, there's this framework that's been developed for understanding these types of non-linear, decentralized stories: the violence hub.

The violence hub was proposed as a way of furthering our understanding of these participatory “multithreaded” stories that offer many voices at once without giving any particular one of those voices the final word.

Personally, I think DSMP is sort of the perfect example of a violence hub story, obviously because of the way it's constructed (talked about this a lot already here so I won't rehash it rn) but also because, quite frankly, it is a violent story. DSMP is bleak as fuck. Warfare, conflict, and abuse are core motifs. Like, even in a fandom as divided as this one, it's not controversial to say that DSMP is a story that's fixated on suffering, and that fixation is one of its main selling points. Not all fans are interested in those parts of the story, of course, but I think it's safe to say a lot of fans find DSMP's treatment of suffering pretty compelling. This is why people are insane about Exile and Pandora and Pogtopia and, and, and. You get the picture.

The violence hub is also a useful way of looking at not just how the story is structured, but how it is consumed. When fans talk about a violent incident in the story, whether it's Exile or c!Dream's imprisonment or something else, oftentimes we talk about these events in relation to other characters' perspectives and POVs. What's c!Tubbo up to during c!Tommy's exile? Who does or doesn't try to visit Logstedshire, and at what points in the Exile timeline? What's c!Dream doing during the Exile period when he's not at Logstedshire? This interactivity with the incident (Exile) through various other POVs deepens viewers' understandings of that incident, what it means to the characters involved, what it means in the larger narrative, and most importantly - what it means to us, the viewers. The same thing could be said about any period that qualifies as a "violent incident" described in the article screenshotted above. Take Pandora, for instance: how many times have you heard someone say "You really need to watch c!Sapnap's prison visit to get a sense of what it was like," or "c!Techno's prison podcast is vital to understanding what happens in Pandora," or something else to that effect. We all have specific streams we see as crucial puzzle pieces, but we don't all agree on what those streams are. And, depending on which streams you see as vital, your idea of what went down during a particular violence hub incident may be very different from my perception of that event, and vice-versa. Be that as it may, consuming the story this way, rather than in the form of a traditional linear narrative, allows viewers to come to more nuanced understandings of not just the incident in question, but also why the incident happened, what other events led up to it, the later impacts it had on the story, how character relationships and dynamics may have shifted as a result of the incident, etc. The decentralized nature of this structure enables an understanding of the story that appropriately accounts for its complexity.

Now that all that's out of the way, I want to talk about the structure of the violence hub in relation to audience expectations. When the finale aired, I was pretty confused by a lot of folks' reactions, partly because of how I perceived the DSMP and how I see its structure interacting with its themes. To me, DSMP is a story largely about cyclical violence, so an ending where c!Tommy (a main character, driving force for the narrative, and sometimes the protagonist depending on who you ask) chooses to put an end to that cycle made sense to me. I never saw the finale as being about an abuse victim forgiving his abuser, I saw it as being about an abuse victim purposefully stepping back, seeing the cycle of violence for what it is, and choosing to opt out. This is a fairly unpopular interpretation of the finale, but it's one that I stand by because it's firmly grounded in the text: the dialogue, the scenecraft, the structure. Up until the finale, c!Tommy perceives the DSMP as a linear, centralized story - his own! He acts accordingly, rarely stopping to consider consequences or other characters' perspectives. When he's sent to limbo in the finale, c!Tommy is suddenly able to see into c!Dream's past memories from c!Dream's perspective. In this moment, c!Tommy's experience of the narrative ceases to be linear and centralized; in this moment, he is experiencing the story in the same way that we, the audience have been experiencing the story. c!Tommy, placed in the audience's shoes, is presented with what is essentially a "branch" to the violence hub that is Exile, that is Pandora, that is the aftermath of both of those arcs. Upon seeing that "branch" (the memory), c!Tommy is given context for some of the events that have happened on the server. This newfound context allows him to understand his own story as a part of the stories of the people around him. This newfound context enables him to understand the story in a way that accounts for its complexities. He doesn't forgive c!Dream, but he is able to recognize the cycle of violence and choose not to participate anymore.

So, back to the thing I said about structure and audience expectations. As I mentioned, a lot of fans hated the finale for a lot of reasons. It wasn't to everyone's taste, and that's fine. But I would like to posit that one reason people didn't like the finale was because the rhizomatic structure of the story (which c!Tommy becomes aware of) subverted their own expectations about storytelling in general and this story in particular. I'd argue that a lot of people hated the finale because they were still clinging to the possibility of a non-rhizomatic narrative: one that has a traditional beginning/middle/end, one that has clear heroes and villains, one in which unambiguous good vanquishes unambiguous evil. Which isn't a bad thing to want, it's just...not what DSMP is, or ever was.

