#multicore threading

[pc-619]

Another doodle I found on a whim. The class gets boring real quick. The dorky pose derives from the default HL1 pose (kinda like the t-pose).

[blubberquark]

Share Your Anecdotes: Multicore Pessimisation

I took a look at the specs of new 7000 series Threadripper CPUs, and I really don't have any excuse to buy one, even if I had the money to spare. I thought long and hard about different workloads, but nothing came to mind.

Back in university, we had courses about map/reduce clusters, and I experimented with parallel interpreters for Prolog, and distributed computing systems. What I learned is that the potential performance gains from better data structures and algorithms trump the performance gains from fancy hardware, and that there is more to be gained from using the GPU or from re-writing the performance-critical sections in C and making sure your data structures take up less memory than from multi-threaded code. Of course, all this is especially important when you are working in pure Python, because of the GIL.

The performance penalty of parallelisation hits even harder when you try to distribute your computation between different computers over the network, and the overhead of serialisation, communication, and scheduling work can easily exceed the gains of parallel computation, especially for small to medium workloads. If you benchmark your Hadoop cluster on a toy problem, you may well find that it's faster to solve your toy problem on one desktop PC than a whole cluster, because it's a toy problem, and the gains only kick in when your data set is too big to fit on a single computer.

The new Threadripper got me thinking: Has this happened to somebody with just a multicore CPU? Is there software that performs better with 2 cores than with just one, and better with 4 cores than with 2, but substantially worse with 64? It could happen! Deadlocks, livelocks, weird inter-process communication issues where you have one process per core and every one of the 64 processes communicates with the other 63 via pipes? There could be software that has a badly optimised main thread, or a badly optimised work unit scheduler, and the limiting factor is single-thread performance of that scheduler that needs to distribute and integrate work units for 64 threads, to the point where the worker threads are mostly idling and only one core is at 100%.

I am not trying to blame any programmer if this happens. Most likely such software was developed back when quad-core CPUs were a new thing, or even back when there were multi-CPU-socket mainboards, and the developer never imagined that one day there would be Threadrippers on the consumer market. Programs from back then, built for Windows XP, could still run on Windows 10 or 11.

In spite of all this, I suspect that this kind of problem is quite rare in practice. It requires software that spawns one thread or one process per core, but which is deoptimised for more cores, maybe written under the assumption that users have for two to six CPU cores, a user who can afford a Threadripper, and needs a Threadripper, and a workload where the problem is noticeable. You wouldn't get a Threadripper in the first place if it made your workflows slower, so that hypothetical user probably has one main workload that really benefits from the many cores, and another that doesn't.

So, has this happened to you? Dou you have a Threadripper at work? Do you work in bioinformatics or visual effects? Do you encode a lot of video? Do you know a guy who does? Do you own a Threadripper or an Ampere just for the hell of it? Or have you tried to build a Hadoop/Beowulf/OpenMP cluster, only to have your code run slower?

I would love to hear from you.

[govindhtech]

Intel Core Ultra 9 285HX Specs, Benchmarks, Price and Gaming

The Core Ultra 9 285HX

This page describes the Intel Core Ultra 9 285HX processor and includes PerformanceTest benchmark data. It compares the CPU's single- and multi-threaded performance against others and highlights its core count, clock rates, and memory support. The content includes a gaming suitability assessment and a list of laptops using this CPU. It describes this Intel laptop CPU technically well.

Intel released their Arrow Lake-HX flagship mobile processor, the Core Ultra 9 285HX, in Q1 2025. It upgrades old versions and is for games and content development.

Key Features & Architecture

24 cores (eight for 5.5 GHz performance, sixteen for 4.6 GHz efficiency)

40MB L2, 36MB L3 cache

Max turbo 160W, base 55W

Intel Arc Graphics, a 300–2.0 GHz integrated GPU with four Xe cores.

13 TOPS NPU in AI, OpenVINO, ONNX RT, and DirectML support

DDR5-6400 up to 256GB, optional ECC.

Manufacturing uses TSMC N3B (3nm) and Foveros 3D packaging.

Intel's hybrid design optimises single- and multi-threaded programs with efficient and powerful processors. Installing an NPU to accelerate AI activities speeds up machine learning framework applications.

Video Game Performance

The Core Ultra 9 285HX excels in multi-core CPU games. High-setting games run smoothly with fast clocks and core distribution. It may behind AMD's gaming-optimized Ryzen 7000X3D series in benchmarks. Gaming is complete with the 285HX and discrete GPUs in high-end laptops.

