Tue Feb 15 10:37:05 AEDT 2022
How to write safer concurrency code - http://www.artima.com/forums/flat.jsp?forum=276&thread=178345
reentrant and thread safe functions - http://kevinrodrigues.com[..]/31/reentrant-and-thread-safe-functions/
Libraries / toolkits for multicore process - http://www.ddj.com[..]intableArticle.jhtml?articleID=212900103
Introduction - http://www.ddj.com[..]QQSNDLRSKHSCJUNN2JVN?articleID=212903586 http://www.ddj.com[..]CQSNDLRSKHSCJUNN2JVN?articleID=213001517
Collections of links - http://dobbscodetalk.com[..]rallel-Or-Get-Left-Behind.html&Itemid=29
Briefing of difference modeling of threading system - http://www.ddj.com[..]intableArticle.jhtml?articleID=215900465 http://software.intel.com[..]inners-guide-to-multithreaded-libraries/
Saving the Failwhale: The Art of Concurrency (Page last updated December 2012, Added 2012-12-26, Author Dhanji R. Prasanna, Publisher informit). Tips:
1) Contention is unavoidable - some resources are just slower, and you must wait for them. The secrets to good concurrency are 1) ensuring that these slower resources are rarely used, and 2) during such waiting periods, giving the faster tiers other work to do so that they continue to be utilized well.
2) Overuse of synchronization constructs such as locks and mutexes leads to systems that perform poorly under load.
3) ConcurrentHashMap is an efficient thread-safe map while HashMap is not thread-safe.
4) ConcurrentHashMap doesn't do away with locks, it still uses them but it uses more than the single global lock, so that threads gain some measure of concurrency. It uses separate locks for partitions, so that multiple threads writing to the map are likely to access different partitions, using separate locks and therefore process their data simultaneously. This technique is known as lock-striping. Efficient striping uses a number of locks proportional to the number of CPU cores in a system.
5) The asynchronous processing model smooths resource spikes by adding requests to a queue which is serviced by a pool of workers - spikes in requests make the queue grow rather than overloading the workers. (The ExecutorService is essentially a thread pool accompanied by a task queue.)
Discussion of using difference model for concurrency - http://highscalability.com[..]cks-application-architecture-pros-a.html
Concurrency vs Parallelism - http://concurrencyfreaks.blogspot.hk[..]/2013/12/concurrency-vs-parallelism.html
Compare between Actors, CSP, Disruptor and raw Threads - http://java-is-the-new-c.blogspot.com.au[..]omparision-of-different-concurrency.html
Few coding tips that should be useful for most languages - http://www.javacodegeeks.com[..]erformance-scalability-and-liveness.html http://www.javacodegeeks.com[..]2015/09/java-concurrency-essentials.html
Do one thing, do it well
No shared operational state
Bound your queues
Name custom thread pools and register an UncaughtExceptionHandler
Prefer immutable data objects over mutable state
On Parallelism and Concurrency - https://inside.java/2021/11/30/on-parallelism-and-concurrency/ https://towardsdatascience.com[..]lism-what-is-the-difference-bdf01069b081
Solving Common Concurrency Problems - https://blog.professorbeekums.com[..]s.com/2021/solving-concurrency-problems/
(google search) (amazon search)