The Java™ programming language made starting a new thread easier than ever before. But freeing your concurrent programs of obscure bugs is a different matter, and Java's programming model might not be the best available. A language called Erlang is getting some good press now in the areas of concurrency, distributed systems, and soft real-time systems.
Candygram is a Python implementation of Erlang concurrency primitives. Erlang is widely respected for its elegant built-in facilities for concurrent programming. This package attempts to emulate those facilities as closely as possible in Python. With Candygram, developers can send and receive messages between threads using semantics nearly identical to those in the Erlang language.
PARLEY is an API for writing Python programs that implement the Actor model of distributed systems, in which lightweight concurrent processes communicate through asynchronous message-passing.
This package is the backport of java.util.concurrent API, introduced in Java 5.0 and further refined in Java 6.0, to older Java platforms. The backport is based on public-domain sources from the JSR 166 CVS repository, the dl.util.concurrent package, and
A collection of Concurrent and Highly Scalable Utilities. These are intended as direct replacements for the java.util.* or java.util.concurrent.* collections but with better performance when many CPUs are using the collection concurrently.
In an earlier post I mentioned that one goal of the new introductory curriculum at Carnegie Mellon is to teach parallelism as the general case of computing, rather than an esoteric, specialized subject for advanced students. Many people are incredulous when I tell them this, because it immediately conjures in their mind the myriad complexities…
A full stack, reactive architecture for general purpose programming. Algebraic and monadically composable primitives for concurrency, parallelism, event handling, transactions, multithreading, Web, and distributed computing with complete de-inversion of control (No callbacks, no blocking, pure state)
I participated in the design and development of a couple of concurrency libraries for shared-memory multiprocessors long before such machines were popular. So when I started using java.util.concurrent I was already somewhat comfortable with the concepts.
This site will show how to write the concurrency section of A Tour of Go in Haskell. A Tour of Go is a famous tutorial of Go. Haskell has concurrency features similar to Go: lightweight thread, channel, etc.. So it should be interesting to compare equivalent concurrent programs in Haskell and Go.
This book aims to explain green threads by using a small example where we implement a simple but working program where we use our own green threads to execute code.
JPPF enables applications with large processing power requirements to be run on any number of computers, in order to dramatically reduce their processing time. This is done by splitting an application into smaller parts that can be executed simultaneously on different machines.
with Philippa Gardner, Term 1, 2007/2008. Recommended Textbooks R. Milner. Communicating and Mobile Systems: the pi-Calculus. Cambridge University Press, various editions. (Introductory) D. Sangiorgi and D. Walker. The pi-Calculus: a Theory of Mobile Processes. Cambridge University Press, 2001. Online References and Tutorials A Calculus for Mobile Processes (Parts I/II) (by Robin Milner, Joachim Parrow, and David Walker). Also available from this site: [ Part I] [ Part II] The Polyadic Pi-Calculus: A Tutorial (by Robin Milner). Also available from this site: [(Postscript)] An Introduction to the Pi-calculus (by Joachim Parrow) A Brief Introduction to Applied Pi (by Peter Sewell) Asynchronous process calculi: the first-order and higher-order paradigms (Tutorial) (by Davide Sangiorgi)
The Little Book of Semaphores is a free (in both senses of the word) textbook that introduces the principles of synchronization for concurrent programming. In most computer science curricula, synchronization is a module in an Operating Systems class. OS textbooks present a standard set of problems with a standard set of solutions, but most students don't get a good understanding of the material or the ability to solve similar problems. The approach of this book is to identify patterns that are useful for a variety of synchronization problems and then show how they can be assembled into solutions. After each problem, the book offers a hint before showing a solution, giving students a better chance of discovering solutions on their own. The book covers the classical problems, including "Readers-writers," "Producer-consumer", and "Dining Philosophers." In addition, it collects a number of not-so-classical problems
Kamaelia - Concurrency made useful, fun In Kamaelia you build systems from simple components that talk to each other. This speeds development, massively aids maintenance and also means you build naturally concurrent software. It's intended to be accessible by any developer, including novices. What sort of systems? Network servers, clients, desktop applications, pygame based games, transcode systems and pipelines, digital TV systems, spam eradicators, teaching tools, and a fair amount more :)
The C++ standardization committee is hard at work standardizing threads for the next version of C++. Some members recently met to discuss the issues, and The C++ Source was there. Read on to learn what the world’s leading experts on concurrency are plan
Parallel4 is a easy-to-use multi-threading API for Java an other JVM based languages like Groovy. It offers parallel versions of the "for" and "foreach" loops to leverage the full power of todays multi-core CPUs.
Parallel4's goals are:
* Simple API: Hide as many details of multi-threaded programming as possible from the API. Although it does not offer transparent/implicit multi-threading in a strict sense, it tries to come as close to this as a non-functional programming language allows.
* Familiar API: Offer well known constructs, e.g. the "for" loop and add parallelism to it.
* Easy adaptation of parallel programming: Based on a familiar API, it is easy to introduce multi-threading. Often, two additional lines of code are enough without imposing any structural changes.
* Adaptive: Let the "framework" make reasonable defaults to adapt to the execution environment, e.g. use as many threads as CPU cores are available.
* Performance: A low overhead makes it easy to decide whether to use parallel processing or not.
* Reliable: Multi-threading gets tricky when things go wrong unexpectedly. A well defined exception handling makes this a bit easier.
Akka is the platform for the next generation event-driven, scalable and fault-tolerant architectures on the JVM
We believe that writing correct concurrent, fault-tolerant and scalable applications is too hard. Most of the time it's because we are using the wrong tools and the wrong level of abstraction.