3D rendering, video editing

The 285HX excels in 3D rendering and video editing for content developers. Benchmarks demonstrate 57% gains in Blender rendering speed over preceding processor generations. Its large core count and clever architecture make rendering and high-resolution video editing easy for creative professionals.

Performance Benchmarks

Cinebench R23: Single: 2008; Multi-core: 35,391.

Geekbench 6: 3,112 single-core, 21,839 multi-core

Up to 25% faster multicore performance than the i9-14900HX.

Advantages

More Multithreaded Performance: Ideal for video editing and 3D rendering.

AI Integration: NPUs accelerate AI workloads in AI-enabled apps.

Advanced Manufacturing: 3nm and Foveros packaging improve performance and efficiency.

Maximum memory capacity is 256GB DDR5-6400 for memory-intensive applications.

Disadvantages

High turbo power (up to 160W) can overheat laptops and shorten battery life.

Thermal Management: High workloads might cause thermal throttling, requiring strong cooling systems.

High-end gaming may not be achievable without a dedicated GPU, despite its advances.

Cost: Premium pricing may not benefit users with lower performance needs.

Cost and Access

Core Ultra 9 285HX laptops are premium desktops.The $4,499.99 MSI Raider 18 HX has this processor and an NVIDIA RTX 5080 GPU.

Conclusion:

The Intel Core Ultra 9 285HX's high core count, AI, and newest memory standards make it perfect for professionals and power users. Based on needs and use, consider cost, thermal management, and power consumption.

[programmingandengineering]

CSE4100: Multicore Programming

배경지식 Network programming Echo 서버 프로그래밍 동시 주식 서버 (Concurrent Stock Server) 설계 및 구현 (각 30점 + 보고서 10점) Task1: Event-driven Approach Task2: Thread-based Approach Task3: 성능 평가 및 분석 제출 방법 부록 프로젝트 목표 “여러 client들의 동시 접속 및 서비스를 위한 Concurrent stock server 을 구축” 주식 서버 주식 정보를 저장하고 있고 여러 client들과 통신하여, 주식 정보 List, 판매, 구매의 동작을 수행 주식 클라이언트 각 client는 server에 주식 사기, 팔기, 가격과 재고 조회 등의 요청을 함 배경지식: Echo…

[skilliqcourse]

Mastering Python: Advanced Training Techniques You Need to Know

Python, renowned for its simplicity and versatility, has become a staple in the toolkit of programmers worldwide. While mastering the basics of Python is essential, advancing your skills to the next level can open up a world of possibilities. Here are some advanced training techniques that will elevate your Python proficiency to new heights:

Data Structures and Algorithms: Delve deeper into data structures like stacks, queues, trees, and graphs, and learn algorithms for sorting, searching, and traversing. Understanding these fundamental concepts will enable you to write more efficient and optimized code for complex problems. 

Object-Oriented Programming (OOP): Explore advanced OOP concepts such as inheritance, polymorphism, encapsulation, and abstraction. Leveraging the power of classes and objects allows you to build modular, reusable, and scalable Python applications. 

Functional Programming: Embrace the functional programming paradigm by mastering concepts like higher-order functions, lambda expressions, and recursion. Functional programming techniques can lead to more concise, readable, and maintainable code. 

Concurrency and Parallelism: Learn how to write concurrent and parallel Python programs to make efficient use of multicore processors and improve performance. Libraries like asyncio, threading, and multiprocessing empower you to handle asynchronous tasks and parallel computations effectively. 

Pythonic Idioms and Best Practices: Familiarize yourself with Pythonic idioms and best practices to write code that is elegant, idiomatic, and easy to understand. Techniques such as list comprehensions, generator expressions, and context managers can streamline your code and enhance readability. 

Debugging and Profiling: Sharpen your debugging and profiling skills to identify and fix bugs, optimise performance, and troubleshoot issues in your Python code. Profiling tools like cProfile and line_profiler can help you pinpoint bottlenecks and optimise critical sections of your code. 

Testing and Test-Driven Development (TDD): Explore advanced testing frameworks like pytest and learn how to write comprehensive unit tests, integration tests, and functional tests for your Python projects. Adopting test-driven development practices can lead to more robust, reliable, and maintainable codebases.

Advanced Libraries and Frameworks: Explore advanced Python libraries and frameworks tailored to your specific areas of interest, such as data science (e.g., pandas, scikit-learn), web development (e.g., Django, Flask), or machine learning (e.g., TensorFlow, PyTorch).

