Async programming vs Parallel programming
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Async programming compared to Parallel programming
- Snippet from Wikipedia: Parallel computing
Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. 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. Parallelism has long been employed in high-performance computing, but has gained broader interest due to the physical constraints preventing frequency scaling. 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, mainly in the form of multi-core processors.
In computer science, parallelism and concurrency are two different things: a parallel program uses multiple CPU cores, each core performing a task independently. On the other hand, concurrency enables a program to deal with multiple tasks even on a single CPU core; the core switches between tasks (i.e. threads) without necessarily completing each one. A program can have both, neither or a combination of parallelism and concurrency characteristics.
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. Specialized parallel computer architectures are sometimes used alongside traditional processors, for accelerating specific tasks.
In some cases parallelism is transparent to the programmer, such as in bit-level or instruction-level parallelism, but 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, of which race conditions are the most common. 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 utilised.
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), 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)
Async Programming: Async Programming Best Practices, Asynchronous Programming Fundamentals, Promises and Futures, Async C, Async C++, Async C, Async Clojure, Async Dart, Async Golang, Async Haskell, Async Java (RxJava), Async JavaScript, Async Kotlin, Async PowerShell, Async Python, Async Ruby, Async Scala, Async TypeScript, Async Programming Bibliography, Manning Concurrency Async Parallel Programming Series. (navbar_async - see also navbar_concurrency, navbar_python_concurrency, navbar_golang_concurrency, navbar_java_concurrency)
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