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Its really so easy to distinguish between the concurrent & parallel threads that

A parallel thread is the thread maintained the parallel processing system including the process sheduling system is quite murcible.

Where as the concurrent threading is the way of manupulation of a thread using simultaneous process threading.

It would be be quite better if the book named "Galvin & Siberscartz" book of "Operating System" the chapter process & threads would be followed for maximum details.

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How can I efficiently execute a Python run loop in parallel?

To efficiently execute a Python run loop in parallel, you can use libraries like multiprocessing or threading to create multiple processes or threads that run simultaneously. This allows you to take advantage of multiple CPU cores and speed up the execution of your loop. Be sure to carefully manage shared resources and handle synchronization to avoid conflicts between the parallel processes or threads.


Is executing two threads in parallel to improve multitasking performance?

hyper-threading dumb @sses


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In Python, the concurrent.futures module can be used to implement parallel processing similar to MATLAB's parfor. By using the ThreadPoolExecutor or ProcessPoolExecutor classes from this module, you can execute multiple tasks concurrently across multiple threads or processes. This allows for efficient parallel processing in Python.


What are the differences between semaphores and monitors in concurrent programming?

Semaphores and monitors are both synchronization mechanisms used in concurrent programming, but they have some key differences. Semaphores are simple integer variables that are used to control access to shared resources by multiple threads. They can be used to signal when a resource is available or to block threads until a resource becomes available. Semaphores are low-level and require explicit management by the programmer. Monitors, on the other hand, are higher-level constructs that encapsulate both data and synchronization operations within a single object. Monitors provide a more structured way to manage shared resources by allowing only one thread to access the resource at a time. Monitors also provide mechanisms for condition variables, which allow threads to wait for specific conditions to be met before proceeding. In summary, semaphores are lower-level synchronization primitives that require explicit management, while monitors are higher-level constructs that provide a more structured and convenient way to manage shared resources in concurrent programming.


List reasons why a Mode switch between threads may be cheaper than a Mode switch between processes?

Answer# 11. reason - the control blocks for processes are larger than for threads (hold more state information), so the amount of information to move during the thread switching is less than for process context switching 2. reason - the major reason is that the memory management is much simpler for threads than for processes. Threads share their memory so during mode switching, memory information does not have to be exchanged/changed, pages and page tables do not have to be switched, etc. This makes the thread context switch much cheaper than for processes. In case of processes the memory pieces (pages) need to be exchanged, etc. (Will talk about the details in few weeks). 3. reason - threads do not have to worry about accounting, etc, so do not have to fill out all the information about accounting and other process specific information in their thread control block, so keeping the thread control block consistent is much faster 4. reason - threads share files, so when mode switch happens in threads, these information stay the same and threads do not have to worry about it (similar to accounting information) and that makes the mode switch much faster.answer 2## Process :Generally heavy weight by, the PCB holds kernel objects the values generally referred as state information. A application can be divided into two types in design phase: 1.Process - may affect application/program architecture 2.Threads - didn't affect architecture Threads typically are spawned for a short-term benefit where as process for long-term even the thread share its own process address space is never larger than 4GB. A single process may hold "n" threads so exchanging value between process; then the CPU spend most of its time for swapping it leads to thrasing definitely. Threads easily exchange their locale variables within its scope but exchange value between process stolen more CPU cycles.

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