ConclusionBy embracing these advanced training techniques and committing to continuous learning and improvement, you can elevate your Python skills to mastery level and tackle even the most challenging programming tasks with confidence and proficiency. Enhance your Python prowess with SkillIQ's comprehensive Python Training program. From fundamentals to advanced concepts, master Python programming and unleash your potential in software development, data analysis, and more.

[robertemma27-blog]

Global Embedded System Market – Industry Trends and Analysis 2030

The Embedded System Market is projected to grow from USD 86.5 billion in 2020 to USD 116.2 billion by 2025; it is expected to grow at a CAGR of 6.1% from 2020 to 2025. 

Key factors fueling the growth of this market include an increase in the number of research and development activities related to embedded systems, rise in demand for advanced driver-assistance systems (ADAS) and electromobility solutions for electric vehicles and hybrid vehicles, increase in demand for portable devices such as wearables, and rise in the use of multicore processors in military applications.

Intel (US), Renesas (Japan), STMicroelectronics (Switzerland), NXP Semiconductors (Netherlands), Texas Instruments (US), Microchip (US), Cypress Semiconductors (US), Qualcomm (US), Analog Devices (US), and Infineon Technologies (Germany) are the key players operating in the embedded system market.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=98154672

Based on functionality, the real-time embedded systems segment of the market is projected to grow at a high CAGR from 2020 to 2025

The real-time embedded systems segment of the embedded system market is projected to grow at a high CAGR from 2020 to 2025. Real-time embedded systems are dedicated to perform given functions within a specified time frame. Since there is an increasing focus on efficiency and power management in various applications, these embedded systems are increasingly being used in network systems, medical systems, process control systems, robot manufacturing systems, traffic control systems, multimedia systems, etc. Moreover, the rising demand for real-time data and its analysis, along with the requirement of advanced communication infrastructure for technologies such as 5G and artificial intelligence (AI), is expected to lead to demand for real-time embedded systems globally.

The medium-scale embedded systems segment accounted for the largest share of the embedded system market in 2019

In 2019, the medium-scale embedded systems segment accounted for the largest share of the embedded system market. Medium-scale embedded systems are used for packet processing in network switches and routers and data processing in ultrasonic sensors. These systems form a major part of network and communication applications owing to their excellent power handling capacities. The increasing requirement of fast processing of embedded systems and efficient power consumption are key factors leading to the growing demand for 16-bit and 32-bit microcontrollers. Based on the number of threads and memory requirements, embedded devices commonly use either 16-bit microcontroller or 32-bit microcontroller architectures or a combination of both.

Among industries, the communication segment accounted for the largest share of the embedded system market in 2019

The communication segment accounted for the largest share of the embedded system market in 2019. Embedded systems are used in routers, Ethernet devices, wireless infrastructures, and protocol converters for high-speed data transfer to support the expanding communication infrastructures. Embedded hardware such as FPGA and ASIC are being used in various network and telecom applications. Telecom providers often integrate hard IP for interfaces such as peripheral component interconnect (PCI) Express, 100G Ethernet, and DDR3/4 for high-speed data transfer using embedded hardware such as FPGA. Moreover, technologies such as eASIC, which are cost-effective and meet power targets, are increasingly used in radio access network (RAN) equipment.

APAC is projected to hold the largest share of the embedded system market in 2025

APAC is expected to hold the largest share of the embedded system market in 2025. The growing per capita income and ongoing large-scale industrialization and urbanization are factors driving the growth of the embedded system market in this region. In addition, the availability of low-cost electronic products in APAC is expected to contribute to an increased demand for microprocessors and microcontrollers in the region. The rising use of autonomous robots and embedded vision systems is also projected to lead to an increased demand for embedded system hardware such as microprocessors and controllers for use in industrial applications in APAC.

[research02]

Enhancing Application Performance with Java Multithreading

Introduction: In today's fast-paced digital world, optimizing application performance is crucial for delivering a seamless user experience. Java multithreading is a powerful technique that enables developers to improve application performance by executing multiple tasks concurrently. In this comprehensive guide, we'll explore the fundamentals of Java multithreading, its benefits, implementation strategies, and the importance of Java training courses in mastering this advanced programming concept.

Understanding Java Multithreading: Java multithreading is a programming technique that allows multiple threads to execute concurrently within a single Java application. A thread is a lightweight process that can perform tasks independently of other threads. By utilizing multithreading, developers can take advantage of modern multicore processors to execute tasks in parallel, thereby improving the overall performance and responsiveness of the application.

