Introduction to Parallel Processing Lecture Notes PDF

Introduction to Parallel Processing Introduction to Parallel Processing Parallel Processing Parallel processing refers to the simultaneous execution of multiple tasks or computations. It allows for faster processing by dividing tasks into smaller parts that can run concurrently on m...

Introduction to Parallel Processing Introduction to Parallel Processing Parallel Processing Parallel processing refers to the simultaneous execution of multiple tasks or computations. It allows for faster processing by dividing tasks into smaller parts that can run concurrently on multiple processors. Importance of Parallel Processing in Computing Reduces computation time for large problems. Enables handling of more complex problems, such as simulations and big data processing. Introduction to Parallel Processing Concurrent Execution Definition: Multiple tasks start, run, and complete in overlapping time periods. However, they may not necessarily execute simultaneously. Execution: Achieved by context switching, where the CPU alternates between tasks. Example: Multitasking on a single-core processor where different tasks share CPU time slices. Key Feature: Focuses on managing multiple tasks in progress, even if they aren't running at the same exact time. Introduction to Parallel Processing Parallel Execution Definition: Multiple tasks run simultaneously on multiple processors or cores. Execution: True parallelism requires a multicore or multiprocessor system where tasks are divided and executed at the same time. Example: Matrix multiplication performed by multiple threads on different cores simultaneously. Key Feature: Requires hardware support for simultaneous task execution. Introduction to Parallel Processing Parallel Execution Definition: Multiple tasks run simultaneously on multiple processors or cores. Execution: True parallelism requires a multicore or multiprocessor system where tasks are divided and executed at the same time. Example: Matrix multiplication performed by multiple threads on different cores simultaneously. Key Feature: Requires hardware support for simultaneous task execution. Introduction to Parallel Processing Amdahl’s law Introduction to Parallel Processing Amdahl's Law is used to determine the potential speedup of a program or system when only a portion of it can be parallelized while the rest remains sequential. It is particularly useful in parallel computing to understand the impact of adding more processors to a computing system and how it affects the overall speedup. Introduction to Parallel Processing Example: Suppose that 80% of a task can be parallelized, while the remaining 20% must be executed sequentially. If you have 4 processors available, Amdahl's Law can be used to calculate the speedup as follows: In this example, the theoretical maximum speedup achievable by parallel processing would be 2.5 times faster than the sequential execution of the task. Types of Parallelism Instruction-Level Parallelism (ILP): Exploits parallelism within a single processor (e.g., pipelining, superscalar architectures). Example: A CPU executing multiple instructions simultaneously from a single instruction stream. Data Parallelism: Distributes data across multiple processing elements and applies the same operation to each. Example: Matrix multiplication, image processing. Types of Parallelism Task Parallelism: Different tasks or functions are executed concurrently across multiple processors. Example: Running different parts of a simulation or different queries in a database system. Pipeline Parallelism: Tasks are divided into stages, and multiple data items are processed in parallel through these stages. Example: Manufacturing assembly lines, data streaming systems. Parallel Computing Models Shared Memory Model: Multiple processors access the same shared memory space. Common in multi-core processors. Advantages: Fast communication between processors (low latency). Challenges: Synchronization and memory contention issues. Example: OpenMP (Open Multi-Processing). Distributed Memory Model: Each processor has its own local memory, and processors communicate via message passing (e.g., MPI). Advantages: Scalability across large systems. Challenges: Higher communication overhead, slower data sharing. Example: A cluster of nodes with message-passing interfaces. Parallel Computing Models Hybrid Model: A combination of shared and distributed memory systems. Example: NUMA (Non-Uniform Memory Access) architecture. Massively Parallel Model: Large-scale systems with thousands or millions of processors (e.g., supercomputers). Example: GPU-based architectures for parallel tasks. Decomposition in Parallel Computing Data Decomposition: Splitting large data into smaller chunks, each processed by a different processor. Example: Breaking a large matrix into sub-matrices for parallel multiplication. Task Decomposition: Dividing a task into smaller independent sub-tasks that can be executed concurrently. Example: Breaking a video encoding task into separate functions. Decomposition in Parallel Computing Hybrid Decomposition: Combining data and task decomposition for more efficient parallelism. Example: Parallelizing both the data and functions in large- scale simulations. Pipeline Decomposition: Breaking a task into a series of stages that can process multiple data items concurrently. Example: Video streaming, where data is processed through multiple stages (decoding, buffering, rendering). 