[irradiate-space]

I'm trying to download a series of files from a file host (HTTPS) which refuses to allow more than one download at a time.

wget --wait 11 --random-wait -c --http2-request-window 1 -i urls.txt

wget is being very helpful, noticing the presence of HTTP/2 support, and is attempting to parallelize the download with as many connections as the server will support (6). The server 429's all connections after the first; this breaks wget -i and requires me to manually re-trigger the process after each file is finished downloading.

Is there a way to get wget to only make one (1) request at a time?

Edit: --max-threads=1 solves it.

Turns out it wasn't HTTP/2; it's that when I switched from Ubuntu to Fedora I got as a bonus a packaged version of wget (GNU wget2) that supports multithreading.

[compneuropapers]

Interesting Papers for Week 44, 2023

Rhythmic temporal coordination of neural activity prevents representational conflict during working memory. Abdalaziz, M., Redding, Z. V., & Fiebelkorn, I. C. (2023). Current Biology, 33(9), 1855-1863.e3.

Which processes dominate visual search: Bottom-up feature contrast, top-down tuning or trial history? Becker, S. I., Grubert, A., Horstmann, G., & Ansorge, U. (2023). Cognition, 236, 105420.

Neural dynamics underlying associative learning in the dorsal and ventral hippocampus. Biane, J. S., Ladow, M. A., Stefanini, F., Boddu, S. P., Fan, A., Hassan, S., … Kheirbek, M. A. (2023). Nature Neuroscience, 26(5), 798–809.

A reservoir of foraging decision variables in the mouse brain. Cazettes, F., Mazzucato, L., Murakami, M., Morais, J. P., Augusto, E., Renart, A., & Mainen, Z. F. (2023). Nature Neuroscience, 26(5), 840–849.

Spike-phase coupling patterns reveal laminar identity in primate cortex. Davis, Z. W., Dotson, N. M., Franken, T. P., Muller, L., & Reynolds, J. H. (2023). eLife, 12, e84512.

Is all mental effort equal? The role of cognitive demand-type on effort avoidance. Embrey, J. R., Donkin, C., & Newell, B. R. (2023). Cognition, 236, 105440.

Ventral striatum dopamine release encodes unique properties of visual stimuli in mice. Gonzalez, L. S., Fisher, A. A., D’Souza, S. P., Cotella, E. M., Lang, R. A., & Robinson, J. E. (2023). eLife, 12, e85064.

Computational complexity drives sustained deliberation. Hong, T., & Stauffer, W. R. (2023). Nature Neuroscience, 26(5), 850–857.

Mathematical Model of Synaptic Long-Term Potentiation as a Bistability in a Chain of Biochemical Reactions with a Positive Feedback. Katauskis, P., Ivanauskas, F., & Alaburda, A. (2023). Acta Biotheoretica, 71(3), 16.

Geometric determinants of the postrhinal egocentric spatial map. LaChance, P. A., & Taube, J. S. (2023). Current Biology, 33(9), 1728-1743.e7.

Learning at your brain’s rhythm: individualized entrainment boosts learning for perceptual decisions. Michael, E., Covarrubias, L. S., Leong, V., & Kourtzi, Z. (2023). Cerebral Cortex, 33(9), 5382–5394.

Retinal motion statistics during natural locomotion. Muller, K. S., Matthis, J., Bonnen, K., Cormack, L. K., Huk, A. C., & Hayhoe, M. (2023). eLife, 12, e82410.

Neural dynamics and architecture of the heading direction circuit in zebrafish. Petrucco, L., Lavian, H., Wu, Y. K., Svara, F., Štih, V., & Portugues, R. (2023). Nature Neuroscience, 26(5), 765–773.

Elucidating a locus coeruleus-dentate gyrus dopamine pathway for operant reinforcement. Petter, E. A., Fallon, I. P., Hughes, R. N., Watson, G. D., Meck, W. H., Ulloa Severino, F. P., & Yin, H. H. (2023). eLife, 12, e83600.

Principles for coding associative memories in a compact neural network. Pritz, C., Itskovits, E., Bokman, E., Ruach, R., Gritsenko, V., Nelken, T., … Zaslaver, A. (2023). eLife, 12, e74434.

Critical Drift in a Neuro-Inspired Adaptive Network. Sormunen, S., Gross, T., & Saramäki, J. (2023). Physical Review Letters, 130(18), 188401.

Dopaminergic prediction errors in the ventral tegmental area reflect a multithreaded predictive model. Takahashi, Y. K., Stalnaker, T. A., Mueller, L. E., Harootonian, S. K., Langdon, A. J., & Schoenbaum, G. (2023). Nature Neuroscience, 26(5), 830–839.