Benefits of Java Multithreading:

Improved Responsiveness: Multithreading allows applications to remain responsive even when executing CPU-intensive tasks. By offloading tasks to separate threads, the main application thread remains available to handle user interactions, resulting in a smoother user experience.

Enhanced Performance: Multithreading can significantly improve the performance of applications by leveraging the computational power of multicore processors. Tasks can be executed concurrently, leading to faster execution times and reduced latency.

Resource Utilization: Multithreading enables better utilization of system resources by maximizing CPU usage and reducing idle time. This can lead to more efficient use of hardware resources and improved overall system throughput.

Concurrency Control: Java provides robust mechanisms for synchronizing access to shared resources and coordinating the execution of multiple threads. This allows developers to write thread-safe code and avoid common concurrency issues such as race conditions and deadlocks.

Scalability: Multithreading facilitates the development of scalable applications that can handle increasing workloads by efficiently distributing tasks across multiple threads. This scalability is essential for supporting growing user bases and evolving business requirements.

Implementing Java Multithreading:

Thread Creation: Create and manage threads using Java's built-in Thread class or the Runnable interface.

Synchronization: Use synchronization mechanisms such as synchronized blocks and locks to control access to shared resources and prevent data corruption in multithreaded environments.

Thread Pools: Implement thread pools to manage the lifecycle of threads efficiently and avoid the overhead of thread creation and destruction.

Concurrency Utilities: Leverage Java's concurrency utilities, such as the Executor framework and Concurrent collections, to simplify the development of multithreaded applications and improve performance.

Testing and Debugging: Thoroughly test multithreaded code to identify and resolve concurrency issues, such as race conditions and deadlocks, using tools like Java VisualVM and thread dump analysis.

The Importance of Java Training Courses: Java training courses provide developers with the knowledge and skills needed to effectively utilize multithreading in Java applications. These courses cover essential topics such as thread management, synchronization, concurrency control, and best practices for writing multithreaded code. Additionally, they provide hands-on experience through practical exercises and projects, allowing developers to gain proficiency in implementing multithreading solutions that enhance application performance and scalability.

Conclusion: Java multithreading is a powerful technique for improving application performance, responsiveness, and scalability. By understanding the fundamentals of multithreading and mastering advanced concepts through Java training course, developers can unlock the full potential of Java multithreading to create high-performance and scalable applications that meet the demands of today's digital world.

[cognitiveprogrammer]

Multithreading in C++11 Series 

[aaksconsulting]

Mastering Real Multithreading In Python - Tips And Tricks For Optimal Performance

Are you tired of slow and unresponsive Python programs? Are you ready to take your programming skills to the next level by mastering real multithreading in Python? Look no further!

In this blog post, we will share tips and tricks for achieving optimal performance with real multithreading. Whether you’re a beginner or an experienced developer, these techniques will help you elevate your programming game and create lightning-fast applications that impress even the toughest critics. So let’s dive in and become masters of real multithreading in Python!

INTRODUCTION TO MULTITHREADING IN PYTHON

Multithreading is a powerful tool that can help you write more efficient code. In Python, the standard library provides the threading module, which allows you to create and work with threads. In this article, we’ll look at some tips and tricks for working with threads in Python.

First, let’s take a look at what a thread is. A thread is simply a unit of execution. When you create a new thread, it starts running in parallel with the main thread of your program. This can be useful when you have tasks that are independent of each other and can be run concurrently.

For example, let’s say you’re writing a program that downloads files from the web. You could have one thread responsible for downloading the files, while another thread handles extracting data from the downloaded files. By using two threads, you can make better use of your computer’s resources and potentially speed up the overall execution of your program.

Of course, working with threads also comes with its own set of challenges. For example, if two threads try to access the same data at the same time, they may end up corrupting that data. To avoid this, we need to use synchronization primitives such as locks and semaphores. We’ll discuss these later on in the article.

Now that we know what threads are and why they can be useful, let’s look at how to create and work with them in Python.

OVERVIEW OF MULTITHREADING BENEFITS

Python’s “threading” module allows for the creation of threads within a Python program. These threads can run concurrently, which can lead to performance gains on multicore processors. Multithreading can also be used to improve responsiveness in GUI applications.

There are several benefits to using multithreading in Python programs:

Concurrency: Threads can run concurrently, which can lead to more efficient use of processor resources on multicore processors.

