1. Introduction to Parallel computing | Serial Computing| | HPC

Summary

In this video, the basics of serial computing are explored, explaining how tasks are executed one after another by a single processor, which can lead to inefficiencies. The video highlights the evolution of computing from Von Neumann architecture to modern processing techniques, including increasing clock speeds, vectorization, multi-threading, and hyper-threading. These methods aim to improve computational efficiency. However, as tasks continue to grow in complexity, the need for parallel computing becomes clear. The video sets the stage for understanding how multiple tasks can be processed simultaneously to achieve faster execution, paving the way for the next video on parallel computing.

Takeaways

• 😀 Parallel computing is introduced after understanding the limitations of serial computing.
• 😀 Serial computing involves executing one task at a time, which can lead to low performance when handling large workloads.
• 😀 Early computers followed the Von Neumann architecture with input/output devices, a CPU, and memory.
• 😀 A process is an instance of an executing program, and a thread is a lightweight sub-process created to help with task execution.
• 😀 Tasks in serial computing are executed one after another, causing the processor to be overwhelmed with long execution times.
• 😀 Clock speed, the operating speed of the processor, was increased to improve performance, but it generates more heat, limiting further increases.
• 😀 Vectorization allows algorithms to process multiple values simultaneously, increasing processing speed.
• 😀 Multi-threading allows a single process to be divided into multiple threads, enabling multiple tasks to be executed at the same time.
• 😀 Hyper-threading is a technology that allows a single CPU core to appear as multiple virtual cores, improving multitasking and processing speed.
• 😀 Improvements in memory architecture (e.g., DDR1 to DDR4) have enhanced data transfer speed between the CPU and memory, further improving overall system performance.

Q & A

What is serial computing?

Serial computing is a type of computing in which tasks are executed and completed one at a time by a single processor. Each task must finish before the next one begins, which can result in lower performance when handling multiple tasks or heavy workloads.

What are some key assumptions made in the script about how CPUs and tasks function?

The script assumes that a single CPU core performs one task at a time, with each task being completed before the next one starts. Additionally, it clarifies that 'CPU' refers to the physical hardware in the system, while 'processor' or 'core' refers to the units responsible for executing tasks.

How does the von Neumann architecture impact early computer design?

The von Neumann architecture laid the foundation for digital computers, incorporating input/output devices, a central processor (CPU), and memory (RAM) that stores data temporarily or permanently. This architecture set the standard for how data is processed and handled in computers.

What is the process of application execution described in the video?

When an application is launched, it first loads into memory (RAM). The CPU then retrieves the instructions from memory and executes them. Frequently used data is stored in the processor's cache memory for quicker access during execution.

What is a process in computing, as defined in the video?

A process is an instance of an executing program. It begins when an application is launched and runs until completion. Each process occupies a certain amount of memory and is executed by the CPU.

How do threads differ from processes?

Threads are lightweight sub-processes that help perform smaller tasks within a larger process. Unlike processes, which have their own memory space, threads share the same memory space as their parent process but can run concurrently to improve performance.

What role do threads play in improving computational efficiency?

Threads allow a process to perform multiple tasks simultaneously, utilizing more cores of the CPU to divide and conquer large tasks. For instance, a web browser can open multiple tabs (each with its own thread) while keeping the overall process running efficiently.

Why is serial computing limited when handling large workloads?

Serial computing is limited because a single processor handles all tasks sequentially, which becomes inefficient as the number of tasks increases. As the processor gets overwhelmed, task completion takes longer, leading to reduced overall performance.

What is vectorization, and how does it improve processing speed?

Vectorization involves processing multiple values simultaneously rather than one at a time. By using vector units, tasks like adding numbers can be executed on multiple data points simultaneously, significantly speeding up computation compared to serial execution.

What is hyper-threading, and how does it differ from multi-threading?

Hyper-threading is a technology that allows a single physical CPU to behave like multiple virtual CPUs, increasing the number of threads the processor can handle. Unlike multi-threading, which uses multiple physical cores for parallel processing, hyper-threading simulates additional cores within a single processor.

How does parallel computing address the limitations of serial computing?

Parallel computing seeks to overcome the limitations of serial computing by executing multiple tasks simultaneously, using multiple processors or cores. This significantly improves computation speed, making it possible to handle large and complex workloads more efficiently.