Enhance Your Learning with Parallel Computing Flash Cards for quick learning
A type of computation in which many calculations or processes are carried out simultaneously, improving performance and solving complex problems efficiently.
The design and structure of computer systems that enable parallel processing, including shared memory, distributed memory, and hybrid architectures.
An algorithm designed to solve a problem by dividing it into smaller subproblems that can be solved concurrently, exploiting parallelism for faster execution.
A framework or abstraction that allows programmers to express parallel algorithms and control the execution of parallel programs on parallel architectures.
A method or approach used in parallel computing to achieve efficient and scalable execution, such as task parallelism, data parallelism, or pipeline parallelism.
The process of evaluating and measuring the performance of parallel programs and systems, identifying bottlenecks, and optimizing for better efficiency and scalability.
The use of parallel computing techniques to solve real-world problems in various domains, such as scientific simulations, data analytics, image processing, and machine learning.
The difficulties and obstacles faced in parallel computing, including load balancing, synchronization, communication overhead, scalability, and programming complexity.
The potential advancements and trends in parallel computing, including the development of new architectures, algorithms, programming models, and applications for improved performance and efficiency.
The ratio of the execution time of a sequential algorithm to the execution time of a parallel algorithm solving the same problem, indicating the performance improvement achieved through parallelization.
A formula that predicts the maximum speedup achievable by parallelizing a computation, taking into account the fraction of the computation that cannot be parallelized.
A formula that emphasizes the importance of scaling the problem size with the number of processors to achieve better performance in parallel computing, in contrast to Amdahl's Law.
A classification system for computer architectures based on the number of instruction streams and data streams available, including SISD, SIMD, MISD, and MIMD.
A parallel computing architecture where multiple processors share a common memory space, allowing them to access and modify data stored in the shared memory.
A parallel computing architecture where each processor has its own private memory and communicates with other processors through message passing, exchanging data explicitly.
A parallel computing architecture that combines elements of both shared memory and distributed memory architectures, leveraging the advantages of both approaches.
A parallel programming model where a master thread forks multiple parallel tasks, which are executed concurrently, and then joins the results back to the master thread.
A parallel programming technique where the same operation is performed on different data elements simultaneously, exploiting parallelism at the data level.
A parallel programming technique where different tasks or subproblems are executed concurrently by multiple processors, exploiting parallelism at the task level.
A parallel programming technique where a sequence of operations is divided into stages, and each stage is executed by a different processor, overlapping computation and communication.
The distribution of computational workload evenly among processors in a parallel system, ensuring that each processor has a similar amount of work to maximize efficiency.
The coordination and ordering of parallel tasks or threads to ensure correct and consistent execution, preventing race conditions and data inconsistencies.
The additional time and resources required for communication and synchronization between parallel tasks or processors, affecting the overall performance of parallel programs.
The ability of a parallel system or algorithm to maintain or improve performance as the problem size or number of processors increases, without significant degradation.
The challenges and difficulties faced by programmers in developing and debugging parallel programs, including race conditions, deadlocks, and data dependencies.
A standardized library and communication protocol used in parallel computing to enable message passing between processes or tasks running on different processors.
A parallel programming API for shared memory architectures, providing directives and library routines to express parallelism and control the execution of parallel programs.
A parallel computing platform and programming model developed by NVIDIA, allowing developers to use GPUs for general-purpose parallel computing using the CUDA programming language.
A programming model and software framework for processing large-scale data sets in parallel, dividing the computation into map and reduce tasks that can be executed concurrently.
The process of sorting a collection of elements in parallel, utilizing multiple processors or threads to divide the sorting task and achieve faster sorting times.
The computation of the product of two matrices using parallel algorithms and techniques, distributing the workload among multiple processors for improved performance.
Algorithms designed to solve graph-related problems in parallel, such as graph traversal, shortest paths, minimum spanning trees, and graph clustering.
The use of parallel computing to perform Monte Carlo simulations, which involve repeated random sampling to estimate numerical results or analyze complex systems.
The training and execution of artificial neural networks using parallel computing techniques, leveraging multiple processors or GPUs for faster learning and prediction.
The optimization and search of solution spaces using genetic algorithms in parallel, exploring multiple candidate solutions simultaneously for improved efficiency.
The analysis and processing of large-scale datasets using parallel computing, enabling faster insights and decision-making in various domains, such as finance, healthcare, and marketing.
The manipulation and analysis of digital images using parallel computing techniques, allowing for faster image filtering, enhancement, segmentation, and feature extraction.
The training and inference of machine learning models using parallel computing, accelerating the learning process and enabling real-time predictions on large datasets.
The use of parallel computing techniques and architectures to achieve high-performance computing capabilities, delivering faster and more efficient computations.
The use of parallel computing in supercomputers, combining thousands or millions of processors to solve complex scientific, engineering, and computational problems.
The utilization of parallel computing in cloud computing environments, enabling scalable and on-demand processing power for various applications and workloads.
The application of parallel computing principles and techniques in quantum computing, harnessing the power of quantum systems for solving complex problems.
The use of parallel computing to process and analyze massive volumes of data in big data applications, enabling faster insights and knowledge extraction.
The utilization of parallel computing in artificial intelligence systems, accelerating training and inference tasks for deep learning models and intelligent algorithms.
The application of parallel computing to perform large-scale scientific simulations, such as weather forecasting, molecular dynamics, astrophysics, and computational fluid dynamics.
The use of parallel computing to perform complex financial modeling and simulations, enabling faster risk analysis, portfolio optimization, and option pricing.
The utilization of parallel computing in healthcare applications, such as medical imaging, genomics, drug discovery, and personalized medicine, for faster and more accurate results.
The use of parallel computing techniques in video game development, enabling realistic graphics, physics simulations, artificial intelligence, and immersive gameplay experiences.
The application of parallel computing to cryptographic algorithms and protocols, enhancing the security and efficiency of encryption, decryption, and key generation operations.
The utilization of parallel computing in Internet of Things (IoT) systems, enabling real-time data processing, analytics, and decision-making at the edge and in the cloud.
The use of parallel computing architectures and techniques in data centers, supporting the efficient processing and storage of large-scale data for various applications and services.
The application of parallel computing in high-frequency trading systems, enabling faster market analysis, algorithmic trading, and real-time decision-making for financial transactions.
The utilization of parallel computing to perform weather forecasting and climate modeling, processing massive amounts of meteorological data for accurate predictions and simulations.
The use of parallel computing to analyze biological data, such as DNA sequencing, protein folding, and genome-wide association studies, for understanding biological processes and diseases.
The application of parallel computing to solve complex physical problems, such as quantum mechanics, fluid dynamics, astrophysics, and materials science, for scientific research and simulations.
The utilization of parallel computing to perform computationally intensive chemistry simulations, such as molecular dynamics, quantum chemistry, and drug discovery, for understanding chemical properties and reactions.
The use of parallel computing to perform financial modeling, risk analysis, option pricing, and portfolio optimization, supporting decision-making and trading strategies in the finance industry.
The application of parallel computing to solve engineering problems, such as structural analysis, fluid flow simulations, finite element analysis, and optimization, for designing and optimizing complex systems.
The utilization of parallel computing to analyze social data and networks, perform sentiment analysis, recommendation systems, and social simulations, for understanding human behavior and societal dynamics.
The use of parallel computing to process and analyze natural language data, perform machine translation, sentiment analysis, and language modeling, for advancing language technologies and understanding human language.