Threading In Computer Science

Defining Threading in Computer Science

How Threading Works: An Engaging Illustration

  Coding Example:
  // C++ code for thread creation and joining
  #include 
  #include 
  void threadFunction()
  {
    std::cout << "Welcome to concurrent world\n";
  }
  int main()
  {
    std::thread t(threadFunction);
    t.join();
    return 0;
  }

Real-World Examples of Threading in Computer Science

Threading plays a key role in the functioning of multiple sectors and domains in our digital world. From enhancing the user interface responsiveness to playing a pivotal role in high-performance computing, threading’s applicability is vast and indispensable.

Examining an Example of Threading in Computer Science

To truly grasp the power of threading, let's delve into the intricacies of a real-world example related to online banking. Online banking systems handle millions of concurrent users who are performing numerous operations such as fund transfers, balance checks, bill payments, and more. How is this managed smoothly? The answer lies in threading.

Let's have a closer look at how this unfolds:

• An online banking system has to be constantly live and responsive. Any delay can be catastrophic to operate. Imagine a scenario where a user starts a transaction but on the server-side, the software waits for another user's transaction to finish before catering to the new one. This could lead to significant delays, something a banking system cannot bear. Here, threading allows multiple user transactions to be processed concurrently, making banking operations rapid and efficient.
• In such scenarios, each transaction initiated by a user is treated as a separate thread. This ensures real-time processing, reducing delays and enhancing user experience.

In essence, threading is the silent engine that powers the seamless operations one observes in an online banking system.

How Threading is Applied in Everyday Computer Operations

Threading's role isn't confined to high-scale applications like banking systems. It is integral to everyday computer operations too. From the seamless functionality of operating systems to the smooth performance of web browsers and word processors, threading is everywhere.

Let's consider another everyday example: web browsers. When you open multiple tabs in a browser, each tab is typically handled by a separate thread. This means you can load multiple web pages concurrently, enjoy an uninterrupted Youtube video on one tab while a heavy web application loads on another.

These examples serve to highlight how threading, while not directly visible to the end user, remains an inherent part of modern computing, making it more efficient and dynamic.

Different Types of Threading in Computer Science

Threading in computer science opens up a world of parallel execution and concurrent processing, but not all threads are the same. Different types of threads exist, each lending itself to distinct use cases. Broadly speaking, the three primary types of threads are: User Threads, Kernel Threads, and Hybrid Threading. Understanding these is fundamental to the comprehensive knowledge of threading in computer science.

Exploring the 3 Kinds of Threads in Computer Science

Let's delve into these three types of threads in order to gain a deeper understanding of threading in computer science.

User Threads

User threads, as the name implies, are threads managed entirely by userspace libraries. They have no direct interaction with the kernel and are managed outside the operating system. They are faster to create and manage as they don’t need to interact with the system kernel. Some common user level thread libraries include POSIX Pthreads and Microsoft Windows Fibers.

Kernel Threads

Kernel threads, on the other hand, are managed directly by the operating system, providing benefits such as support for multi-processor systems and system-wide scheduling. However, these benefits come at the cost of slower performance due to the overhead of context-switching between kernel and user mode.

Hybrid Threading

Recognising the different trade-offs between user and kernel threads, some systems employ a hybrid model, where multiple user threads are mapped onto a smaller or equal number of kernel threads. This allows programmers to create as many user threads as needed without the overhead of creating the same number of kernel threads, while still gaining the advantages of kernel level scheduling.

Comparing and Contrasting Different Threads

Although the three types of threads share some similarities, their features, benefits and drawbacks differ greatly. An understanding of these distinctions is critical for efficient and effective application of threads in computer science.

Comparisons of different thread types are best demonstrated via a tabular representation:

User threads are the fastest, but their scheduling isn't controlled by the kernel, which makes it difficult for the system to take global decisions about process scheduling. Conversely, kernel threads have kernel-level scheduling, so they can be managed more efficiently by the operating system, but they also take longer to create and destroy due to kernel overhead.

Lastly, Hybrid Threading models seek to strike a balance by mapping many user-level threads onto an equal or smaller number of kernel threads. This offers more flexibility than pure User or Kernel threading, resulting in efficient management and lower overheads.

Understanding the Role of Starvation in Computer Science Threads

Starvation in computer science is a real challenge that can hamper the efficacy of computer programs and systems. While it is an innate part of the world of threading, starvation is often perceived negatively because it results in unfair allocation of processing time among different threads, thus affecting the performance and execution speed of programs.

Defining Starvation in Context of Computer Science Threads

Starvation is a scenario in multi-threading environments where a thread is constantly denied the necessary resources to process its workload. Specifically, if a thread doesn't get enough CPU time to proceed with its tasks while other threads continue their execution unhindered, this thread is said to be experiencing starvation.

Management of resources among multiple threads weaving in and out of execution is a complex process. Scheduling algorithms determine the sequence of thread execution, and these can sometimes lead to a scenario where a thread becomes a low priority and is denied necessary resources. This usually happens when some threads take up more resources or CPU time than others, leaving less space for the remaining threads.

Algorithmically speaking (though it is simplified), starvation is akin to the below condition, where a thread \( t \) is not allocated CPU time over a given period \( p \): \[ \int_{0}^{p} CPU(t) \, dt = 0 \]

In essence, the role of starvation in threading is a balancing act that maintains the ebb and flow of thread execution in multi-threading environments. However, it is usually a situation to be mitigated or avoided, as it can lead to inefficiencies and delays in task completion.

