multiprocessing - TarisMajor/5143-OpSystems GitHub Wiki

Definition

Multiprocessing refers to the use of multiple processors (CPUs) in a computer system to perform tasks simultaneously. It allows the system to run multiple processes in parallel, improving the overall performance and efficiency, especially in tasks that require a significant amount of processing power. There are two main types of multiprocessing systems:

Symmetric Multiprocessing (SMP): In SMP systems, each processor has equal access to the system’s memory and I/O devices. All processors share the workload equally and are controlled by a single operating system instance. Asymmetric Multiprocessing (AMP): In AMP systems, one processor acts as the master and controls the system, while the others serve as slaves and execute tasks assigned by the master processor. Multiprocessing systems allow multiple processes or threads to run at the same time, each utilizing a separate processor or core. This is particularly beneficial for parallel processing tasks, where tasks can be broken down into smaller sub-tasks that can be executed simultaneously.

Inventor and Year of Invention The concept of multiprocessing has evolved over time, and while it is not attributable to a single inventor, it became a fundamental part of computer architecture in the 1960s and 1970s. Early multiprocessor systems were introduced by companies like IBM and CDC (Control Data Corporation). One of the earliest implementations was in IBM’s System/360 family of computers in the 1960s.

In the 1970s, Unix by Ken Thompson and Dennis Ritchie at AT&T Bell Labs began supporting multiprocessing, which laid the groundwork for modern operating systems with multi-core support. The SMP model became more widespread with the advent of multi-core processors in the 1990s, enabling more practical use of multiprocessing in everyday computing.

Uses

Multiprocessing is commonly used in systems that require high-performance computing and parallel processing, such as:

High-Performance Servers: Multiprocessing is often used in data centers and enterprise servers to handle multiple client requests and perform heavy computation tasks simultaneously, improving system throughput and availability.

Scientific Computing: In fields such as physics, chemistry, and biology, large-scale simulations (e.g., protein folding, climate modeling, and space simulations) often require parallel execution to process vast amounts of data and calculations efficiently.

Real-Time Systems: Systems that require immediate processing, such as air traffic control, medical devices, and robotics, use multiprocessing to handle multiple real-time tasks concurrently, ensuring that critical operations are completed without delay.

Cloud Computing: Multiprocessing is essential in cloud environments, where applications may be distributed across multiple virtual machines or containers, each utilizing a separate processor to handle different tasks concurrently.

Embedded Systems: High-end embedded systems (e.g., automotive systems, industrial automation, and drones) use multiprocessing to handle the complexity of modern applications, such as sensor data processing, AI-driven decision-making, and real-time control.

Multithreaded Applications: Modern software, such as web browsers, video games, and media editing software, often uses multiprocessing to execute different threads or processes concurrently, improving responsiveness and performance.

Gaming: Video games that require real-time rendering, artificial intelligence, and network communication benefit from multiprocessing, allowing these processes to run concurrently and reduce delays or lag.

Examples of Where Multiprocessing is Used Today

Personal Computers (Desktops and Laptops):

Modern personal computers use multi-core processors that support multiprocessing, allowing the system to perform multiple tasks at once. For instance, on a system with a quad-core processor, the operating system can run four different processes or threads in parallel. Operating systems like Windows, macOS, and Linux are designed to utilize multiprocessing to run applications efficiently. For example, a user can have a web browser open while running a video rendering program and playing a game—all simultaneously utilizing different CPU cores. Servers and Data Centers:

Web servers, database servers, and file servers use multiprocessing to manage numerous client requests, process data, and execute queries in parallel. This helps to improve throughput and reduce response times, making the system more efficient in handling a high volume of tasks concurrently. For example, Google and Amazon use massive parallel processing in their cloud infrastructure, where each server (or virtual machine) may run multiple processes concurrently to handle different aspects of the service (search, file storage, etc.). Supercomputers:

Supercomputers like the Fugaku (the fastest supercomputer in the world as of 2020) utilize multiprocessing to perform highly complex calculations in parallel. For example, supercomputers are used in scientific research, weather forecasting, and simulating complex phenomena like molecular dynamics. These systems often use thousands of processors working in tandem to solve problems too large for single processors to handle efficiently. Cloud Computing and Virtualization:

Cloud services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud use multiprocessing across many physical machines to handle virtualized workloads. Each virtual machine or container running on these systems can utilize one or more processors to perform specific tasks. Cloud-based applications like Google Docs or Microsoft 365 rely on multiprocessing to serve multiple users concurrently and ensure smooth performance across their infrastructure. Mobile Devices (Smartphones/Tablets):

Modern smartphones use multi-core processors to allow for smooth multitasking. For instance, Android and iOS smartphones use multiprocessing to run apps in the background (like music playback, notifications, or GPS) while actively running a user-facing app. Apps such as Instagram, Facebook, or Snapchat take advantage of multiprocessing to run background processes for notifications or uploads while the user is browsing or interacting with the interface. Automotive Industry:

Advanced Driver Assistance Systems (ADAS) and autonomous vehicles make use of multiprocessing to handle multiple complex tasks in real-time, such as object detection, path planning, and sensor data fusion. Vehicles from companies like Tesla and Waymo rely on multiprocessing to process data from cameras, LiDAR, and radar simultaneously to make driving decisions. Gaming Consoles:

PlayStation, Xbox, and Nintendo Switch gaming consoles use multiprocessing in their custom hardware to handle various tasks like physics simulation, AI calculations, rendering graphics, and networking, all in parallel to provide a seamless gaming experience. Advantages of Multiprocessing Improved Performance: Multiprocessing significantly enhances performance by distributing tasks across multiple processors or cores. It helps to speed up execution time, especially for resource-intensive tasks.

Increased Reliability and Redundancy: Systems using multiple processors can be more fault-tolerant. If one processor fails, others can take over, improving the overall system’s reliability.

Enhanced Resource Utilization: By utilizing multiple processors, the system can make better use of available hardware resources, reducing idle times and improving overall efficiency.

Parallel Processing: Multiprocessing enables parallelism, which is essential for tasks such as scientific simulations, image processing, and data mining.

Challenges and Considerations Complexity: Managing multiple processors requires sophisticated hardware and software mechanisms for process synchronization, load balancing, and resource management.

Overhead: Communication between processors and managing multiple processes can introduce overhead, which can impact the overall performance if not carefully optimized.

Cost: Multiprocessing systems, especially those with high numbers of processors or cores, tend to be more expensive due to the added hardware complexity and power requirements.

Sources

Tanenbaum, A. S., & Woodhull, D. J. (2009). Operating Systems: Design and Implementation (3rd ed.). Prentice Hall. Silberschatz, A., Galvin, P. B., & Gagne, G. (2018). Operating System Concepts (9th ed.). Wiley. Stallings, W. (2017). Operating Systems: Internals and Design Principles (9th ed.). Pearson Education. McKusick, M. K., & Neville-Neil, G. V. (2004). The Design and Implementation of the FreeBSD Operating System. Addison-Wesley. Hennessy, J. L., & Patterson, D. A. (2011). Computer Architecture: A Quantitative Approach (5th ed.). Elsevier.