The concurrent dictionary-Multithreading

convert a ‘foreach’ loop into a parallel foreach loop

Let’s say you have the following foreach loop, which sends an output of results to Grasshopper:

var results new List<bool>(new bool[pts.Count]);
foreach (Point3d pt in pts)
    bool isIntersecting = CheckIntersection(pt, shape);

DA.SetDataList(0, results);

We set up a list to take our results, we cycle through our points, do a calculation on each, and then save the result. When finished, we send the result as an output back to Grasshopper. Simple enough.

The best thing to do is I show you how to parallelise this, and then explain what’s happening. Here’s the parallel version of the above code:

var results = new System.Collections.Concurrent.ConcurrentDictionary<Point3d, bool>(Environment.ProcessorCount, pts.Count);
foreach (Point3d pt in pts) results[pt] = false; //initialise dictionary

Parallel.ForEach(pt, pts=>
    bool isIntersecting = CheckIntersection(pt, shape);
    results[pt] = isIntersecting; //save your output here

DA.SetDataList(0, results.Values); //send back to GH output

Going from ‘foreach’ to ‘Parallel.ForEach’

The Parallel.ForEach method is what is doing the bulk of the work for us in parallelising our foreach loop. Rather than waiting for each loop to finish as in single threaded, the Parallel.ForEach hands out loops to each core to each of them as they become free. It is very simple – we don’t have to worry about dividing tasks between the processors, as it handles all that for us.

Aside from the slightly different notation, the information we provide is the same when we set up the loop. We are reading from collection ‘pts’, and the current item we are reading within ‘pts’ we will call ‘pt’.

The concurrent dictionary

One real problem though with multithreading the same task is that sometimes two or more processes can attempt to overwrite the same piece of data. This creates data errors, which may or may not be silent. This is why we don’t use a List for the multithreading example – List provides no protection from multiple processes attempting to write into itself.

Instead, we use the ConcurrentDictionary. The ConcurrentDictionary is a member of the Concurrent namespace, which provides a range of classes that allow us to hold and process data without having to worry about concurrent access problems. The ConcurrentDictionary works just about the same as regular Dictionaries, but for our purposes we can use it much like a list.

It is a little more complicated to set up, as you need to specify keys for each value in the dictionary, as well as the value itself. (I have used dictionaries once before here.) Rather than having a collection of items in an order that we can look up with the index (the first item is 0, the second item is 1 and so on) in a dictionary we look up items by referring to a specific item. This specific item is called the ‘key’. Here, I am using the points as the key, and then saving their associated value (the results we are trying to calculate) as a bool.

How much faster is parallel computation?

Basic maths will tell you that you will that your code should increase at a rate of how many cores you have. With my 4-core machine, I might expect a 60 second single-threaded loop to complete in about 15 seconds.

However, there are extra overheads in parallel processing. Dividing up the work, waiting for processes to finish towards the end, and working with the concurrent dictionary, all take up processor power that would otherwise be used in computing the problem itself.

In reality, you will still likely get a substantial improvement compared to single-threaded performance. With 4 cores, I am finding I am getting a performance improvement of around 2.5 to 3.5 times. So a task that would normally take 60 seconds is now taking around 20 seconds.