Inhibitory control of sharp-wave ripple duration during learning in hippocampal recurrent networks. Vancura, B., Geiller, T., Grosmark, A., Zhao, V., & Losonczy, A. (2023). Nature Neuroscience, 26(5), 788–797.

Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3. Watkins de Jong, L., Nejad, M. M., Yoon, E., Cheng, S., & Diba, K. (2023). Current Biology, 33(9), 1689-1703.e5.

The cholinergic basal forebrain provides a parallel channel for state-dependent sensory signaling to auditory cortex. Zhu, F., Elnozahy, S., Lawlor, J., & Kuchibhotla, K. V. (2023). Nature Neuroscience, 26(5), 810–819.

[letsremotify]

What Future Trends in Software Engineering Can Be Shaped by C++

The direction of innovation and advancement in the broad field of software engineering is greatly impacted by programming languages. C++ is a well-known programming language that is very efficient, versatile, and has excellent performance. In terms of the future, C++ will have a significant influence on software engineering, setting trends and encouraging innovation in a variety of fields. 

In this blog, we'll look at three key areas where the shift to a dynamic future could be led by C++ developers.

1. High-Performance Computing (HPC) & Parallel Processing

Driving Scalability with Multithreading

Within high-performance computing (HPC), where managing large datasets and executing intricate algorithms in real time are critical tasks, C++ is still an essential tool. The fact that C++ supports multithreading and parallelism is becoming more and more important as parallel processing-oriented designs, like multicore CPUs and GPUs, become more commonplace.

Multithreading with C++

At the core of C++ lies robust support for multithreading, empowering developers to harness the full potential of modern hardware architectures. C++ developers adept in crafting multithreaded applications can architect scalable systems capable of efficiently tackling computationally intensive tasks.

C++ Empowering HPC Solutions

Developers may redefine efficiency and performance benchmarks in a variety of disciplines, from AI inference to financial modeling, by forging HPC solutions with C++ as their toolkit. Through the exploitation of C++'s low-level control and optimization tools, engineers are able to optimize hardware consumption and algorithmic efficiency while pushing the limits of processing capacity.

2. Embedded Systems & IoT

Real-Time Responsiveness Enabled

An ability to evaluate data and perform operations with low latency is required due to the widespread use of embedded systems, particularly in the quickly developing Internet of Things (IoT). With its special combination of system-level control, portability, and performance, C++ becomes the language of choice.

C++ for Embedded Development

C++ is well known for its near-to-hardware capabilities and effective memory management, which enable developers to create firmware and software that meet the demanding requirements of environments with limited resources and real-time responsiveness. C++ guarantees efficiency and dependability at all levels, whether powering autonomous cars or smart devices.

Securing IoT with C++

In the intricate web of IoT ecosystems, security is paramount. C++ emerges as a robust option, boasting strong type checking and emphasis on memory protection. By leveraging C++'s features, developers can fortify IoT devices against potential vulnerabilities, ensuring the integrity and safety of connected systems.

3. Gaming & VR Development

Pushing Immersive Experience Boundaries

In the dynamic domains of game development and virtual reality (VR), where performance and realism reign supreme, C++ remains the cornerstone. With its unparalleled speed and efficiency, C++ empowers developers to craft immersive worlds and captivating experiences that redefine the boundaries of reality.

Redefining VR Realities with C++

When it comes to virtual reality, where user immersion is crucial, C++ is essential for producing smooth experiences that take users to other worlds. The effectiveness of C++ is crucial for preserving high frame rates and preventing motion sickness, guaranteeing users a fluid and engaging VR experience across a range of applications.

C++ in Gaming Engines

C++ is used by top game engines like Unreal Engine and Unity because of its speed and versatility, which lets programmers build visually amazing graphics and seamless gameplay. Game developers can achieve previously unattainable levels of inventiveness and produce gaming experiences that are unmatched by utilizing C++'s capabilities.

Conclusion

In conclusion, there is no denying C++'s ongoing significance as we go forward in the field of software engineering. C++ is the trend-setter and innovator in a variety of fields, including embedded devices, game development, and high-performance computing. C++ engineers emerge as the vanguards of technological growth, creating a world where possibilities are endless and invention has no boundaries because of its unmatched combination of performance, versatility, and control.

FAQs about Future Trends in Software Engineering Shaped by C++

How does C++ contribute to future trends in software engineering?

C++ remains foundational in software development, influencing trends like high-performance computing, game development, and system programming due to its efficiency and versatility.

Is C++ still relevant in modern software engineering practices?

Absolutely! C++ continues to be a cornerstone language, powering critical systems, frameworks, and applications across various industries, ensuring robustness and performance.

What advancements can we expect in C++ to shape future software engineering trends?

