Parallelism (CloudMonk.io)
Parallelism
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Parallelism in computing refers to the technique of conducting multiple operations simultaneously. This approach leverages the fact that many computational tasks can be divided into smaller, independent tasks that can be executed at the same time across multiple processors or multiple cores within a computer. Parallelism aims to significantly reduce the time required to complete computational tasks and enhance computational efficiency. There are two primary types of parallelism: data parallelism, where the same operation is performed on different pieces of distributed data simultaneously, and task parallelism, where different tasks are executed in parallel on the same or different datasets. Parallel computing architectures, such as multicore processors, graphics processing units (GPUs), and distributed computing systems, are designed to maximize the benefits of parallelism. These architectures are crucial for high-performance computing, real-time processing, and the efficient handling of large-scale data analysis and scientific computing tasks.
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Concurrency: Concurrency Programming Best Practices, Concurrent Programming Fundamentals, Parallel Programming Fundamentals, Asynchronous I/O, Asynchronous programming (Async programming, Asynchronous flow control, Async / await), Asymmetric Transfer, Akka, Atomics, Busy waiting, Channels, Concurrent, Concurrent system design, Concurrency control (Concurrency control algorithms, Concurrency control in databases, Atomicity (programming), Distributed concurrency control, Data synchronization), Concurrency pattern, Concurrent computing, Concurrency primitives, Concurrency problems, Concurrent programming, Concurrent algorithms, Concurrent programming languages, Concurrent programming libraries, Java Continuations, Coroutines, Critical section, Deadlocks, Decomposition, Dining philosophers problem, Event (synchronization primitive), Exclusive or, Execution model (Parallel execution model), Fibers, Futures, Inter-process communication, Linearizability, Lock (computer science), Message passing, Monitor (synchronization), Computer multitasking (Context switch, Pre-emptive multitasking - Preemption (computing), Cooperative multitasking - Non-preemptive multitasking), Multi-threaded programming, Multi-core programming, Multi-threaded, Mutual exclusion, Mutually exclusive events, Mutex, Non-blocking algorithm (Lock-free), Parallel programming, Parallel computing, Process (computing), Process state, Producer-consumer problem (Bounded-buffer problem), Project Loom, Promises, Race conditions, Read-copy update (RCU), Readers–writer lock, Readers–writers problem, Recursive locks, Reducers, Reentrant mutex, Scheduling (computing), Semaphore (programming), Seqlock (Sequence lock), Serializability, Shared resource, Sleeping barber problem, Spinlock, Synchronization (computer science), System resource, Thread (computing), Tuple space, Volatile (computer programming), Yield (multithreading) , Degree of parallelism, Data-Oriented Programming (DOP), Functional and Concurrent Programming, Concurrency bibliography, Manning Concurrency Async Parallel Programming Series, Concurrency glossary, Awesome Concurrency, Concurrency topics, Functional programming. (navbar_concurrency - see also navbar_async, navbar_python_concurrency, navbar_golang_concurrency, navbar_java_concurrency)
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