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Yefim Alekseev
Yefim Alekseev

Parallel Programming With Microsoft .NET: Desig...

The Patterns for Parallel Programming: Understanding and Applying Parallel Patterns with the .NET Framework 4 document describes common parallel patterns and best practices for developing parallel components utilizing those patterns.

Parallel Programming with Microsoft .NET: Desig...

The Design Patterns for Decomposition and Coordination on Multicore Architectures book describes patterns for parallel programming that use the parallel programming support introduced in the .NET Framework 4.

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

In 2003, a team of researchers led by Ian Buck unveiled Brook, the first widely adopted programming model to extend C with data-parallel constructs. Buck later joined NVIDIA and led the launch of CUDA in 2006, the first commercial solution for general purpose computing on GPUs.

Traditionally, organizations have been using a data warehouse for their analytical needs. As the business requirements evolved and data scale increased, they started adopting a modern data warehouse architecture which can process massive amounts of data in a relational format and in parallel across multiple compute nodes. At the same time, they started collecting and managing their non-relational big data that was in semi-structured or unstructured format with a data lake.

Azure Synapse Analytics has Spark pools compute engine which can be leveraged to prepare and transform data from one stage to another stage of the data lakehouse. Spark pool is powered by Apache Spark which is a parallel processing framework that supports in-memory processing to boost the performance of big data analytic applications. Apache Spark in Azure Synapse Analytics is one of Microsoft's implementations of Apache Spark in the cloud. Azure Synapse makes it easy to create and configure an Apache Spark pool in Azure. Spark pools in Azure Synapse are compatible with Azure Storage and Azure Data Lake Generation 2 Storage where we store data for the data lakehouse.

2. Declarative programming paradigm: It is divided as Logic, Functional, Database. In computer science the declarative programming is a style of building programs that expresses logic of computation without talking about its control flow. It often considers programs as theories of some logic.It may simplify writing parallel programs. The focus is on what needs to be done rather how it should be done basically emphasize on what code is actually doing. It just declares the result we want rather how it has be produced. This is the only difference between imperative (how to do) and declarative (what to do) programming paradigms. Getting into deeper we would see logic, functional and database.

Asynchronous programming is a form of parallel programming that allows a unit of work to run separately from the primary application thread. When the work is complete, it notifies the main thread (as well as whether the work was completed or failed). There are numerous benefits to using it, such as improved application performance and enhanced responsiveness.

The .NET Framework provides a few avenues to get on the ramp to asynchronous programming. Some of your implementation choices from the most basic to complex include using background workers, invoking a method asynchronously from a delegate, or implementing the IAsynchResult interface. All of these options allow you to multi-thread your application without ever having to manage your own threads. The .NET Framework asynchronous APIs handle this drudgery for you.

Asynchronous programming is actually easier than you may think. Check out this post to learn how to return AJAX response from an asynchronous JavaScript call. Or, read this post for more information on how Microsoft has made asynchronous programming simple with the implementation of async await in C# and how the latest versions of ASP.NET are utilizing it to boost performance.

In addition to application model frameworks, .NET offers you support for most of the common programming tasks: from file management to network communication, from security to database access. For example, on the networking side, it supports socket programming, HTTP communication, and gRPC. This allows you to create microservices with the protocol that better fits your needs.

Microsoft launched the .NET project in 2002. Since the beginning, the .NET goal was to create a universal platform for programming with any language. Of course, as the first step, Windows was the main .NET target.

The .NET Framework was the initial flavor of .NET. It provides the developer with a set of APIs for the most common programming needs and interacts with the underlying operating system. It runs only on Windows, and its lifecycle is coming to an the end right now, after the release of .NET 5.

Figure 1 shows the Hybridizer compilation pipeline. Using parallelization patterns such as Parallel.For, or by distributing parallel work explicitly as you would in CUDA, you can benefit from the compute horsepower of accelerators without learning all the details of their internal architecture. Here is a simple example using Parallel.For with a lambda.

Functional programming promotes composition, which is the concept the pipeline pattern is based on. In the following listing, the pipeline embraces this tenet by composing each step into a single function and then running and distributing the work in parallel, fully leveraging the available resources. In an abstract way, each function acts as the continuation of the previous one, behaving as a CPS model. The code listing implementing the pipeline is in F#, then consumed in C#. You can find the full implementation in both programming languages in the downloadable source code.

Each item pushed into the pipeline is added to the collection, which will be taken and processed in parallel in the future. The Then function is composing the function nextFunction, passed as argument, with the function func passed into the pipeline constructor. When the pipeline starts the process, it applies the final composed function to each input value.

The parallelism in the pipeline is achieved in the Execute function, which is spawning one task for each BlockingCollection instantiated. This guarantees a buffer for running the thread. The tasks are created with the LongRunning option to schedule a dedicated thread. The BlockingCollection concurrent collection allows thread-safe access to the items stored using the static methods TakeFromAny and AddToAny, which internally distribute the items and balance the workload among the running threads. This collation is used to manage the connection between the input and output of the pipeline, which behave as producer/consumer threads.

Learn how to effectively apply asynchronous principles in any type of .NET application using async and await together with the task parallel library. This course will give you the insight to build fast, powerful, and easy to maintain applications.

So, is there any difference between these two notions? Yes, there is. You can think of parallel programming as a subset of asynchronous programming: every parallel execution is asynchronous, but not every asynchronous execution is parallel.

Returning to programming, the difference between synchronous and asynchronous execution is that in case of synchronous execution, the thread leaves its current task and starts working on a new task immediately. On the other hand, with asynchronous execution the thread continues working on its current task.

We use asynchronous programming when we have a blocking operation in the program and we want to continue with the execution of the program without waiting for the result. This allows us to implement tasks that can run at the same time.

In C#, asynchronous programming is achieved through the use of the async and await keywords. You can learn more about this in our Asynchronous Programming with Async and Await in ASP.NET Core article.

Runtime is a piece of code that implements portions of a programming language's execution model. In doing this, it allows the program to interact with the computing resources it needs to work. Runtimes are often integral parts of the programming language and don't need to be installed separately.

The above is source code. BASIC is an interpretive programming language, which means its instructions can be run without first compiling the code into a runtime version. To run the program and print the word Hello, the coder would insert another BASIC command:

A runtime system is software that comes with programming languages as part of the execution model. It creates the layer described earlier that sits over the OS that contains other programs that help run the main program. 041b061a72


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