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This document outlines the schedule and details of an intensive two-day course focused on introducing the Computer-Aided Parallelization Tool (CAP) developed at EPFL, aimed at facilitating the development
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How to fill out Parallelization of Programs under Windows NT

01
Access the 'Control Panel' from the Start menu.
02
Open 'System and Security,' then select 'System.'
03
Click on 'Advanced system settings' on the left panel.
04
In the 'System Properties' window, navigate to the 'Advanced' tab.
05
Click on 'Settings' under the 'Performance' section.
06
In the 'Performance Options' window, go to the 'Advanced' tab.
07
Locate the 'Processor scheduling' section and select 'Background services.'
08
Click 'OK' to confirm and save the changes.
09
Open the application or program you want to run in parallel.
10
Configure the program's settings to enable parallel processing if available.

Who needs Parallelization of Programs under Windows NT?

01
Software developers seeking to enhance application performance.
02
IT professionals managing servers or workstations requiring efficient resource utilization.
03
Organizations running large-scale computations or data processing tasks.
04
Users of applications that support multi-threading or parallel execution.
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To implement a parallel algorithm you need to construct a parallel program. The environment within which parallel programs are constructed is called the parallel programming environment.
Available in client and server versions, Windows NT included built-in networking and preemptive multitasking. NT was introduced in 1993 as Version 3.1 with the same user interface as Windows 3.1.
Here are some examples: Smartphones. Many smartphones rely on parallel processing to accomplish their tasks faster and more efficiently. Blockchains. Laptop computers. Internet of Things. Artificial intelligence and machine learning. The space shuttle. Supercomputers.
Parallelization is the technique of dividing a large computational task into smaller sub-tasks that can be executed concurrently on multiple processors or cores, with the goal of reducing overall computation time.
Parallel environments are programming environments designed for parallel computing in clusters. The environments enable execution of shared-memory and distributed-memory parallel applications.
Tools for Parallel Programming Two common solutions for creating parallel code are OpenMP and MPI. Both solutions are limited to the C++ or Fortran programming languages (though, other languages may be extended with C++ or Fortran code to utilize OpenMP or MPI).
Parallel environments are programming environments designed for parallel computing in clusters. The environments enable execution of shared-memory and distributed-memory parallel applications.
In computer science terms, parallel programming is the process of splitting a problem into smaller tasks that can be executed at the same time – in parallel – using multiple computing resources. In other words, parallel programming allows programmers to run large-scale projects that require speed and accuracy.

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Parallelization of Programs under Windows NT refers to the process of dividing a program into multiple concurrently executing segments that can run simultaneously across multiple processors, thereby optimizing performance and efficiency in the Windows NT operating system.
Developers and software engineers who create applications intended to take advantage of parallel processing capabilities in Windows NT are required to file information related to the Parallelization of Programs.
To fill out the Parallelization of Programs under Windows NT, developers should provide details about the program's architecture, specify the methods used for parallelization, and outline the resources required for execution in a multi-threaded environment.
The purpose of Parallelization of Programs under Windows NT is to enhance the execution speed and responsiveness of applications by leveraging parallel processing on multi-core processors, improving computational efficiency and reducing processing time.
Information that must be reported includes the program’s design, threading model, performance metrics, hardware requirements, and any dependencies or libraries essential for the program's parallel execution.
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