Parallel Programming Platforms: Implicit Parallelism, Dichotomy of Parallel Computing Platforms, Physical Organization of Parallel Platforms, Communication Costs in Parallel Machines
Parallel programming platforms are computing systems that allow for the execution of multiple tasks or processes simultaneously. There are several different types of parallelism, including explicit and implicit parallelism. In this answer, we will focus on implicit parallelism, the dichotomy of parallel computing platforms, physical organization of parallel platforms, and communication costs in parallel machines.How to Build a Flask CRUD Web App: A Comprehensive Guide with Code Examples and Best Practices | NileshBlog(Opens in a new browser tab)
Implicit parallelism refers to the ability of a computing system to automatically execute tasks or processes in parallel, without requiring the programmer to explicitly specify the parallelism. This is achieved through various techniques such as dynamic task scheduling and data parallelism. Implicit parallelism is commonly used in high-performance computing applications such as scientific simulations and machine learning.Introduction to Parallel Computing: Motivating Parallelism ?(Opens in a new browser tab)
Dichotomy of Parallel Computing Platforms:
Parallel computing platforms can be broadly categorized into two types: shared memory and distributed memory. Shared memory systems allow multiple processors to access a single shared memory, while distributed memory systems have separate memory for each processor. Shared memory systems are typically easier to program, but have limitations on scalability, while distributed memory systems can scale to much larger numbers of processors, but require more complex programming.what are characteristics of Task and Interaction ?(Opens in a new browser tab)
Physical Organization of Parallel Platforms:
Parallel computing platforms can also be organized in various physical configurations, such as clusters, grids, and clouds. Clusters are typically groups of computers connected via a high-speed network, while grids and clouds are larger distributed systems that may span multiple geographic locations. The physical organization of a parallel platform can affect performance and scalability, as well as the communication costs involved in executing parallel programs.
Communication Costs in Parallel Machines:
In parallel computing systems, communication costs refer to the overhead involved in transferring data between processors. This can include the time and bandwidth required to transmit data over a network or between memory modules. Minimizing communication costs is critical to achieving good performance in parallel programs, as excessive communication overhead can limit scalability and reduce overall efficiency. Techniques such as message passing, shared memory, and data locality can be used to reduce communication costs in parallel machines.