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This article lists concurrent and parallel programming languages, categorizing them by a defining paradigm.Concurrent and parallel programming languages involve multiple timelines.
This type of multithreading is known as block, cooperative or coarse-grained multithreading. The goal of multithreading hardware support is to allow quick switching between a blocked thread and another thread ready to run. Switching from one thread to another means the hardware switches from using one register set to another.
A few interpreted programming languages have implementations (e.g., Ruby MRI for Ruby, CPython for Python) which support threading and concurrency but not parallel execution of threads, due to a global interpreter lock (GIL). The GIL is a mutual exclusion lock held by the interpreter that can prevent the interpreter from simultaneously ...
After the MVP release, WebAssembly added support for multithreading and garbage collection (WasmGC, and web browsers including Safari have added support for it), [56] which allowed more efficient compilation for garbage-collecting programming languages like C# (supported via Blazor), F# (supported via Bolero [57] with help of Blazor) and Python ...
"Practical Threaded Programming with Python: Thread Pools and Queues" by Noah Gift "Optimizing Thread-Pool Strategies for Real-Time CORBA" by Irfan Pyarali, Marina Spivak, Douglas C. Schmidt and Ron Cytron "Deferred cancellation. A behavioral pattern" by Philipp Bachmann "A C++17 Thread Pool for High-Performance Scientific Computing" by Barak ...
Single instruction, multiple threads (SIMT) is an execution model used in parallel computing where single instruction, multiple data (SIMD) is combined with multithreading. It is different from SPMD in that all instructions in all "threads" are executed in lock-step.
This approach is characteristic of functional programming and is also used by the string implementations in Java, C#, and Python. (See Immutable object .) The second class of approaches are synchronization-related, and are used in situations where shared state cannot be avoided:
A concise reference for the programming paradigms listed in this article. Concurrent programming – have language constructs for concurrency, these may involve multi-threading, support for distributed computing, message passing, shared resources (including shared memory), or futures