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JuliaAcademy

Parallel Computing

via JuliaAcademy

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Overview

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Video course on writing fast parallel code in Julia. Starts with performance measurement and serial single-core optimization, including SIMD vectorization, then moves through multithreading, parallel algorithm design, tasks (co-routines), distributed computing across processes, and GPU programming. Suited to programmers already comfortable with Julia who want their code to run faster on modern multicore and accelerator hardware.

Syllabus

Introduction
- Introduction (10:24)
- Performance Overview (27:16)

Optimizing Single-Core Performance
- Serial Performance (43:49)
- Single Instruction, Multiple Data (27:36)

Parallel Strategies
- Multithreading (52:04)
- Parallel Algorithm Design (14:53)
- Tasks (Co-routines) (19:23)
- Distributed Computing (66:23)
- GPUs (30:33)

Taught by

Dr. Matt Bauman

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