6. Parallel Algorithms and Techniques Parallel Sorting Algorithms: Parallel Merge Sort and Parallel QuickSort. Techniques for dividing the data and sorting it concurrently. Matrix Operations: Strassen’s Algorithm for matrix multiplication (divides matrices into sub-matrices for parallel multiplication). LU Decomposition for solving linear equations. 6. Parallel Algorithms and Techniques Graph Algorithms: Parallel algorithms for graph traversal: Breadth-First Search (BFS) and Depth-First Search (DFS). Parallel shortest path algorithms like Dijkstra’s and Bellman- Ford. MapReduce Framework: A model for processing large datasets by dividing the task into smaller sub-tasks (Map phase), then combining the results (Reduce phase). Example: Word counting in a text file. 7. Load Balancing and Performance Optimization Load Balancing in Parallel Systems: Static Load Balancing: Predefined division of tasks across processors. Dynamic Load Balancing: Tasks are reassigned during execution to balance workloads (e.g., work-stealing, master-slave approach). Performance Bottlenecks: Communication Overhead: The time spent in communication between processors can limit performance. Synchronization Overhead: The time spent waiting for tasks to synchronize. Optimizing Performance: Strategies like minimizing communication and reducing synchronization barriers. 8. Parallel I/O and Memory Management Parallel I/O Systems: Using parallelism to speed up data input/output in applications like scientific simulations. Parallel File Systems: GPFS, Lustre. Memory Management: Shared Memory Model: Memory management in multi-core processors, cache coherence protocols. Distributed Memory Model: How distributed systems manage memory and data consistency (e.g., MPI). 9. Hardware Architectures for Parallelism Multi-Core Processors: Modern CPUs with multiple cores designed for parallel execution. GPUs for Parallel Processing: GPU Computing: Thousands of threads working simultaneously on large datasets (common in deep learning, scientific computing). CUDA (Compute Unified Device Architecture) programming for GPUs. Supercomputers: Architecture of massively parallel systems. Examples like Fugaku, IBM BlueGene. 10. Applications of Parallel Processing Scientific Computing: Parallel simulations in physics, chemistry, biology (e.g., molecular dynamics, climate modeling). Machine Learning and AI: Speeding up training of large neural networks using parallel architectures (e.g., using GPUs for deep learning). Big Data and Cloud Computing: Parallelizing tasks like data mining, real-time analytics, and large-scale database operations. Real-Time Systems: Autonomous vehicles, robotics, and industrial automation systems require real-time parallel processing. 11. The Future of Parallel Processing Quantum Computing: Introduction to quantum parallelism using qubits. Neuromorphic Computing: Using brain-like architectures to improve parallel processing efficiency. Edge Computing: Distributing parallel processing closer to where data is generated (e.g., IoT devices). 11. The Future of Parallel Processing Quantum Computing Quantum computing is an advanced field of computing that leverages the principles of quantum mechanics, such as superposition, entanglement, and quantum interference, to perform calculations far beyond the capabilities of classical computers. Here's an overview: 1. Qubits: The fundamental unit of quantum information, analogous to classical bits but can exist in a superposition of 0 and 1 simultaneously. 2. Superposition: Enables qubits to be in multiple states at once, allowing quantum computers to process vast amounts of data in parallel. 11. The Future of Parallel Processing 3. Entanglement: A quantum phenomenon where qubits become interconnected, meaning the state of one qubit can instantly affect another, regardless of distance. 4. Quantum Gates: Analogous to classical logic gates, but they manipulate qubits' states using quantum operations. 5. Quantum Interference: Used to amplify correct solutions while diminishing incorrect ones during calculations. 11. The Future of Parallel Processing Applications: 1.Cryptography: 1.Breaking classical encryption schemes (e.g., RSA) and developing quantum-secure algorithms. 2.Optimization: 1.Solving complex optimization problems in logistics, finance, and manufacturing. 3.Material Science: 1.Simulating quantum systems for drug discovery, chemical reactions, and new material development. 11. The Future of Parallel Processing 4. Artificial Intelligence: Enhancing machine learning models with quantum algorithms for faster processing and deeper insights. 5. Climate Modelling: Simulating large-scale systems to predict climate changes more accurately. 11. The Future of Parallel Processing Challenges: 1.Hardware Stability: Quantum systems are prone to errors due to decoherence and noise. 2.Scalability: Building quantum computers with a large number of reliable qubits. 3.Error Correction: Developing efficient quantum error correction methods. 4.Accessibility: High costs and limited availability for practical applications. 11. The Future of Parallel Processing Current State: Companies like IBM, Google, and Microsoft are leading the development of quantum computers. Quantum Supremacy: Google claimed to achieve this in 2019 by performing a calculation on a quantum processor that would take a classical supercomputer thousands of years. 11. The Future of Parallel Processing Neuromorphic Computing: Neuromorphic computing is a paradigm of computation inspired by the structure and function of biological nervous systems, particularly the human brain. It aims to create hardware and software systems that mimic the neural architecture, using principles such as spiking neurons, synaptic plasticity, and parallel processing to achieve efficient and adaptive computation. 