For many years, maximum computer performance was limited largely by the speed of a single microprocessor at the heart of the computer. As the speed of individual processors started reaching their practical limits, however, chip makers switched to multicore designs, giving the computer the opportunity to perform multiple tasks simultaneously. And although OS X takes advantage of these cores whenever it can to perform system-related tasks, your own applications can also take advantage of them through threads.

While one thread is blocked and waiting, a second thread in the same task can run. 5: Multiple processes without using threads use more resources. Multiple threaded processes use fewer resources. 6: In multiple processes each process operates independently of the others. One thread can read, write or change another thread's data. Careful placement of threads, especially when hyperthreading is enabled, can improve performance. NUMA Considerations. Computers with multiple, multi-core processors have Non-Uniform Memory Access (NUMA), i.e. Memory access time depends on the location of physical memory for the process relative to the processor where it is running. In the world of 3D product design and 3D printing this can make a huge difference - far greater than any difference due to CPU cycle time. But so far there are very few applications that can actually do multi-threading. It would be nice to see a list of apps that are multi-threaded, since these will run much faster than single threaded ones. Aug 31, 2020. Folx PRO has the capability to split your downloads into multiple threads, which can significantly improve the speed at which they download. Folx PRO is fully compatible with Apple Music (formerly iTunes). Folx PRO can save your login credentials for multiple torrent trackers and websites you use on a regular basis.

What Are Threads?

Threads are a relatively lightweight way to implement multiple paths of execution inside of an application. At the system level, programs run side by side, with the system doling out execution time to each program based on its needs and the needs of other programs. Inside each program, however, exists one or more threads of execution, which can be used to perform different tasks simultaneously or in a nearly simultaneous manner. The system itself actually manages these threads of execution, scheduling them to run on the available cores and preemptively interrupting them as needed to allow other threads to run.

From a technical standpoint, a thread is a combination of the kernel-level and application-level data structures needed to manage the execution of code. The kernel-level structures coordinate the dispatching of events to the thread and the preemptive scheduling of the thread on one of the available cores. The application-level structures include the call stack for storing function calls and the structures the application needs to manage and manipulate the thread’s attributes and state.

In a non-concurrent application, there is only one thread of execution. That thread starts and ends with your application’s main routine and branches one-by-one to different methods or functions to implement the application’s overall behavior. By contrast, an application that supports concurrency starts with one thread and adds more as needed to create additional execution paths. Each new path has its own custom start routine that runs independently of the code in the application’s main routine. Having multiple threads in an application provides two very important potential advantages:

Multiple threads can improve an application’s perceived responsiveness.
Multiple threads can improve an application’s real-time performance on multicore systems.

If your application has only one thread, that one thread must do everything. It must respond to events, update your application’s windows, and perform all of the computations needed to implement your application’s behavior. The problem with having just one thread is that it can only do one thing at a time. So what happens when one of your computations takes a long time to finish? While your code is busy computing the values it needs, your application stops responding to user events and updating its windows. If this behavior continues long enough, a user might think your application is hung and try to forcibly quit it. If you moved your custom computations onto a separate thread, however, your application’s main thread would be free to respond to user interactions in a more timely manner.

With multicore computers common these days, threads provide a way to increase performance in some types of applications. Threads that perform different tasks can do so simultaneously on different processor cores, making it possible for an application to increase the amount of work it does in a given amount of time.

Of course, threads are not a panacea for fixing an application’s performance problems. Along with the benefits offered by threads come the potential problems. Having multiple paths of execution in an application can add a considerable amount of complexity to your code. Each thread has to coordinate its actions with other threads to prevent it from corrupting the application’s state information. Because threads in a single application share the same memory space, they have access to all of the same data structures. If two threads try to manipulate the same data structure at the same time, one thread might overwrite another’s changes in a way that corrupts the resulting data structure. Even with proper protections in place, you still have to watch out for compiler optimizations that introduce subtle (and not so subtle) bugs into your code.

Threading Terminology

Before getting too far into discussions about threads and their supporting technologies, it is necessary to define some basic terminology.

If you are familiar with UNIX systems, you may find that the term “task” is used differently by this document. On UNIX systems, the term “task” is used at times to refer to a running process.

This document adopts the following terminology:

The term thread is used to refer to a separate path of execution for code.
The term process is used to refer to a running executable, which can encompass multiple threads.
The term task is used to refer to the abstract concept of work that needs to be performed.

Alternatives to Threads

One problem with creating threads yourself is that they add uncertainty to your code. Threads are a relatively low-level and complicated way to support concurrency in your application. If you do not fully understand the implications of your design choices, you could easily encounter synchronization or timing issues, the severity of which can range from subtle behavioral changes to the crashing of your application and the corruption of the user’s data.

Another factor to consider is whether you need threads or concurrency at all. Threads solve the specific problem of how to execute multiple code paths concurrently inside the same process. There may be cases, though, where the amount of work you are doing does not warrant concurrency. Threads introduce a tremendous amount of overhead to your process, both in terms of memory consumption and CPU time. You may discover that this overhead is too great for the intended task, or that other options are easier to implement.

Table 1-1 lists some of the alternatives to threads. This table includes both replacement technologies for threads (such as operation objects and GCD) and alternatives that are geared towards efficiently using the single thread you already have.

**Table 1-1** Alternative technologies to threads
Technology	Description
Operation objects	Introduced in OS X v10.5, an operation object is a wrapper for a task that would normally be executed on a secondary thread. This wrapper hides the thread management aspects of performing the task, leaving you free to focus on the task itself. You typically use these objects in conjunction with an operation queue object,

Mac Multi Threaded Apps