Future C++ developments may focus on enhancing parallel computing capabilities, improving interoperability with other languages, and optimizing for emerging hardware architectures, paving the way for cutting-edge software innovations.

[hob28]

Advanced C Programming: Mastering the Language

Introduction

Advanced C programming is essential for developers looking to deepen their understanding of the language and tackle complex programming challenges. While the basics of C provide a solid foundation, mastering advanced concepts can significantly enhance your ability to write efficient, high-performance code.

1. Overview of Advanced C Programming

Advanced C programming builds on the fundamentals, introducing concepts that enhance efficiency, performance, and code organization. This stage of learning empowers programmers to write more sophisticated applications and prepares them for roles that demand a high level of proficiency in C.

2. Pointers and Memory Management

Mastering pointers and dynamic memory management is crucial for advanced C programming, as they allow for efficient use of resources. Pointers enable direct access to memory locations, which is essential for tasks such as dynamic array allocation and manipulating data structures. Understanding how to allocate, reallocate, and free memory using functions like malloc, calloc, realloc, and free can help avoid memory leaks and ensure optimal resource management.

3. Data Structures in C

Understanding advanced data structures, such as linked lists, trees, and hash tables, is key to optimizing algorithms and managing data effectively. These structures allow developers to store and manipulate data in ways that improve performance and scalability. For example, linked lists provide flexibility in data storage, while binary trees enable efficient searching and sorting operations.

4. File Handling Techniques

Advanced file handling techniques enable developers to manipulate data efficiently, allowing for the creation of robust applications that interact with the file system. Mastering functions like fopen, fread, fwrite, and fclose helps you read from and write to files, handle binary data, and manage different file modes. Understanding error handling during file operations is also critical for building resilient applications.

5. Multithreading and Concurrency

Implementing multithreading and managing concurrency are essential skills for developing high-performance applications in C. Utilizing libraries such as POSIX threads (pthreads) allows you to create and manage multiple threads within a single process. This capability can significantly enhance the performance of I/O-bound or CPU-bound applications by enabling parallel processing.

6. Advanced C Standard Library Functions

Leveraging advanced functions from the C Standard Library can simplify complex tasks and improve code efficiency. Functions for string manipulation, mathematical computations, and memory management are just a few examples. Familiarizing yourself with these functions not only saves time but also helps you write cleaner, more efficient code.

7. Debugging and Optimization Techniques

Effective debugging and optimization techniques are critical for refining code and enhancing performance in advanced C programming. Tools like GDB (GNU Debugger) help track down bugs and analyze program behavior. Additionally, understanding compiler optimizations and using profiling tools can identify bottlenecks in your code, leading to improved performance.

8. Best Practices in Advanced C Programming

Following best practices in coding and project organization helps maintain readability and manageability of complex C programs. This includes using consistent naming conventions, modularizing code through functions and header files, and documenting your code thoroughly. Such practices not only make your code easier to understand but also facilitate collaboration with other developers.

9. Conclusion

By exploring advanced C programming concepts, developers can elevate their skills and create more efficient, powerful, and scalable applications. Mastering these topics not only enhances your technical capabilities but also opens doors to advanced roles in software development, systems programming, and beyond. Embrace the challenge of advanced C programming, and take your coding skills to new heights!

[deltainfoteklive]

