What are the latest trends and innovations in multithreading and parallel computing?

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Multithreading is a technique that allows a program to execute multiple tasks concurrently, using multiple processors or cores. It can improve the performance and responsiveness of applications, especially for complex and intensive tasks. However, multithreading also introduces some challenges and trade-offs, such as managing the CPU and memory utilization by threads. In this article, you will learn how to measure and optimize the CPU and memory usage of your multithreaded programs, and what are the latest trends and innovations in multithreading and parallel computing.

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1 Why CPU and memory utilization matter

The CPU and memory are two critical resources that affect the speed and efficiency of your multithreaded programs. The CPU is responsible for executing the instructions of your code, while the memory is used to store the data and variables of your program. If your threads use too much CPU or memory, they may cause bottlenecks, contention, or even crashes. On the other hand, if your threads use too little CPU or memory, they may waste resources and underutilize the potential of your hardware. Therefore, you need to monitor and balance the CPU and memory utilization by threads, to achieve optimal performance and scalability.

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Feng Yuan
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Such authorless articles are useless, trying to cover too many things in a single place. Instead, it would be much useful to cover a single narrower topic in depth.

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Ken Tindell

CTO at Apitronix Semiconductor Ltd.
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Exactly right. Embedded systems is an area already using multithreading (or better named “multitasking”) and the issues there aren’t covered in this. For example, semaphores are inappropriate for real-time systems and special mechanisms are used to avoid priority inversion.

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2 How to measure CPU and memory utilization by threads

There are different tools and methods that you can use to measure the CPU and memory utilization by threads, depending on your operating system, programming language, and framework. For example, you can use the Task Manager or Performance Monitor in Windows, the top or ps commands in Linux, or the Activity Monitor in macOS, to view the overall CPU and memory usage of your processes and threads. You can also use specialized tools, such as Visual Studio, Xcode, or Valgrind, to analyze the CPU and memory usage of your code in more detail. These tools can help you identify the threads that consume the most resources, the sources of CPU or memory leaks, and the potential areas for improvement.

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3 How to optimize CPU and memory utilization by threads

Once you have measured the CPU and memory utilization by threads, you can apply some techniques and best practices to optimize them. For example, you should determine the optimal number of threads for your task using formulas, benchmarks, or adaptive algorithms. Additionally, thread pools or executors can help you manage the creation and reuse of threads. Synchronization mechanisms, such as locks, semaphores, or barriers, should be used sparingly and with the smallest scope possible. It is also important to use data structures and algorithms that are suitable for multithreading, such as concurrent collections, atomic variables, or parallel streams. Lastly, memory management techniques like garbage collection, smart pointers, or memory pools can help avoid memory leaks or fragmentation.

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4 What are the latest trends and innovations in multithreading and parallel computing

Multithreading and parallel computing are constantly evolving fields that offer new opportunities and challenges for developers. The latest trends in these fields include cloud computing and distributed systems, GPU computing and heterogeneous computing, and functional programming and reactive programming. Cloud computing and distributed systems can increase scalability, availability, and fault tolerance of applications, but also require dealing with latency, security, consistency, and coordination among different nodes. GPU computing and heterogeneous computing can offer higher performance, lower power consumption, or specialized functionality for certain tasks, but require learning new programming models, languages, or frameworks. Functional programming and reactive programming emphasize the use of pure functions, immutable data, and asynchronous streams to express parallel computations. While these paradigms can help avoid or simplify common problems of multithreading, they also require adopting a different mindset, syntax, or style of coding.

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5 How to learn more about multithreading and parallel computing

If you want to learn more about multithreading and parallel computing, there are a variety of resources that can help you expand your knowledge and skills. For instance, you can explore books such as The Art of Multiprocessor Programming by Maurice Herlihy and Nir Shavit, Parallel and Concurrent Programming in Haskell by Simon Marlow, or Java Concurrency in Practice by Brian Goetz et al. Moreover, there are courses such as Introduction to Parallel Programming by Udacity, Parallel Programming in Java by Coursera, or Concurrent Programming in Erlang by FutureLearn. Additionally, there are blogs and podcasts like InsideHPC, Parallel Programming Zone, or The Changelog that provide the latest news, insights, and tips on multithreading and parallel computing from different experts and practitioners.

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Peter Lyons Kehl

Rust Software Engineer; Investor; Speaker at Rustlang Conf42; Technical Reviewer; Loving Rust
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Look at Rust. It makes applications synchronization-safe memory-safe by design. While functional programming and immutable data structures help, you do need some (controlled) mutability. Go (Golang) and Rust offer channels for communication between threads. See "Effective Rust" > "Item 17" for more.

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What are the latest trends and innovations in multithreading and parallel computing?

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1 Why CPU and memory utilization matter

2 How to measure CPU and memory utilization by threads

3 How to optimize CPU and memory utilization by threads

4 What are the latest trends and innovations in multithreading and parallel computing

5 How to learn more about multithreading and parallel computing

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