Parallel and Distributed Computing Models Explained

Posted on Dec 21, 2025 in Computers

Parallel and Distributed Programming Models

Parallel Programming Models

Shared Memory Model: Allows all processors to use the same memory space.

Distributed Memory Model: Uses separate memory for each processor and relies on message passing.

Data Parallelism: Applies the same operation to many data items at once.

Task Parallelism: Runs different tasks in parallel on possibly different data.

Distributed Programming Models

  • Client-Server Model: Involves clients requesting services and servers providing them.
  • Peer-to-Peer Model: Allows each computer to act as both client and server.
  • Service-Oriented Architecture (SOA): Builds applications from independent services.
  • MapReduce: Processes large data sets using a map phase and a reduce phase.
  • Event-Driven Programming: Responds to actions or events from users or systems.

Micro-architectures Exploiting Parallelism in Modern CPUs

Instruction-Level Parallelism (ILP)

ILP enables the execution of multiple instructions from a single thread simultaneously.

  • Superscalar Architecture: Allows multiple instructions per clock cycle using multiple execution units.
  • Out-of-Order Execution: Lets the CPU execute instructions as resources become available, not just in order.
  • Branch Prediction: Guesses the outcome of conditional instructions to keep the pipeline full.
  • Speculative Execution: Runs instructions before knowing if they are needed and discards them if wrong.

Thread-Level Parallelism (TLP)

TLP enables the execution of multiple threads at the same time.

  • Multicore Processors: Contain multiple cores that run different threads concurrently.
  • Simultaneous Multithreading (SMT): Allows a single core to execute multiple threads by sharing resources.
  • Multithreading: Allows quick switching between threads to reduce idle time.
  • Context Switching: Helps maintain thread states and enables smooth task transitions.

Key Applications Driving Parallel and Distributed Systems

Applications of Parallel Systems

Scientific simulations, such as climate modeling and astrophysics, use parallel systems to run complex computations faster. High-performance computing tasks, like fluid dynamics and structural analysis, benefit from parallel execution. Machine learning model training uses parallel GPUs to speed up computation and reduce training time. Graphics rendering in gaming and virtual reality uses parallel processing for real-time visuals.

Applications of Distributed Systems

  • Big Data Analytics: Uses distributed frameworks like Hadoop and Spark to process large data sets.
  • Real-Time Systems: Such as fraud detection and stock trading, use distributed systems for low-latency processing.
  • IoT Systems: Rely on distributed computing to handle data from millions of connected devices.
  • Cloud Services: Provide scalable and on-demand resources through distributed infrastructure.

Virtual Machines and Architectures

Virtual Machines – Key Points

Virtual Machines (VMs) are software-based emulations of physical computers. VMs allow multiple operating systems to run on a single physical machine. Each VM has its own virtual CPU, memory, storage, and network interface. VMs enable better hardware utilization and application isolation.

Types of VM Architectures

Native (Bare-Metal) Virtual Machines

  • Hypervisor runs directly on physical hardware.
  • Offers high performance and low overhead.
  • No host OS is needed.
  • Examples: VMware ESXi, Microsoft Hyper-V.

Hosted Virtual Machines

  • Hypervisor runs on top of a host operating system.
  • Easier to install and manage, suitable for desktop virtualization.
  • May introduce performance overhead.
  • Examples: Oracle VirtualBox, VMware Workstation.

Paravirtualization

  • Host OS is modified to interact efficiently with the hypervisor.
  • Provides better performance than hosted VMs.
  • Reduces the need for full hardware emulation.
  • Example: Xen Hypervisor.

Note on Multithreading Technology

Multithreading allows multiple threads to run concurrently within a single process. A thread is a lightweight unit of execution that shares memory with other threads in the same process. Multithreading improves performance by making efficient use of CPU resources and enhances responsiveness, especially in user-interface-driven applications.

Threads in the same process share memory, enabling faster communication and lower overhead. Fine-grained multithreading switches threads frequently, improving CPU utilization during stalls. Coarse-grained multithreading switches threads less frequently to reduce context switching overhead. Synchronization mechanisms like mutexes and semaphores are used to avoid race conditions.

Multithreading is widely used in web servers, gaming, data processing, and real-time systems. Real-world CPUs distribute threads across multiple cores for better throughput and speed.

Scalable Computing Over the Internet

Scalable computing over the internet enables systems to maintain performance as workloads increase. Scalability refers to a system’s ability to grow and handle increased demands efficiently.

  • Vertical Scalability: Increases the capacity of a single machine.
  • Horizontal Scalability: Adds more machines to distribute the workload.

Distributed architectures like clusters, grids, and clouds support scalable computing. Cloud computing provides virtualized resources that scale on demand. Grid computing connects geographically dispersed systems to work together. Scalable systems manage variable workloads without performance degradation. Good scalability improves user experience by maintaining fast and reliable services. E-commerce sites use scalability to handle traffic surges during peak times.

Scalable Computing Trends and New Paradigms

Scalable computing is evolving with new technologies and increasing demand for flexible solutions.

  • Cloud Computing: Enables on-demand, scalable resource access over the internet (IaaS, PaaS, SaaS).
  • Microservices Architecture: Breaks applications into independent, scalable services, improving flexibility.
  • Containerization: Uses tools like Docker to create portable, scalable environments, increasing resource efficiency.
  • Serverless Computing: Removes infrastructure management, scaling functions automatically based on usage.
  • Edge Computing: Processes data near its source, reducing latency and enabling real-time responses.

Internet of Things (IoT) and Cyber-Physical Systems (CPS)

Internet of Things (IoT) – Key Points

IoT connects everyday objects to the internet for data exchange and automation. Devices include sensors, appliances, and vehicles with data communication abilities. Interconnectivity allows real-time communication between devices and systems. Data collection from surroundings enables better decisions through analysis. Automation and control use IoT data to optimize operations. Scalability supports billions of interconnected devices globally.

Cyber-Physical Systems (CPS) – Key Points

CPS integrates physical systems with computing and networking components. This involves the integration of cyber (software) and physical (sensors/actuators) elements. Real-Time Operation ensures timely responses to environmental changes. Feedback Loops enable CPS to adjust behavior based on real-world input.

GPU Computing Explained

GPU computing means using a graphics card (GPU) to solve big problems fast, going beyond just showing images. GPUs are really good at doing many small tasks at the same time, which helps speed up things like games, AI, video editing, and big data work.

How GPUs Work

  • GPUs have lots of small processors that work together.
  • They do the same job on many pieces of data at once (SIMD/SIMT).
  • GPUs have their own fast memory to work quickly.
  • Special tools like CUDA or OpenCL help programmers use GPUs better.

Cluster Versus Computational Grid

🖥️ Computer Cluster

  • All computers (nodes) are located in the same physical location.
  • Managed and controlled by a single organization.
  • Nodes are tightly connected using a fast local network.
  • Used for solving large problems using shared computing power; acts like a single powerful computer to the user.

🌐 Computational Grid

  • Computers are spread across different locations (geographically distributed).
  • Can be owned by multiple organizations or individuals.
  • Connected through the internet or wide area network (WAN).
  • Used to solve complex tasks by sharing resources from many systems; each system can work independently and join the grid when needed.