C++ Tools Every Programmer Should Know in 2023

As technology continues to evolve rapidly, it is essential for programmers to stay updated with the latest tools to enhance their productivity and efficiency. In this article, we will explore some of the top C++ tools that every programmer should know in 2023. From integrated development environments (IDEs) to debuggers, code editors, libraries and frameworks, documentation tools, and build systems, we'll cover a comprehensive range of tools that will help programmers streamline their C++ development process. 1. Integrated Development Environments (IDEs) Visual Studio Code Visual Studio Code (VS Code) is a highly popular and powerful IDE that offers excellent support for C++ development. It provides a wide range of features, including code completion, syntax highlighting, debugging capabilities, and easy integration with version control systems like Git. Eclipse Eclipse is another widely used IDE for C++ development. It offers a comprehensive set of tools, such as code refactoring, code navigation, and a customizable user interface. Eclipse also supports various plugins and extensions to enhance its functionality further. Qt Creator Qt Creator is a specialized IDE for C++ and Qt development. It provides an intuitive and user-friendly interface, along with powerful tools for designing graphical user interfaces (GUIs) using the Qt framework. Qt Creator also supports cross-platform development, making it an excellent choice for C++ programmers. 2. Debuggers 2.1 GDB The GNU Debugger (GDB) is a command-line debugger that helps programmers analyze and debug their C++ code efficiently. It allows breakpoints, stepping through code, inspecting variables, and examining call stacks, among other debugging features. GDB is highly customizable and supports various platforms. 2.2 Visual Studio Debugger The Visual Studio Debugger is a robust and user-friendly debugger provided by Microsoft Visual Studio. It offers advanced debugging capabilities for C++ programs, including real-time expression evaluation, memory tracking, and parallel debugging. The Visual Studio Debugger integrates seamlessly with Visual Studio IDE. 2.3 LLDB LLDB is a powerful, modern debugger primarily developed for LLVM-based programming languages such as C++. It provides capabilities like multithreaded debugging, just-in-time compilation, and efficient memory management. LLDB offers a command-line interface and can be used on multiple platforms. 3. Code Editors 3.1 Sublime Text Sublime Text is a lightweight yet feature-rich code editor known for its speed and extensibility. It offers a minimalistic user interface, multiple selections, powerful search and replace functionality, and a vast collection of plugins to customize the editor according to programmers' needs. 3.2 Atom Atom is a highly customizable and open-source code editor that gained popularity among developers. It supports various programming languages, including C++, and provides features like smart autocompletion, file system browser, and Git integration. Atom also allows third-party package installations to extend its functionality. 3.3 Vim Vim is a highly efficient and text-based code editor preferred by many experienced programmers. It offers a wide range of features designed to enhance developers' productivity, such as extensive key bindings, split editing, powerful search and replace, and support for plugins and scripts. 4. Libraries and Frameworks 4.1 Boost Boost is a widely used C++ libraries collection known for its high-quality and portable code. It provides a range of libraries that cover various aspects of C++ programming, including smart pointers, multithreading, regular expressions, and container classes. Boost libraries offer excellent support and compatibility with different platforms. 4.2 Qt Qt is a comprehensive cross-platform framework that includes a powerful set of C++ libraries and tools. It enables developers to create high-performance applications with an intuitive UI and supports features like network programming, XML processing, database connectivity, and 3D rendering. Qt is widely adopted for GUI application development. 4.3 STL The Standard Template Library (STL) is an essential component of C++ programming. It offers a collection of generic algorithms, containers, and iterators, making it easier to write efficient and maintainable code. STL provides various data structures and algorithms, such as vectors, lists, sorting, and searching functions. 5. Documentation Tools 5.1 Doxygen Doxygen is a popular documentation tool for C++ projects. It automatically generates documentation from properly formatted comments in the source code. Doxygen supports various output formats like HTML, LaTeX, and PDF, allowing programmers to create professional-looking documentation for their projects easily. 5.2 Docutils Docutils is a Python-based documentation tool that provides support for multiple markup languages, including reStructuredText. It enables programmers to write documentation in a human-readable format and convert it into different output formats. Docutils is widely used in the C++ community for documenting projects. 5.3 Sphinx Sphinx is another widely adopted documentation tool that integrates well with the C++ ecosystem. It supports multiple markup languages, including reStructuredText and Markdown, and offers features like automatic cross-referencing, code highlighting, and generation of API documentation. Sphinx is highly customizable and extensible. 6. Build Systems 6.1 CMake CMake is a popular build system that simplifies the process of building C++ projects across multiple platforms and compilers. It provides a unified build configuration language and generates platform-specific build scripts. CMake offers excellent scalability and flexibility, making it suitable for projects of any size. 6.2 Make Make is a classic and widely used build system for C++ projects. It utilizes makefiles to automate the build process by specifying dependencies and actions. Make is highly efficient and can handle complex project structures. It is a fundamental tool in the C++ development ecosystem, and many other build systems are based on its principles. 6.3 Ninja Ninja is a fast and lightweight build system designed for speed and efficient dependency tracking. It focuses on the minimalistic build process and supports parallel builds. Ninja is often used in conjunction with CMake, allowing for faster compilation and linking of C++ projects. Conclusion: staying up to date with the right tools can significantly improve a programmer's productivity and efficiency. The C++ tools mentioned in this article, including IDEs, debuggers, code editors, libraries and frameworks, documentation tools, and build systems, are essential for any C++ developer in 2023. By leveraging these tools, programmers can streamline their development process, write cleaner code, and build robust applications. Embrace these tools and take your C++ programming skills to new heights! FAQs FAQ 1: What are Integrated Development Environments (IDEs)? Integrated Development Environments (IDEs) are software applications that provide comprehensive tools and features to simplify the software development process. They typically include code editors, debuggers, build systems, and other utilities needed for efficient coding and debugging. FAQ 2: What are Debuggers? Debuggers are tools that help programmers identify and fix issues in their code. They allow developers to step through the code, set breakpoints, inspect variables, and analyze program execution to understand and resolve bugs and errors. FAQ 3: What are Code Editors? Code editors are software tools designed specifically for writing and editing code. They provide features like syntax highlighting, code completion, and navigation to enhance productivity and make coding easier and more efficient. FAQ 4: What are Libraries and Frameworks? Libraries and frameworks are collections of pre-written code that provide reusable functionality to programmers. They can simplify coding tasks by providing ready-made solutions for common problems, saving time and effort. FAQ 5: What are Build Systems? Build systems are software tools that automate the process of compiling and linking code to create executable programs or libraries. They manage dependencies, handle project configurations, and ensure that all required resources are properly built and linked together. Read the full article