Akka is here to change that.
Using the Actor Model together with Software Transactional Memory we raise the abstraction level and provides a better platform to build correct concurrent and scalable applications.
For fault-tolerance we adopt the "Let it crash" / "Embrace failure" model which have been used with great success in the telecom industry to build applications that self-heals, systems that never stop.
Actors also provides the abstraction for transparent distribution and the basis for truly scalable and fault-tolerant applications.
Akka is Open Source and available under the Apache 2 License.
MINA is a simple yet full-featured network application framework which provides:
* Unified API for various transport types:
o TCP/IP & UDP/IP via Java NIO
o Serial communication (RS232) via RXTX
o In-VM pipe communication
o You can implement your own!
* Filter interface as an extension point; similar to Servlet filters
* Low-level and high-level API:
o Low-level: uses ByteBuffers
o High-level: uses user-defined message objects and codecs
* Highly customizable thread model:
o Single thread
o One thread pool
o More than one thread pools (i.e. SEDA)
* Out-of-the-box SSL · TLS · StartTLS support using Java 5 SSLEngine
* Overload shielding & traffic throttling
* Unit testability using mock objects
* JMX managability
* Stream-based I/O support via StreamIoHandler
* Integration with well known containers such as PicoContainer and Spring
* Smooth migration from Netty, an ancestor of Apache MINA.
RP has extremely good performance and scalability properties. Many uses of RP in the Linux Kernel have resulted in several orders of magnitude performance improvement compared to locking and transactional memory. Is it easy to program with? RP is not difficult to program with. Allowing each execution sequence to proceed using its own view of memory, by default, simplifies concurrent programming because it prevents memory from changing spontaneously. Threads are guaranteed to observe coherent memory, i.e., memory will contain values that were actually written at some time in the past. Read paths also exhibit deterministic performance characteristics, since they can not block or retry. This feature simplifies programming of time-sensitive systems. Nevertheless, RP is a new programming paradigm with a new interface and there are several ways to misuse it. Read Copy Update (RCU), an early example of RP, has seen extensive use in the Linux Kernel at over 2000 uses
One of those things I have to do fairly often in multithreaded programming is send off a whole bunch of threads to do their thing while I do something else on the main thread until they’re done. For example, imagine you’re downloading a bunch of images from the web, you don’t want to call httpGet one image right after another, because network resources are slow and processing them takes up almost no CPU time. But on the other hand, forkIO doesn’t return anything, so a thread thunk will have to put its contents somewhere you can access them later. Thus, my short, simple solution, far too small to bother putting up on Hackage: module Control.Concurrent.Future where import Control.Concurrent future :: IO a -> IO (MVar a) future thunk = do ref <- newEmptyMVar forkIO $ thunk >>= putMVar ref return ref forceAll :: [MVar a] -> IO [a] forceAll = mapM takeMVar
We have designed, implemented, and evaluated AtomCaml, an extension to Objective Caml that provides a synchronization primitive for atomic (transactional) execution of code. A first-class primitive function of type (unit->'a)->'a evaluates its argument (which may call other functions, even external C functions) as though no other thread has interleaved execution. Our design ensures fair scheduling and obstruction-freedom. Our implementation extends the Objective Caml bytecode compiler and run-time system to support atomicity. A logging-and-rollback approach lets us undo uncompleted atomic blocks upon thread pre-emption, and retry them when the thread is rescheduled. The mostly functional nature of the Caml language and the Objective Caml implementation's commitment to a uniprocessor execution model (i.e., threads are interleaved, not executed simultaneously) allow particularly efficient logging.
I didn't see anyone post them yet, so here are the slides from Tim Sweeney's POPL talk entitled "The Next Mainstream Programming Languages: A Game Developer's Perspective". I know Tim and I aren't the only game developers who follow LtU, and I figure even non-game developers might find them quite interesting!
CUDA lets you work with familiar programming concepts while developing software that can run on a GP This is the first of a series of articles to introduce you to the power of CUDA -- through working code -- and to the thought process to help you map applications onto multi-threaded hardware (such as GPUs) to get big performance increases. Of course, not all problems can be mapped efficiently onto multi-threaded hardware, so part of my thought process will be to distinguish what will and what won't work, plus provide a common-sense idea of what might work "well-enough". "CUDA programming" and "GPGPU programming" are not the same (although CUDA runs on GPUs). CUDA permits working with familiar programming concepts while developing software that can run on a GPU. It also avoids the performance overhead of graphics layer APIs by compiling your software directly to the hardware (GPU assembly language, for instance), thereby providing great performance.
JavaScript is single threaded language but multi threading can be achieved in JavaScript using HTML5 Web Workers API. This will enable JavaScript code to run in background AKA parallel programming.
We've discussed differences between parallel, computational systems and concurrent, event handling systems. Areas of differences include:
Determinism: desirable vs impossible
Sign of parallel safety: same results vs correct results
Parallelism bugs: easy to pinpoint vs impossible to define
Queues: implementation detail vs part of the interface
Preemption: nearly useless vs nearly essential
S. Abramsky. Electronic Notes in Theoretical Computer Science, (2006)Proceedings of the Workshop "Essays on Algebraic Process Calculi" (APC 25)Proceedings of the Workshop "Essays on Algebraic Process Calculi" (APC 25).
D. Aumayr, S. Marr, E. Gonzalez Boix, und H. Mössenböck. Proceedings of the 16th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes, Seite 157--171. ACM, (Oktober 2019)
J. Bezivin. OOPSLA '87: Conference proceedings on Object-oriented programming systems, languages and applications, Seite 394--405. New York, NY, USA, ACM, (1987)