Improved responsiveness: Threads can be used to improve the responsiveness of GUI applications by running tasks in the background while the main thread continues to process user input.

Better utilization of resources: Threads can be used to better utilize system resources such as network and I/O devices. By running multiple threads, these resources can be shared among the various threads and utilized more efficiently.

Reduced latency: Threads can be used to reduce latency in applications that need to perform time-sensitive tasks. By running multiple threads, tasks can be executed in parallel, which can lead to shorter overall execution times.

TYPES OF THREADS AND HOW TO CREATE THEM IN PYTHON

There are two types of threads in Python: the main thread and daemon threads. The main thread is the one that starts when the program begins execution. Daemon threads are created by the main thread and run in the background. They are used to perform tasks such as garbage collection and logging.

Threads can be created in Python using the threading module. To create a thread, you need to instantiate a Thread object. The constructor takes an optional argument, which is a function that will be run by the thread. If no function is provided, the thread will simply exit when it is started.

Once you have created a Thread object, you can start it by calling its start() method. This will cause the function that was passed to the constructor to be executed by the thread. If no function was passed, the thread will simply exit when it is started.

If you want to wait for a thread to finish before continuing execution of your program, you can call its join() method. This will block until the thread has finished running. Note that if you try to join() a daemon thread, your program will never terminate since daemon threads do not ever finish running (unless they are terminated with an unhandled exception).

SYNCHRONIZATION TECHNIQUES

There are many synchronization techniques that can be used to achieve optimal performance in Python. Here are some tips and tricks to help you get the most out of your multithreading applications:

1. Use locks wisely. Locks are a necessary evil in multithreaded programming. They are useful for protecting critical sections of code, but they can also lead to deadlocks if not used correctly. When using locks, always try to acquire them in the same order to avoid deadlocks. 2. Use thread-safe data structures. Some data structures, such as lists and dictionaries, are not thread-safe. This means that if multiple threads try to access and modify them concurrently, strange things can happen. To avoid this, you can use thread-safe versions of these data structures, such as the Queue class from the standard library. 3. Use the new asyncio module. The asyncio module was added in Python 3.4 and it provides a powerful framework for writing concurrent code using coroutines. If you’re targeting Python 3.4 or newer, this is definitely the way to go for optimal performance.

KEY PERFORMANCE OPTIMIZATIONS FOR MULTITHREADED APPLICATIONS

Python’s standard library provides a number of synchronization primitives including locks, semaphores, and events. In this section, we’ll cover some key performance optimizations that can be made when using these synchronization primitives in multithreaded applications.

One optimization that can be made is to use a lock object’s acquire() method with the blocking argument set to False . This will cause the acquire() method to return immediately if the lock is already held by another thread. If the lock is not available, then the current thread will continue executing without blocking. This can be useful in situations where it is not critical for the current thread to acquire the lock.

Another optimization that can be made is to use a semaphore object’s release() method with the count argument set to a value greater than 1 . This will release the semaphore multiple times, which can be helpful in situations where multiple threads are waiting on the semaphore. Releasing the semaphore multiple times can help to avoid unnecessary context switches between threads.

It is important to note that using too many synchronization primitives can actually hurt performance. When used excessively, synchronization primitives can introduce a significant amount of overhead into an application. Therefore, it is important to use them judiciously and only when absolutely necessary.

DEBUGGING TIPS AND PRACTICES

When it comes to debugging multithreaded Python applications, there are a few practices that can make your life much easier. Firstly, it’s important to understand the basics of the Python threading model. The Python Global Interpreter Lock (GIL) ensures that only one thread can execute Python code at a time. This means that if you’re trying to debug a multithreaded application, you need to be aware of the potential for threads to block each other.

Another important practice is to use a tool like pdb or ipdb when debugging multithreaded applications. These tools allow you to set breakpoints in your code and inspect the state of your application at those points. This can be extremely helpful in understanding what is happening in your code and why it is not working as expected.

It’s often useful to run your application under a profiler like Profile or pyprof2calltree. This can help you identify which parts of your code are taking up the most time, which can be helpful in pinpointing areas that need optimization.

CONCLUSION

We have come to the end of our discussion on mastering real multithreading in Python. With these tips and tricks, you will be able to optimize your code for maximum performance. Working with threads can be tricky, so it is important to understand the fundamentals before diving into complex operations like thread pooling or synchronization.