[codingprolab]

CS 211: C and Systems Programming Programming Assignment 3: Multithreaded Online Shopping

1 Introduction For this assignment, you will write programs for a multithreaded shopping cart simulation. This will give you an opportunity to exercise mutexes and thread coordination. You will write client/server programs with a single server that supports multiple clients communicating through TCP/IP network connections. Having to support multiple concurrent client-service threads in the server…

[korshubudemycoursesblog]

Master C++ in 2025: Your Complete Programming Journey from Beginner to Pro

Are you ready to level up your programming game in 2025? Whether you're just dipping your toes into coding or already knee-deep in code, C++ remains one of the most powerful languages you can learn. From building operating systems and high-performance games to shaping the very foundations of modern software, C++ is the language that never goes out of style.

But here's the catch—learning C++ from scratch can feel overwhelming if you don’t have the right guide. That’s where this course comes in. If you’re looking for a structured, effective way to learn, this C++ Programming: Beginners to Advanced for Developers course is your one-stop solution.

Let’s break it all down for you—why C++ is still a must-learn in 2025, what you’ll gain from this course, and how it can supercharge your career.

Why C++ Still Matters in 2025

C++ isn’t just another programming language on the list. It's the workhorse of software development. Think of it as the unsung hero that powers video games, flight simulators, embedded systems, and even space missions.

So, why is C++ still relevant?

🔧 Performance & Speed: C++ gives you close-to-hardware control, making it perfect for performance-critical applications.

🛠️ System-Level Programming: Ideal for developing OS, device drivers, and high-level applications.

🎮 Game Development: Industry giants like Unreal Engine are built on C++.

🧠 Problem-Solving Mindset: Mastering C++ builds a deep understanding of memory management and logic structures.

🌐 Cross-Platform Power: Write once, run anywhere—Linux, Windows, macOS, and more.

Even newer languages like Rust and Go take cues from C++. So, if you're serious about becoming a developer with real, marketable skills, mastering C++ gives you a solid edge.

Who Should Take This C++ Course?

This course is tailored for everyone who wants to master C++, regardless of your starting point. Here's who it's perfect for:

✅ Absolute Beginners with no prior coding experience

✅ College Students learning C++ for academics or competitive programming

✅ Software Developers looking to upskill and future-proof their toolkit

✅ Game Developers needing low-level programming control

✅ Embedded Systems Engineers wanting mastery over hardware

This course meets you where you are—and then takes you beyond what you thought possible.

What Makes This Course a Standout?

Let’s be honest—there are tons of C++ tutorials out there. So why choose this one?

Because it’s not just about syntax; it’s about helping you think like a C++ developer.

Here’s what sets this course apart:

📚 Comprehensive Coverage

From the fundamentals to the advanced features of C++, this course covers:

Data types, loops, arrays, and strings

Functions, pointers, and references

Object-oriented programming (OOP): Classes, inheritance, polymorphism

Templates and STL (Standard Template Library)

File handling and exception management

Advanced topics like lambda functions, smart pointers, and multithreading

Whether you're new or experienced, you’ll find exactly what you need.

💡 Hands-On Coding

This isn’t a watch-and-forget course. You’ll be coding real C++ programs from day one. Each concept comes with practical examples and exercises that help cement your understanding.

👨‍🏫 Expert-Led Instruction

Learn directly from industry professionals who explain complex topics in a clear, friendly way—without using unnecessary jargon. It feels like you're having a conversation, not sitting in a lecture.

📈 Career-Focused Approach

By the end, you’ll be confident enough to crack job interviews, ace college exams, or start building serious software projects. Your learning is focused not just on theory—but real-world application.

What You'll Learn: Module-by-Module Breakdown

Here's a peek into what you'll master in this C++ Programming: Beginners to Advanced for Developers course.