11. The Future of Parallel Processing 1. Brain-Inspired Architecture: Neuromorphic systems emulate the brain's neural networks using artificial neurons and synapses. 2. Event-Driven Processing: Unlike conventional systems, which rely on clock-based processing, neuromorphic systems are asynchronous and process data only when events (spikes) occur. 11. The Future of Parallel Processing 3. Low Power Consumption: Leveraging analog and mixed-signal circuits, these systems significantly reduce energy consumption, ideal for IoT and edge devices. 4. Adaptivity: These systems can learn and adapt to new information in real-time, similar to how the brain adjusts to stimuli. 11. The Future of Parallel Processing Applications: 1.Edge AI: 1.Efficient processing of sensory data (e.g., vision, audio) in devices with limited power. 2.Robotics: 1.Enabling real-time decision-making and motor control with low energy costs. 3.Healthcare: 1.Brain-machine interfaces, prosthetics, and early disease detection through pattern recognition. 11. The Future of Parallel Processing 4. Autonomous Systems: Improving the decision-making and energy efficiency of drones, vehicles, and other autonomous platforms. 5. Neurological Research: Simulating brain functions to study diseases like Alzheimer’s and Parkinson’s. 11. The Future of Parallel Processing Key Technologies: 1.Spiking Neural Networks (SNNs): 1.Modelled after biological neural networks, SNNs process information through spikes, mimicking neuron communication. 2.Specialized Hardware: 1.Examples include IBM’s TrueNorth, Intel’s Loihi, and SpiNNaker. These chips are designed for neuromorphic workloads. 3.Memristors: 1.Memory-resistive components that emulate synaptic behavior, essential for efficient neural computations. 11. The Future of Parallel Processing Advantages: 1.Energy Efficiency: 1. Ideal for battery-powered and embedded devices. 2.Real-Time Processing: 1. Supports applications requiring immediate responses. 3.Scalability: 1. Potential to model large-scale neural systems with minimal resource usage. 4.Resilience: 1. Capable of graceful degradation, where partial failures do not result in complete system breakdown. 11. The Future of Parallel Processing Challenges: 1.Complexity of Algorithms: 1. Developing algorithms compatible with spiking neuron models is non- trivial. 2.Hardware Limitations: 1. Designing and manufacturing scalable, reliable neuromorphic hardware remains challenging. 3.Standardization: 1. Lack of standardized tools and frameworks for developing neuromorphic applications. 4.Integration: 1. Combining neuromorphic systems with conventional computing for hybrid applications. 11. The Future of Parallel Processing Difference between Neuromorphic computing and Artificial neural networks (ANNs) Neuromorphic computing replicates the structure and dynamics of the biological brain, using spiking neurons and asynchronous, event- driven processing. It operates on specialized hardware (like Intel’s Loihi) for real-time, energy-efficient tasks, ideal for edge devices and sensory processing. Artificial neural networks (ANNs) are simplified abstractions of the brain, using continuous values and layer-based computations. They rely on conventional hardware (CPUs, GPUs) and excel in high- performance tasks like image recognition and natural language processing, but consume more energy and require large datasets for training. 11. The Future of Parallel Processing Edge Computing: Edge computing is a distributed computing paradigm that brings computation, storage, and data processing closer to the location where it is needed, such as devices, sensors, or local networks, rather than relying on a centralized data center or cloud. 11. The Future of Parallel Processing Proximity: Computations are performed near the data source (e.g., IoT devices, sensors), reducing the need to send data to distant servers. Low Latency: Enables real-time data processing by minimizing the delay caused by data transmission to the cloud. Bandwidth Optimization: Reduces the amount of data sent over the network, conserving bandwidth and lowering costs. Decentralization: Distributes processing power across multiple nodes at the "edge" of the network. 11. The Future of Parallel Processing Benefits: 1.Faster Response Times: 1. Ideal for latency-sensitive applications like autonomous vehicles, video streaming, and gaming. 2.Enhanced Security: 1. Data is processed locally, reducing exposure to cyberattacks during transmission. 3.Cost Savings: 1. Decreases the dependency on expensive cloud storage and bandwidth. 4.Scalability: 1. Supports the growing number of IoT devices and data they generate. 5.Reliability: 1. Local processing allows systems to function even with intermittent internet connectivity. 11. The Future of Parallel Processing Applications: 1.IoT (Internet of Things): 1. Smart homes, factories, and healthcare devices process data locally for real-time actions. 2.Autonomous Vehicles: 1. Processes data from sensors and cameras in real-time for navigation and decision- making. 3.Retail: 1. Enables real-time analytics for customer behavior and inventory management in stores. 4.Healthcare: 1. Wearable devices and remote patient monitoring systems provide instant feedback. 5.Content Delivery: 1. Video streaming services like Netflix use edge computing to cache content closer to users.