If you use these techniques correctly, you can drastically improve your program’s execution time and maintain a level of concurrency that suits your needs perfectly.

[transhermetic-witchery]

Next shape, better shape? Such a shape to me.

Next shape, better shape; if I am are we?

Next shape, this shape? poetry to be.

Next shape, brk. pat.? Or let it simmer see.

bp.

Loud sound in loud times, better shape indeed?

To ignore, to be a bore, takes a guess at what's in store.

Nearly naughty, a little game of fear.

Practice for what, exactly? On this am I sore?

explain it to the judge, our civil lesson learned.

Weaving, blending, thread or multicore?

Will the cells explore?

Looking onward, looking outword, with doings of done be proud.

But wilted down to value, when measured in near now.

This I shit an artifact, an input metaphor. At full scale, a life entire, but curated for what's in store.

To be honest in a way which resonates, oft attracts attention of they who do bite back.

Flavors of nothing follow, decomposed to see.

Uhhhhh... I was gonna add something about culture of fear but lost the train of thought. Perhaps come back in a future edit?

Proud to be an American? I sure would like to be...

How balance two or three extremes?

How balance N extremes, where N is in aleph null?

Heat death they call growing entropy, that lovely rich brown soil from come me.

Me and we and we and me and pipes like blood or breath.

What could I have meant? Self digesting memory.

Finding self a suffering, too deep and cruel to enjoy, opt right out, we can stop the ride in event of an emergency.

On your tour you'll see things strange, and silly, and raunchy perhaps. Estrange the those what won't endure.

pb.

Is this worth attention? Quite a question if honest asked. ready to digest the answer that comes back.

Let's just do it, trust games they can ensue, trust them to do the same or different as of before.

How painful is it to speak to me. Is this status? Do we like this? Honest question.

bp-soft.

I do not need success, I need comfort, I need meaning. Except when I don't. Except when many many exceptions because motivation isn't logical and I think that's good news.

To be vulnerable and unpecked is the need need be fulfilled. Not mere safety, too easy becomes control.

bp.

hell for seagulls.

bp.

Not a punishment. The word break is actually too harsh for this. May stoke anxiety past sandbox thresholds. Oh, no, that's still in mentalese.

Alrighty, that's the letter. Sincerely, Love, With Great Supplication, ad nauseum.

Hello World.

Lorem Ipsum.

Null Island.

Three Empties Confirmed.

goodnight.

- "to me from me" 2023

[nobalg]

Rant of a Frog

Bruh, Yo Java Guys, why you faking it bruh.

Why you trying to be someone else and failing so hard on it.

May be change is the only way we can be better than yesterday

But your base was so so rigid bruh, its making things difficult for the older community and the newer community is unable to connect with their Grand uncles bruh.

Its obviously clear that at last you accepted the fact that JavaScript's functional way and callbacks were legit from the start. You trying so hard to make Java geeks believe that reactivity is something which will change the world, but I want you to intimate your audience that node js was doing it from so long, concepts like event loop were lying around there from so long. Its just that you were may be late to the party, or have to follow the trend to make the developer's stick to you. May be you want to attract Js developers with your functional styles and lambdas introduction, but this seduction is far from what you planned for.

You only can boost the utilization of multithreaded programs, and utilizing the multicores of a system. But let me tell you this, they will eat you up in this aspect as well.

Why the default was introduced man, in interfaces? Why you had to break your own principles?

Introduce a new concept bruh, but don't alter the story mate, why create such a story, you have created holes in your happy story, while trying to replicate what JS (even though single threaded and have a reputation of having browser language only) have done from long ago.

Functional Programming , ehh, what's wrong with your OOPS man, you used to boost of. Han?

Even though if somebody tries to start from fresh on this path, other then Josh long or Venkat, no legit resources, no community, no answers on Stack overflow for legit questions. Just Blackbox.

So you want to make some threads free ehh, thats why reactivity, ehh,

onErrorContinue ehh, you yourself say the biggest anomaly ehh.

Please comeback when you are consolidated bruh, We devs dont know how to estimate around you with such open things, We don't know how to handle Exceptions efficiently, Venkat said, if you can't handle or sometimes feels like imperative is better , Go for it.

He said if you want only happy path, then functional will be good.

But ehh, you know right, nothing is happy about programming, there are exceptions , errors , which are meant to be handled.

Bruh, either grow exponentially, or die, as We devs are not able to justify, why you are actually good, but what it will cost to get married with you and your unstability may be.

