"Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously."
The utilization of multiple processors or cores to perform tasks simultaneously in order to increase computational speed.
Parallel programming languages: This involves understanding the different programming languages like MPI, OpenMP, CUDA, etc., used for writing parallel programs.
Parallel architectures: Understanding the different parallel computing system architectures like shared-memory systems, distributed-memory systems, SIMD architectures, and MIMD architectures.
Parallel algorithms: This involves understanding the different types of parallel algorithms, such as divide-and-conquer, dynamic programming, and graph algorithms.
Parallel performance analysis: Knowing how to analyze the performance of parallel programs using profiling and tracing tools.
Load balancing: A technique used to distribute the workload uniformly across all processing nodes in a parallel computing system.
Parallel data structures: This involves understanding the different parallel data structures used to store and manipulate data in parallel computing systems.
Parallel processing models: Types of parallel processing models like message passing, data parallelism, task parallelism, and server-based processing.
Parallel scheduling: A technique used to schedule tasks in a parallel computing system to achieve maximum performance.
Parallel debugging: Methods and tools used for debugging parallel programs.
GPU programming: Understanding how to write parallel programs using GPUs to achieve high performance.
Heterogeneous computing: Understanding how to leverage different types of processors/accelerators (e.g., CPUs, GPUs) in a single parallel computing system.
Big data processing: Processing large datasets using parallel computing techniques.
Cloud computing: Understanding how to take advantage of cloud resources for parallel computing.
Parallel file systems: Understanding file systems designed for parallel computing systems.
Parallel I/O: Techniques used for input/output operations in parallel computing systems.
Task Parallelism: In task parallelism, multiple tasks are executed simultaneously on different processors to achieve higher performance.
Data Parallelism: Data parallelism is a type of parallel computing where large data sets are divided into smaller portions that are processed simultaneously on different processors.
Message Passing Interface (MPI): Message Passing Interface is a communication protocol used for parallel computing. It allows multiple processors to communicate with each other by exchanging messages.
Shared Memory Parallelism: In shared memory parallelism, multiple processors share a common memory space, and different threads access that memory simultaneously to execute parallel tasks.
Distributed Memory Parallelism: In distributed memory parallelism, processors operate independently and communicate through a message-passing system.
Cluster Computing: A group of interconnected computers or servers dedicated to performing a specific task or set of tasks is called cluster computing.
Grid Computing: Grid computing is a form of distributed computing where different resources are shared among various individual computers.
Cloud Computing: In cloud computing, a network of remote servers can process, store, and manage data over the internet.
GPU Acceleration: Graphics Processing Units (GPUs) are specialized processors that can handle complex graphics and parallel tasks.
SIMD and MIMD: Single Instruction Multiple Data (SIMD) and Multiple Instruction Multiple Data (MIMD) are two types of parallel processing that allow multiple processors to execute tasks simultaneously.
Vector Processing: Vector processing is a high-speed method of parallel processing that uses parallelism in CPU instruction sets.
Hybrid Parallelism: Hybrid parallelism combines two or more of the above parallel computing techniques to achieve higher performance.
"Large problems can often be divided into smaller ones, which can then be solved at the same time."
"There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism."
"Parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors, due to the physical constraints preventing frequency scaling."
"Parallel computing is closely related to concurrent computing as they are frequently used together, and often conflated, though the two are distinct."
"It is possible to have parallelism without concurrency, and concurrency without parallelism (such as multitasking by time-sharing on a single-core CPU)."
"In parallel computing, a computational task is typically broken down into several, often many, very similar sub-tasks that can be processed independently and whose results are combined afterwards, upon completion."
"Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids use multiple computers to work on the same task."
"Explicitly parallel algorithms, particularly those that use concurrency, are more difficult to write than sequential ones, because concurrency introduces several new classes of potential software bugs."
"Communication and synchronization between the different subtasks are typically some of the greatest obstacles to getting optimal parallel program performance."
"A theoretical upper bound on the speed-up of a single program as a result of parallelization is given by Amdahl's law, which states that it is limited by the fraction of time for which the parallelization can be utilized."
"As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture."
"Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks."
"Concurrency introduces several new classes of potential software bugs, of which race conditions are the most common."
"In some cases parallelism is transparent to the programmer, such as in bit-level or instruction-level parallelism."
"In contrast to parallel computing, in concurrent computing, the various processes often do not address related tasks."
"Typical in distributed computing, the separate tasks may have a varied nature and often require some inter-process communication during execution."
"As power consumption by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture."
"Communication and synchronization between the different subtasks are typically some of the greatest obstacles to getting optimal parallel program performance."
"Explicitly parallel algorithms, particularly those that use concurrency, are more difficult to write than sequential ones, because concurrency introduces several new classes of potential software bugs."