📌 Module 1: Introduction to C++ & Setup

Installing compilers and IDEs

Writing your first "Hello World"

Understanding how C++ differs from other languages

📌 Module 2: Core Programming Concepts

Variables, constants, and data types

Arithmetic, logical, and bitwise operations

Control flow: if-else, switch, loops

📌 Module 3: Functions, Arrays, and Pointers

Declaring and calling functions

Recursion and function overloading

Deep dive into arrays, multidimensional arrays

Pointers and dynamic memory allocation

📌 Module 4: Object-Oriented Programming

Introduction to classes and objects

Constructors, destructors, and access modifiers

Inheritance, polymorphism, abstraction, and encapsulation

📌 Module 5: Advanced Topics

Exception handling

File input/output

Smart pointers (unique, shared, weak)

Lambda expressions and function objects

📌 Module 6: STL – Standard Template Library

Vectors, lists, stacks, queues, maps, and sets

Algorithms and iterators

Building efficient and reusable code

📌 Module 7: Final Project

You’ll apply everything by building a capstone project—something real and impressive to showcase in your portfolio.

What You’ll Gain After Completing the Course

By the end of this C++ journey, you’ll:

✅ Write efficient and clean C++ code

✅ Build complete applications from scratch

✅ Solve complex programming challenges

✅ Understand the inner workings of memory and system performance

✅ Be job-ready with interview-level knowledge

✅ Stand out with practical C++ projects in your resume

Why Now Is the Best Time to Learn C++

The tech landscape is evolving fast. While Python and JavaScript dominate in certain domains, C++ is becoming more critical in AI infrastructure, finance tech, IoT, and space systems.

C++ developers are in high demand, and their average salaries reflect that—often higher than developers in other mainstream languages.

And let’s not forget that companies like Adobe, Amazon, Microsoft, Google, and NVIDIA are always looking for strong C++ developers.

The sooner you start learning, the sooner you start building your future.

Real Feedback from Learners

Here’s what learners who took this course are saying:

“I had no idea C++ could be this fun. The way each topic builds on the previous one is brilliant.” – Neha R.

“I cracked my first software job interview because of this course. It gave me confidence I didn’t have before.” – Mark J.

“As someone transitioning from Python, this course made it easy to switch gears. Totally worth it.” – Vivek K.

Is It Worth It? 100% Yes.

If you're asking yourself whether investing your time in this course is worth it, consider this:

You’re learning a high-demand language.

You’re learning from experts who know how to teach.

You get a portfolio-ready project at the end.

You gain real skills that employers want.

It’s not just another course. It’s a roadmap to a better, more confident you.

Let’s Get Started

Don’t wait for the “perfect time” to start learning C++. That time is now.

👉 Jump into the C++ Programming: Beginners to Advanced for Developers course and start building the skills that will shape your future.

This course is a smart investment in your career, your skillset, and your confidence.

[sharmanilesh]

PLINQ

The Asynchronous counterpart of LINQ to object query is termed as PLINQ. While the LINQ typically get its behaviors and properties using System.Linq, the PLinq gets the corresponding from System.Linq.ParallelEnumerable.

When we deal with the parallel programming using the Task Parallel Library and queries the Objects generally it gets a bit slower using LINQ owing to the ultimate objective to perform the task efficiently and faster. The idea for the same gets a boost using PLINQ, here we have option to asynchronously query the objects and assign it to various Threads under execution.

How?

The ParallelEnumerable class exposes the functionality that is required to achieve the objective for such type of conditions.

Some of the methods that can be used are

AsParallel().

WithDegreeOfParallelism()

ForAll()

When?

PLINQ should be carefully used as it can make things complex and adds overburden to existing implementations, since its implementation requires first breaking the things then assigning it to various threads and ultimately merging them to obtain the final result.

We can keep in mind things like Ordering of Objects, final result whether it should be sorted or not etc. There are numerous helpers provided to us with the LINQ:

ForAll Operator

Cancellation Token

Custom Partitioners

Exception Handling using Aggregate Exception

Summarizing the overall concept, we can say that it is a powerful thing at the exposure but it should be used wisely to avoid complexities and unnecessary burden to the code.

References: https://learn.microsoft.com

[govindhtech]

Improving Python Threading Strategies For AI/ML Workloads

Python Threading Dilemma Solution Python excels at AI and machine learning. CPython, the computer language's reference implementation and byte-code interpreter, needs intrinsic support for parallel processing and multithreading. The notorious Global Interpreter Lock (GIL) “locks” the CPython interpreter into running on one thread at a time, regardless of the context. NumPy, SciPy, and PyTorch provide multi-core processing using C-based implementations.

Python should be approached differently. Imagine GIL as a thread and vanilla Python as a needle. That needle and thread make a clothing. Although high-grade, it might have been made cheaper without losing quality. Therefore, what if Intel could circumvent that “limiter” by parallelising Python programs with Numba or oneAPI libraries? What if a sewing machine replaces a needle and thread to construct that garment? What if dozens or hundreds of sewing machines manufacture several shirts extremely quickly?

Intel Distribution of Python uses robust modules and tools to optimise Intel architecture instruction sets.

