#Sequential program faster than parallel how to#
#Sequential program faster than parallel code#
#Sequential program faster than parallel free#

We must lock the additon because it isn't add all these together to get the final sum. We use threadLocalState to compute the total for each partition, and then we have to and then add up all the values using a separate thread for each partition. (the Partitioner will use knowledge about the number of processor cores to optimize this) To do this, we use class Partitioner to split the input array into just a few partitions, For each double in data we are going to calculate Math.Sqrt(Math.Sin(x)) and

#Sequential program faster than parallel how to#

Here's an example showing how to do it: using System In fact, Microsoft provide a Partitioner class to help with exactly this approach. Yes, partitioning the input array is a good approach. Remark: 'rest' variable to do the last items is currently not used in this example.

#Sequential program faster than parallel code#

The code I created for another parallel approach: (used in my own static class) public static void ForEach(IEnumerable list, Action body, int listDevide)įoreach(var item in list.Skip(x * listPart).Take(listPart)) So I had another thought and that is to divide the list in 4 (or more) equal peaces and create a thread to loop through the items to get it faster.īefore creating my own class, is this a good approach? Or any other thoughts of how to speed things up? Or do you know a better way of handling this. The first thought was to use Parallel, but due to the limited time duration for each item, Parallel lasts longer than a sequential Foreach, this is due to ' Why was the parallel version slower than the sequential version in this example?', which explains that the creation of each task will cost time. The CPU utilization when using Sequential is around 10%, so there's a lot of resources not used.

#Sequential program faster than parallel free#

Lightbend also offers an akka streams course (academy is free at this time) that delves into this topic, if you or your organization are interested in a more formal training option.I got a large list to loop through (1.500.000 items), with each item I have to do a very small check. > 1ms) then you will begin to see the benefits of parallelization because the operation cost will outweigh the overhead.Ĭolin Breck has a good blog post which describes the pros and cons of parallelism in akka streams.

If your operation took a non-trivial amount of time (i.e. In order to propagate I/O between the two actors we incur extra overhead and that’s what’s skewing the results of your benchmark.

Concretely, everything before the async runs in one actor, and everything after runs in another. When an async boundary is introduced we create an “island” for that operation and everything before it. Output from one operator is fed directly as input to the next. By default akka streams operations are “fused” together and run by a single actor. When you use the async operator in Akka Streams it incurs extra overhead when the stream is run. Hi example app highlights the trouble in benchmarking parallelism with trivial (fast) operations.

Here is my code: object Main extends App '))ĭo you have an idea why the sequential code is so much faster than the parallel? Is the workload not enough or did I write the parallel code in a wrong way? My results are always that my sequential code is a lot faster, it needs mostly around 16 sec for the computation, whereas my parallel code needs somewhere between 70 and 100 sec. I added a warm-up phase, so the comparison is fair. I used a huge amount of numbers and some mathematical operations to give the computer enough workload. I used a Balancer and a Merger and my computer has 4 cores. But the sequential processing is faster than the parallel processing and I wonder why? For that purpose, I built a simple stream in a parallel and a sequential way to see the difference. I am currently trying to understand better how parallelism works in Akka Streams.