Granularity (parallel computing)

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Computer engineering
High-performance computing
Parallel computing

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Related lectures (21)

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Dynamic Scheduling

Explores dynamic scheduling in processor design to increase parallelism by executing instructions out of order, improving performance and efficiency.

Data Operations and Parallel Mapping

Explores parallel programming in Scala, functional operations on collections, and parallel map functions on arrays and trees.

VLIW Architectures: Compilers and Instruction Level Parallelism

Covers VLIW architectures and compilers, focusing on instruction level parallelism and optimization techniques.

Superscalars: Execution and Cache Optimization

Explores superscalar processors, dynamic branch prediction, nonblocking caches, and control speculation.

Parallelism: Programming and Performance

Explores parallelism in programming, emphasizing trade-offs between programmability and performance, and introduces shared memory parallel programming using OpenMP.

Superscalars: Execution and Cache Requirements

Explores superscalar processors and cache optimization for improved performance.

Memory Consistency Models: Performance Impact and Parallel Computing

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

Parallel Programming in Scala: Performance Analysis

Delves into the performance analysis of parallel programs in Scala, covering asymptotic analysis, recursive functions, and Amdahl's Law.

GPUs: Architecture and Programming

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

Dynamic Scheduling in Processors: Enhancing Instruction Execution

Covers dynamic scheduling in processors, focusing on out-of-order execution and managing instruction dependencies effectively.