Using oneAPI libraries to reduce Python overheads and accelerate math operations, the Intel distribution gives compute-intensive Python numerical and scientific apps like NumPy, SciPy, and Numba C++-like performance. This helps developers provide their applications with excellent multithreading, vectorisation, and memory management while enabling rapid cluster expansion.One Let's look at Intel's Python parallelism and composability technique and how it helps speed up AI/ML processes. Numpy/SciPy Nested Parallelism The Python libraries NumPy and SciPy were designed for scientific computing and numerical computation.

Exposing parallelism on all software levels, such as by parallelising outermost loops or using functional or pipeline parallelism at the application level, can enable multithreading and parallelism in Python applications. This parallelism can be achieved with Dask, Joblib, and the multiprocessing module mproc (with its ThreadPool class).

An optimised math library like the Intel oneAPI Math Kernel Library helps accelerate Python modules like NumPy and SciPy for data parallelism. The high processing needs of huge data for AI and machine learning require this. Multi-threat oneMKL using Python Threading runtimes. Environment variable MKL_THREADING_LAYER adjusts the threading layer. Nested parallelism occurs when one parallel part calls a function that contains another parallel portion. Sync latencies and serial parts—parts that cannot operate in parallel—are common in NumPy and SciPy programs. Parallelism-within-parallelism reduces or hides these areas.

Numba

Even though they offer extensive mathematical and data-focused accelerations, NumPy and SciPy are defined mathematical instruments accelerated with C-extensions. If a developer wants it as fast as C-extensions, they may need unorthodox math. Numba works well here. Just-In-Time compilers Numba and LLVM. Reduce the performance gap between Python and statically typed languages like C and C++. We support Workqueue, OpenMP, and Intel oneAPI Python Threading Building Blocks. The three built-in Python Threading layers represent these runtimes. New threading layers are added using conda commands (e.g., $ conda install tbb). Only workqueue is automatically installed. Numba_threading_layer sets the threading layer. Remember that there are two ways to select this threading layer: (1) picking a layer that is normally safe under diverse parallel processing, or (2) explicitly specifying the suitable threading layer name (e.g., tbb). For Numba threading layer information, see the official documentation.

Threading Composability

The Python Threading composability of an application or component determines the efficiency of co-existing multi-threaded components. A “perfectly composable” component operates efficiently without affecting other system components. To achieve a fully composable Python Threading system, over-subscription must be prevented by ensuring that no parallel piece of code or component requires a specific number of threads (known as “mandatory” parallelism). The alternative is to provide "optional" parallelism in which a work scheduler chooses which user-level threads components are mapped to and automates task coordination across components and parallel areas. The scheduler uses a single thread-pool to arrange the program's components and libraries, hence its threading model must be more efficient than the built-in high-performance library technique. Efficiency is lost otherwise.

Intel's Parallelism and Composability Strategy

Python Threading composability is easier with oneTBB as the work scheduler. The open-source, cross-platform C++ library oneTBB, which supports threading composability, optional parallelism, and layered parallelism, enables multi-core parallel processing. The oneTBB version available at the time of writing includes an experimental module that provides threading composability across libraries, enabling multi-threaded performance enhancements in Python. Acceleration comes from the scheduler's improved Python Threading allocation. OneTBB replaces Python ThreadPool with Pool. By dynamically replacing or updating objects at runtime, monkey patching keeps the thread pool active across modules without code changes. OneTBB also substitutes oneMKL by activating its Python Threading layer, which automates composable parallelism using NumPy and SciPy calls.

Nested parallelism can improve performance, as seen in the following composability example on a system with MKL-enabled NumPy, TBB, and symmetric multiprocessing (SMP) modules and IPython kernels. IPython's command shell interface allows interactive computing in multiple programming languages. The demo was ran in Jupyter Notebook to compare performance quantitatively.

If the kernel is changed in the Jupyter menu, the preceding cell must be run again to construct the ThreadPool and deliver the runtime results below.

With the default Python kernel, the following code runs for all three trials:

This method can find matrix eigenvalues with the default Python kernel. Activating the Python-m SMP kernel improves runtime by an order of magnitude. The Python-m TBB kernel boosts even more.

For this composability example, OneTBB's dynamic task scheduler performs best because it manages code where the innermost parallel sections cannot completely leverage the system's CPU and where work may vary. SMP is still useful, however it works best when workloads are evenly divided and outermost workers have similar loads.

Conclusion

In conclusion, multithreading speeds AI/ML operations. Python AI and machine learning apps can be optimised in several ways. Multithreading and multiprocessing will be crucial to pushing AI/ML software development workflows. See Intel's AI Tools and Framework optimisations and the unified, open, standards-based oneAPI programming architecture that underpins its AI Software Portfolio.