"Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously."
A technique used to execute multiple instructions simultaneously by dividing the computation task into sub-tasks and executing them concurrently.
Parallel computing architectures: This topic covers the different types of parallel computing architectures such as SIMD, MIMD, and hybrid architectures. It also covers the similarities and differences among these architectures with regards to their structure and performance.
Parallel algorithms: This topic covers the various parallel algorithms used in solving different problems. It also covers the analysis of the efficiency and performance of these algorithms.
Parallel programming models: This topic covers the various programming models such as message passing, shared memory, and data parallelism. It also covers the advantages and disadvantages of using each programming model.
Parallel programming languages: This topic covers the various high-level programming languages used in parallel computing such as OpenMP, MPI, and CUDA. It also covers the syntax, data structures, and control flow used in these programming languages.
Memory hierarchy and caching: This topic covers the different levels of memory hierarchy used in parallel processing systems such as cache memory, main memory, and disk storage. It also covers caching mechanisms used to enhance the performance of these systems.
Interconnection networks: This topic covers the various types of interconnection networks used in parallel computing such as bus, ring, and mesh. It also covers the performance and efficiency of these networks.
Multiprocessor systems: This topic covers the design and architecture of multiprocessor systems such as multicore processors, clusters, and grids. It also covers the performance and scalability of these systems.
Performance analysis and evaluation: This topic covers the measurement and evaluation of the performance of parallel processing systems. It also covers the techniques used in analyzing the efficiency of these systems.
Fault tolerance and reliability: This topic covers the various techniques used in ensuring the reliability and fault tolerance of parallel processing systems. It also covers the mechanisms used in detecting and correcting errors in these systems.
Load balancing: This topic covers the techniques used in distributing tasks evenly among processors in parallel processing systems. It also covers the performance and efficiency of load balancing techniques.
SIMD (Single Instruction Multiple Data): In SIMD, a single instruction is executed on multiple data elements simultaneously. This is commonly used for multimedia processing, such as image manipulation or video compression.
MIMD (Multiple Instruction Multiple Data): In MIMD, each processing unit has its own set of instructions and data to work with. This is commonly used for scientific simulations or parallel computing.
SPMD (Single Program Multiple Data): In SPMD, each processing unit executes the same program on different sets of data. This is commonly used in high-performance computing applications that require large amounts of data to be processed simultaneously.
SMP (Symmetric Multiprocessing): In SMP, multiple processors share access to the same memory and I/O devices. This allows multiple processors to work on a problem simultaneously and can lead to improved performance.
NUMA (Non-Uniform Memory Access): In NUMA, processors have different amounts of memory that are not directly accessible to other processors. This type of architecture is used in large-scale computing environments.
Distributed processing: In distributed processing, computation is distributed across multiple computers or processors that are connected by a network. This can provide scalability and fault tolerance, but requires coordination and communication between the different nodes.
Grid computing: In grid computing, multiple resources are shared across a network of computers or data centers. This allows for large-scale computing tasks to be executed in parallel, but requires specialized infrastructure and software.
Cloud computing: Cloud computing is a type of distributed processing that provides on-demand access to computing resources, such as storage and processing power, over the internet. This can provide scalability and cost savings, but requires specialized software and security measures.
"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."