Parallel programming models
Parallel programming models exist as an abstraction of hardware and memory architectures. In fact, these models are not specific and do not refer to particular types of machines or memory architectures. They can be implemented (at least theoretically) on any kind of machines. Compared to the previous subdivisions, these programming models are made at a higher level and represent the way in which the software must be implemented to perform a parallel computation. Each model has its own way of sharing information with other processors in order to access memory and divide the work.
There is no better programming model in absolute terms; the best one to apply will depend very much on the problem that a programmer should address and resolve. The most widely used models for parallel programming are:
- The shared memory model
- The multithread model
- The distributed memory/message passing model
- The data parallel model
In this recipe, we will give you an overview of these models. A more accurate description will be in the next chapters that will introduce you to the appropriate Python module that implements these.
The shared memory model
In this model the tasks share a single shared memory area, where the access (reading and writing data) to shared resources is asynchronous. There are mechanisms that allow the programmer to control the access to the shared memory, for example, locks or semaphores. This model offers the advantage that the programmer does not have to clarify the communication between tasks. An important disadvantage in terms of performance is that it becomes more difficult to understand and manage data locality; keeping data local to the processor that works on it conserves memory accesses, cache refreshes, and bus traffic that occur when multiple processors use the same data.
The multithread model
In this model, a process can have multiple flows of execution, for example, a sequential part is created and subsequently, a series of tasks are created that can be executed parallelly. Usually, this type of model is used on shared memory architectures. So, it will be very important for us to manage the synchronization between threads, as they operate on shared memory, and the programmer must prevent multiple threads from updating the same locations at the same time. The current generation CPUs are multithreaded in software and hardware. Posix threads are the classic example of the implementation of multithreading on software. The Intel Hyper-threading technology implements multithreading on hardware by switching between two threads when one is stalled or waiting on I/O. Parallelism can be achieved from this model even if the data alignment is nonlinear.
The message passing model
The message passing model is usually applied in the case where each processor has its own memory (distributed memory systems). More tasks can reside on the same physical machine or on an arbitrary number of machines. The programmer is responsible for determining the parallelism and data exchange that occurs through the messages. The implementation of this parallel programming model requires the use of (ad hoc) software libraries to be used within the code. Numerous implementations of message passing model were created: some of the examples are available since the 1980s, but only from the mid-90s, was created to standardized model, coming to a de facto standard called MPI (the message passing interface). The MPI model is designed clearly with distributed memory, but being models of parallel programming, multiplatform can also be used with a shared memory machine.
The message passing paradigm model
The data parallel model
In this model, we have more tasks that operate on the same data structure, but each task operates on a different portion of data. In the shared memory architecture, all tasks have access to data through shared memory and distributed memory architectures, where the data structure is divided and resides in the local memory of each task. To implement this model, a programmer must develop a program that specifies the distribution and alignment of data. The current generation GPUs operates high throughout with the data aligned.
The data parallel paradigm model