Angry

[mensajeroseis]

how do u get fo3 to run on pc . ive tried damn near everything and ur the only fo3 enjoyer i follow

im honored to be the fo3 enjoyer u follow....so onto the topic at hand.

my first general recommendation is to buy fo3 on GOG and not steam. Steam's version is literally so fucked on various levels, GOG's fixes a lot of the issues it has (defunct GFWL, memory space issue, plus its drm free !). Supposedly it also runs fine thru XBOX PC gamepass, if that's an option for you that youre open to trying.

now some PCs can run it just fine out the gate w/ GOG. Mine did not lol, but also my laptop is almost 6 years old saur... I'll be real, I don't even remember what I did to fix it. I was maddogging the fuck out of suggestions and googling possible answers, trying everything. Like u...

my general suggestions are :

the multicore fix ; get into Documents>Games>Fallout3 and make sure you change bUseThreadedAI=0 to bUseThreadedAI=1 and add iNumHWThreads=2 in the FalloutPrefs.ini ; it really doesn't like more than 2 threads. Running it in WinXP/7/8 SP3 compatibility mode as an Administrator may also make it work. Also install the Win10 compatibility mod on nexus. Look thru this thread for suggestions. There's a general post on the fallout subreddit of ways to get all the games to work, but I can't find it so when I do. I'll add it. I'll probably come back later to add more things I can think of. For the sake of everyone who wants to play fo3 on PC 😌

A lot of people suggest just playing it via TTW (the option is always there). I haven't personally, I played fo3 on console for years. So I wanted to solely play it unrelated to FNV also. Once I got it running, it ran perfectly fine. The only crashes I ever have are related to mods (which is why ive been too lazy to go back and fix it for a few months LMFAOO)

unrelated note : here is a personally curated mod list i made (i also run the game from my mod manager rather than thru the launcher)

[govindhtech]

AMD Ryzen 9 9950x Vs AMD Ryzen 7 9800x3d For Gaming

Ryzen 7 9800x3d vs. 9950x Features

Performance Benchmarks

Multitasking and Efficiency

Due to its higher core and thread count, the Ryzen 9 9950X excels in multithreaded workloads. Regarding benchmarks:

With approximately 40,924 points, the Cinebench R23 Multi-Core outperforms previous CPU generations.

Jobs like software development, 3D rendering, and video editing benefit from its performance increases over its predecessors.

Video Game Performance

The Ryzen 7 9800X3D is gaming-focused.

It beats Intel's Core Ultra 9 285K by 45% in Cyberpunk 2077.

Gaming Performance: It outperforms the 7800X3D by 11% in various games.

Gaming is high-performing and energy-efficient because it uses less electricity.

Use Case Suggestions

The Ryzen 9 9950X is great for content producers, multitaskers, and multicore users. Its integrated graphics provide basic display capabilities without a GPU.

Ryzen 7 9800X3D: For top-performing gamers. Gaming benefits from its 3D V-Cache technology include faster frame rates and smoother gameplay.

9950x vs. 9800x3d Ryzen 7

AMD Ryzen 9 9950X advantages

Professionals and amateurs who need multitasking performance should select Ryzen 9 9950X. Virtual machines, software compilation, video editing, and 3D rendering benefit from AMD Zen 5's 16 cores and 32 threads. Its huge L2+L3 cache and 5.7 GHz boost speeds help productivity programs. Compatible with new gear, it supports DDR5 and PCIe 5.0. Additionally, TSMC's 4nm production efficiency makes it more power-efficient than prior high-core CPU generations.

AMD Ryzen 9 9950X cons

Despite its power, the 9950X has limitations. First, its price makes it excessively pricey for casual users and gamers. The load power consumption can approach 170W, necessitating a robust cooling system and possibly a high-end PSU. It cannot outperform lower-core processors with 3D V-Cache like the 9800X3D in gaming due to cache-sensitive performance limits. Thermal throttling may occur without cooling because it runs hotter, reaching 90–95°C under load.

AMD Ryzen 7 9800X3D advantages

Gaming-focused With its massive 3D V-Cache, AMD Ryzen 7 9800X3D performs well. The 96MB of L3 cache above the compute units delivers minimal latency and high frame rates, especially in CPU-bound scenarios like competitive esports or 1080p gaming. Even with 8 cores and 16 threads, its cache layout makes it a great gaming CPU, often surpassing CPUs with more cores.

