Parallelism: Programming and Performance

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Related lectures (32)

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GPUs: Architecture and Programming

Explores GPU architecture, multithreading, SIMD processors, and CUDA programming for parallel computing.

Memory Consistency Models: Performance Impact and Parallel Computing

Covers memory consistency models, parallel computing, atomic subroutines, GPU architecture, and multithreading.

Parallel Computing: Principles and OpenMP

Covers the principles of parallel computing and introduces OpenMP for creating concurrent code from serial code.

GPUs: Multithreading and Architecture

Explores GPUs' architecture, multithreading, and their role in machine learning, discussing limitations and future trends.

Principles of Parallel Computing: OpenMP

Explores the principles of parallel computing, focusing on OpenMP as a tool for creating concurrent code from serial code.

Parallel Programming I

Covers the basics of parallel programming, including concurrency, forms of parallelism, synchronization, and programming models like PThreads and OpenMP.

Multi-threaded Processors

Covers the basics of multi-threaded processors, including design, performance impact, and pipeline utilization.

Understanding Simultaneous Multithreading in Modern Processors

Covers simultaneous multithreading, its implementation, and its impact on processor performance.

GPUs: Introduction to CUDA

Introduces the basics of GPUs, CUDA programming, and thread synchronization for parallel computing applications.

Principles of Parallelism

Covers the basics of parallelism, including physical examples, historical context, multicore era, and parallel collections in Scala.