Starvation in Threads: Causes and Consequences

Identifying causes and understanding consequences is critical in addressing and resolving any issue, and thread starvation is no exception. Since starvation pertains to the unfair or inadequate allocation of resources to threads, its causes are usually rooted in flaws or biases in the process scheduling algorithm.

Scheduling algorithms are designed to prioritise certain threads, based on various properties such as process size, priority level or time of arrival in the queue. Sometimes, high-priority threads can dominate resources, leaving low-priority threads languishing without receiving the necessary CPU time—a typical cause of starvation.

Another common cause of thread starvation is related to thread priority. Certain streaming or gaming applications, for example, may be coded to take priority, leaving other applications with fewer resources.

Often, mutual exclusion could lead to thread starvation. If two threads require the same resource and one gets access to it for an extended period, the other will starve indefinitely until the resource becomes available again.

Now, what are the consequences of thread starvation? This doesn't merely slow down individual threads; it often leads to significant performance degradation of the entire process or system. A thread undergoing starvation can delay dependent threads and processes, leading to a ripple effect of reduced performance. For example, a web server could start performing poorly if critical threads handling client requests undergo starvation.

Moreover, starvation could lead to complete process termination in severe cases. This could occur when a thread doesn't get the necessary resources to reach a certain system requirement or fails to meet a timing constraint—an extreme case being program failure.

Conclusively, starvation could wreak havoc on thread execution and program performance if not identified and handled promptly. Therefore, it is crucial to anticipate the possibility of starvation during thread handling and include preventive or mitigating measures in the programming or system design phase.

Practical Aspects of Threading in Computer Science

In computer science, threads are not just theoretical concepts. They're vital components that underpin many aspects of practical software development. The optimal usage of threads can significantly improve the efficiency of programs, while improper use can lead to performance degradation or even failure. The practical aspects of threading include managing starvation and implementing effective threading techniques. These are critical for writing efficient and robust software applications.

Mitigating Starvation in Computer Science Threads

In threading, starvation is a critical issue that can lead to impaired performance or even failure of applications. However, it is also a preventable one, and with the right techniques, its negative effects can be largely mitigated.

One effective solution to counteract starvation is careful design and implementation of scheduling algorithms. Going beyond simple priority-based scheduling, algorithms such as Round Robin or the Shortest Job First algorithm can prevent starvation by ensuring fair distribution of CPU time amongst threads. In Round Robin scheduling, each thread is given an equal slice or 'quantum' of CPU time. The Shortest Job First algorithm, on the other hand, gives preference to threads with smaller processing demands.

Consider using priority aging, a technique that progressively increases the priority of waiting threads, ensuring that no thread waits indefinitely. Another way is to implement feedback mechanisms in scheduling algorithms where starving threads are gradually elevated in priority.

Let’s look at a piece of sample code that can help illustrate the concept of starvation:

Thread highPriority = new Thread(() -> {
   while (true) {
     count++;
   }
});
highPriority.setPriority(Thread.MAX_PRIORITY);

Thread lowPriority = new Thread(() -> {
   while (true) {
     count++;
   }
});
lowPriority.setPriority(Thread.MIN_PRIORITY);

lowPriority.start();
highPriority.start();

In the code snippet above, two threads are started: one with low priority and the other with high priority. Since these two threads are competing for the same resource (CPU), the thread with higher priority will consume most of the CPU time, while the low priority thread will eventually starve, and the system will suffer from performance degradation. Mitigation strategies like re-adjusting the priority levels or re-configuring the scheduler can help handle such situations in a better way.

Effective Threading Techniques for Better Performance

The effective use of threads can significantly enhance the performance of your programs. The following advanced techniques and methodologies can help you optimise your use of threads.

First, always consider the problem of Thread Overhead. Modern operating systems and programming environments improve thread performance, but there is still a cost associated with thread creation, context-switching, and termination. It's more prudent to have a fixed set of worker threads to handle tasks, rather than continuously ending and creating new threads, as in a Thread Pool model.

To illustrate, let's consider two different threading solutions to handling multiple incoming network requests:

// Initial Approach
for (int i = 0; i < requests.size(); i++) {
    new Thread(new NetworkRequestHandler(requests.get(i))).start();
}

// Thread Pool Approach
ExecutorService executor = Executors.newFixedThreadPool(10);
for (Request request : requests) {
    executor.execute(new NetworkRequestHandler(request));
}

In the initial approach, a new thread is created for each request, leading to substantial overhead due to continuous thread creation and termination. The Thread Pool approach, however, reuses a set of threads to process incoming requests, thereby reducing overhead and improving overall system performance.

Furthermore, use synchronization judiciously. Overusing synchronization constructs (like locks or mutexes) can lead to thread contention, where multiple threads are waiting for a shared resource, potentially leading to Deadlocks or Starvation.

Finally, try to take advantage of thread-local storage, a method which provides separate storage for variables for each thread. While this might slightly increase memory usage, it can drastically reduce the need for synchronization and mitigate problems like contention or race conditions.

// Before Using Thread Local
public class UserContext {
    private static Session session;

    public static void setSession(Session s) {
        session = s;
    }

    public static Session getSession() {
        return session;
    }
}
// After Using Thread Local
public class UserContext {
    private static ThreadLocal userSession = new ThreadLocal<>();

    public static void setSession(Session s) {
        userSession.set(s);
    }

    public static Session getSession() {
        return userSession.get();
    }
}
  

Conclusively, threading can greatly enhance or impair your programs' performance, depending on how effectively you use it. It's therefore crucial to understand and use threading techniques to write efficient, robust, and scalable software applications.