There are a few things to keep in mind about Go’s goroutines: They are not threads in the sense of Java’s or C++ threads goroutines are more like greenlets The go runtime multiplexes the goroutines across the system threads the number of system threads is controlled by an environment variable GOMAXPROCS and defaults to 1 … Read more
No it’s not safe even though you may not sometimes observe any troubles. IIRC, the fmt package tries to be on the safe side, so probably intermixing of some sort may occur but no process crash, hopefully. This is an instance of a more universal Go documentation rule: Things are not safe for concurrent access … Read more
BlockingCollection and ConcurrentQueue are there for precisely this reason. I doubt that you’ll find anything better, or simpler to use. The parallel extensions team know their stuff 🙂 Just a quick check on versions though – you’re definitely using .NET 4, not just C# 4? (For example, you could be using Visual Studio 2010 and … Read more
The issue seems to come from your use of rand.Float64(), which uses a shared global object with a Mutex lock on it. Instead, if for each CPU you create a separate rand.New(), pass it through to the interactions(), and use it to create the Float64(), there’s a massive improvement. Update to show the changes to … Read more
[…] what is happening on let data = data.clone()? Arc stands for Atomically Reference Counted. An Arc manages one object (of type T) and serves as a proxy to allow for shared ownership, meaning: one object is owned by multiple names. Wow, that sounds abstract, let’s break it down! Shared Ownership Let’s say you have … Read more
There is nothing wrong with guarding the MySlice = append(MySlice, &OneOfMyStructs) with a sync.Mutex. But of course you can have a result channel with buffer size len(params) all goroutines send their answers and once your work is finished you collect from this result channel. If your params has a fixed size: MySlice = make([]*MyStruct, len(params)) … Read more
Do you really mean concurrent or distributed? If you mean concurrent (multi-threaded, multi-core etc.), then I’d say Clojure is the natural solution. Clojure’s STM model is perfectly designed for multi-core concurrency since it is very efficient at storing and managing shared state between threads. If you want to understand more, well worth looking at this … Read more
It’s a pretty simple difference. In a shared memory model, multiple workers all operate on the same data. This opens up a lot of the concurrency issues that are common in parallel programming. Message passing systems make workers communicate through a messaging system. Messages keep everyone seperated, so that workers cannot modify each other’s data. … Read more
My research work includes work on compilers and on spam filtering. I also do a lot of ‘personal productivity’ Unix stuff. Plus I write and use software to administer classes that I teach, which includes grading, testing student code, tracking grades, and myriad other trivia. Multicore affects me not at all except as a research … Read more
In your original code, you only started one MailboxProcessor. Make wait() a function, and call it with each yield. Also you are not waiting for them to spin up or receive the messages, which I think invalidates the timing info; see my code below. That said, I have some success; on my box I can … Read more