![]() You can read more about hard disk performance here: On the other hand, if we are reading a single file at a time, it is more likely that the contents of the file (or large segments of the file) are in the same physical location on the disk and therefore the disk takes less time moving the head. Moving from one location to another in HDDs is slow because the physical head in the disk needs to move. When reading from the HDD in parallel, the disk might need to read data from different locations. This might be problematic if ReadDocumentFromSourceStore is an I/O operation on a device that performs badly on parallel access.įor example, ReadDocumentFromSourceStore might be reading the document from a simple Hard Disk Drive (HDD). This means that it is possible that at a given instance, there would be eight threads invoking ReadDocumentFromSourceStore. In the previous example, we assumed that the system will use eight thread-pool threads to process the documents. Simple parallelism – Some Issues Too much parallelism for some operations To keep the discussion simple, I am going to assume that in a machine with eight cores, Parallel.ForEach will have exactly eight tasks running in parallel. For example, even if a machine has eight cores, the system might decide to run more than eight tasks in parallel if it sees that the currently executing tasks, are blocked by I/O. It automatically manages the degree of parallelism. NET components used by it) is sophisticated. ![]() Note: The behavior of Parallel.ForEach (and the. I will go through these issues and finally introduce the producer consumer pattern and show how it solves some of these issues. Parallel.ForEach will manage all of this.Īlthough this might seem a good idea, there might be some issues with this approach. Once each thread is done with its document, it will grab another document id and process it. Of course, all of this depends on the environment!įor example, the higher the number of available cores, the more the number of documents that can be processed in parallel.Īssuming that currently eight threads are processing, we can imagine each of these threads taking one document id, calling ReadDocumentFromSourceStore, TranslateDocument, and then SaveDocumentToDestinationStore. Parallel.ForEach will parallelize the invocation of the Process method over the items in the documentIds array. This is called Data Parallelism because we apply the same operations on each data item and in this particular case, on each document id. In C#, we can easily modify this code to use multiple cores using the Parallel class like this: The code right now will run under a single thread. database), translates it, and then saves it to the destination store. This code gets the ids of documents that need to be processed, obtains each document from the source store (e.g. Void SaveDocumentToDestinationStore(Document document) =>. ĭocument TranslateDocument(Document document, Language language) =>. SaveDocumentToDestinationStore(translatedDocument) ĭocument ReadDocumentFromSourceStore(string identifier) =>. Var translatedDocument = TranslateDocument(document, Language.English) Var document = ReadDocumentFromSourceStore(documentId) String documentIds = GetDocumentIdsToProcess() Subscribe to the DotNetCurry (DNC) Magazine for FREE and download all previous, current and upcoming editions.Ĭonsider the following code from an application that processes documents: NET Core, MVC, Azure, Angular, React, and more. It makes sense to write software that uses multiple cores to enhance performance.Īre you keeping up with new developer technologies? Advance your IT career with our Free Developer magazines covering C#, Patterns. A typical PC these days has four to eight cores, and servers have a lot more than that. As a result of machines having multiple processing cores, parallel programming is becoming more important these days.
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