Advanced Julia Programming and Parallel Computing

JUL-104 (2 Days)
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Advanced Julia Training Overview

This Advanced Julia training course teaches attendees how to leverage the power of the Julia language to manage challenging computational jobs. Students learn how to harness Julia's robust compilation processes and how to scale across many threads, GPUs, processes, and machines.

Location and Pricing

This course is taught as a private online class for teams of 3 or more. All our private online courses are hands-on, instructor-led, and customizable to fit your group’s goals and needs. To receive a customized proposal and price quote, please contact us.

Advanced Julia Training Objectives

All students will learn how to:

  • Optimize their Julia code for highest performance
  • Build production-ready Julia packages
  • Use Julia for complex high throughput data science workflows
  • Leverage the power of language to parallelize and distribute computational workloads
  • Understand how Julia code can be run on GPUs

Advanced Julia Training Outline

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Introduction
Writing Efficient, High Performant Julia Code
  • Julia LLVM compiler system
  • Macros and overview of metaprogramming
  • Type stability
  • Julia performance tips
  • Non materialized data structures (sparse arrays, banded matrices)
Julia Package Ecosystem
  • Packages and artifacts
  • Managing package repositories
  • Managing packages
  • Building production quality packages (CI, automated unit testing, coverage)
  • Making a package public
Parallel and Distributed Computing with Julia
  • SIMD
  • Green-threading and asynchronous computing
  • Multi-threading
  • Local multiprocessing
  • Typical Julia parallelization design patterns
  • Inter-process communication
  • Computational clusters and distributed computing
Introduction to GPU Computing
  • Writing cross compiler compatible (GPU/CPU) Julia code
  • CuArrays.jl
  • Overview of deep learning with Flux.jl
Conclusion
Request Pricing for Advanced Julia Programming and Parallel Computing

Lecture percentage

50%

Lecture/Demo

Lab percentage

50%

Lab

Course Number:

JUL-104

Duration:

2 Days

Prerequisites:

Students must have a basic knowledge of using Julia for daily data science tasks, as taught in Introduction to Julia for Data Scientists. In addition, participants must have practical experience with daily data science jobs in any popular language (Julia, Python, R, or MATLAB)  

Training Materials:

All Julia programming training students receive comprehensive courseware.

Software Requirements:

Students must have a modern web browser and Internet access. Accelebrate will provide installation instructions (for Windows, Linux, and macOS).

Contact Us:

Accelebrate’s training classes are available for private groups of 3 or more people at your site or online anywhere worldwide.

Don't settle for a "one size fits all" public class! Have Accelebrate deliver exactly the training you want, privately at your site or online, for less than the cost of a public class.

For pricing and to learn more, please contact us.

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