It operates cooler, is more efficient for daily usage, and uses less electricity (about 120W). This is a great choice for gamers who want high-performance gaming without the heat and power consumption of flagship CPUs.

Bad things about AMD Ryzen 7 9800X3D

The 9800X3D's inflexibility is its biggest weakness. Games are its strength, but 3D rendering, software building, and multitasking require more cores and threads. Due to heat, the 3D V-Cache limits overclocking, and its base and boost clocks are lower than the 9950X. Streamers and gamers that have CPU-intensive occupations may be constrained by the 8-core design. Finally, non-gamers who could benefit more from a general-purpose processor may not like the price-performance ratio.

In conclusion

Your major use case high-performance gaming or demanding productivity tasks should guide your pick.

The future-proof Ryzen 9 9950X multi-core processor powers productivity, streaming, multimedia production, and performance. Ryzen 7 9800X3D delivers top-notch gaming at 1080p or competitive frame rates with low heat, noise, and power.

[elusivehawk]

Scaling Inter-Entity Interactions for Multicore Systems

Picture this: you’re making a game. Your game only runs on a single thread, but you take a look at Ryzen and realize that newer CPUs will be multicore beasts. So, you decide to go ahead and try to multithread it. After separating the game logic, rendering, and other miscellaneous things, you’re still at best only utilizing a quad-core, and unevenly at that. So you decide to multithread the game logic, and what do you get? Race conditions, inconsistent behavior, and just plain ol’ crashing. What now?

This approach is designed to help with multithreading entity interactions. Mind you, it’s not going to be perfect.

Core Data Structures

Let’s get the general data structures out of the way.

Entity: This represents a single player, enemy, or other object in the game world. It contains AI and data about itself. Every game tick, it recalculates itself, including what it’s suppose to be doing.

World: Functionally speaking, a list of entities. We don’t care about the details because that’s very implementation-specific.

Message: The secret sauce that makes this system work. Contains a message type (probably an int), a pointer to the entity sending it, and a pointer to a struct containing the actual data*. The message type is put through a switch statement which then does the appropriate action and casts the data struct appropriately.

Now then, on to the rest.

The Core Idea

The core idea is to take entity interactions and put them into little update packets, or messages. These messages are sent to the entity in a thread-safe manner, and then through a new three-phase update system, the messages are read and actions are made. By taking this approach, entities can be safely updated on any thread.

Dividing the update phases into three requires a little discipline, but bear with me. The three stages are the pre-update, update, and post-update.

In the pre-update phase, typical bookkeeping is done. The entity determines where it should be going, what it should attack, things like that. Potion effects should be done here, too.

In the update phase, actions are taken and messages are passed. If an entity is attacking, it sends a message to the attacked entity to lower its health.

In the post-update phase, messages are read and data is modified. Check for the entity’s death here (and also pre-update if there’s a poison effect or something)

To further simplify:

The pre-update phase is for updating self.

The update phase is for updating others.

The post-update phase is for updating itself again to account for the update phase.

Or, to put it into pseudocode:

parallel_for entity in world:

    entity.preUpdate(delta);

//TODO Sync all threads

parallel_for entity in world:

    entity.update(delta);

//TODO Sync all threads

parallel_for entity in world:

    entity.postUpdate(delta);

Is update even a word anymore? Anyway, you might be wondering how this can be done, won’t there be a lot synchronization issues? And the answer is, maybe. And that’s where lock-free queues come in to play. Each entity should have its own message queue that utilizes atomic integers to ensure a lock-free experience. Stefan Reinalter goes over a similar queue system he uses for a job system, and you can read about it here.

However, that job system is for a system in which all involved threads aren’t in lock-step, whereas this system is. So, the queue doesn’t have to be so complicated. A simple list with an atomic integer to keep track of the next element is sufficient. Once the update phase is done, the post-update phase can be used to read from the queue without worrying about another thread writing to it. At the end of the post-update phase, the queue can be cleared and reused in the next frame.

Conclusion

And that about covers it. If you have any questions, drop a comment below. I probably need to further clarify a thing or two.

Now if you’ll excuse me, I have to go back to crying over Vulkan.

* Yes, this does need another allocator. Worry about that yourself.

[noreencarreno-blog]

Batch Free Flac To MP3 Converter, Assist Watched Folder Changing

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[programgamer]

Something I hate about my brain is that occupying my thoughts with something else just doesn’t work, my brain is like a multicore processor, if I do something to try and distract myself then the other threads will just keep thinking about the thing so I kinda just have to wait Bad Thoughts out