MIMD vs SPMD

In this lab, we will explore the concepts of MIMD (Multiple Instruction, Multiple Data) and SPMD (Single Program, Multiple Data) parallel computing models. These models are essential in the world of parallel and distributed computing, helping us harness the power of multiple processors or cores to perform tasks efficiently. In this lab we will:

1. Create C++ programs to demonstrate MIMD and SPMD architecture.

2. Compare their speed and performances.

MIMD (Multiple Instruction, Multiple Data)

MIMD is a parallel computing model where multiple processors execute different instructions on different data simultaneously. Each processor operates independently and can execute its own set of instructions. This model is highly suitable for tasks that involve a high degree of parallelism.

In a MIMD system, processes or threads are launched to perform specific tasks. These processes may have different code segments and can communicate with each other. MIMD is particularly useful when dealing with complex problems that can be divided into smaller, independent units of work. Each processor can execute different instructions and work on different data, and they may synchronize or communicate when necessary.

SPMD (Single Program, Multiple Data)

SPMD is a parallel computing model where multiple processors execute the same program on different data. Unlike MIMD, all processors in an SPMD model execute the same instructions, but they may work on different pieces of data. This model is often used in situations where a single program can be applied to multiple data sets.

In an SPMD system, processes or threads are created to execute the same code but work on different data segments. These processes often operate independently and do not necessarily communicate with each other during execution. SPMD is well-suited for data-parallel problems, where the data segments are independent of each other, and the same operations can be applied to each data segment.

Advantages of MIMD and SPMD

MIMD

1. Highly flexible and versatile for tasks with varying degrees of parallelism.

2. Suitable for complex problems that require different instructions to be executed concurrently.

3. Maximizes resource utilization when many processors are available.

SPMD

1. Ideal for tasks where the same code can be applied to multiple data sets.

2. Simplifies synchronisation, as all processors follow the same set of instructions.

3. Well-suited for data-parallel problems where data independence is high.

Summary

Understanding the differences between MIMD and SPMD models and their applications is crucial for parallel computing tasks. By implementing and comparing both models through C++ programs, we gain valuable insights into how to choose the right parallel computing approach for specific tasks and optimize performance.