Lesson Highlight: Julia for High-Performance Computing
Write high-performance, parallel, and GPU-accelerated code — all in one elegant language. Why this lesson matters What if you could combine the performance of C or Fortran with the simplicity […]
Julia for High-Performance Scientific Computing (Online)
Overview Welcome to the online workshop on Julia for High-Performance Scientific Computing on Dec. 9-12 (2024). In this four-half-day course, we will start with the basic features of Julia, and […]
[Webinar] Practical intro to GPU programming in Python and Julia
ENCCS and SciLifeLab are organising a webinar giving an intro on GPU programming in Python and Julia. This event is part of the Tools for AI/ML research in life sciences. Practical […]
Julia for High Performance Data Analysis
Julia is a modern high-level programming language that is fast (on par with traditional HPC languages like Fortran and C) and relatively easy to write like Python or Matlab. It […]
Julia for High-Performance Scientific Computing
Julia is a modern high-level programming language that is fast (on par with traditional HPC languages like Fortran and C) and relatively easy to write like Python or Matlab. It […]
Julia for High-Performance Scientific Computing
Julia is a modern high-level programming language which is both fast (on par with traditional HPC languages like Fortran and C) and relatively easy to write like Python or Matlab.
A brief tour of Julia for High-Performance Computing
want to give a flavor of Julia’s features and capabilities for high-performance computing (HPC) covered in the workshop lesson material by presenting a demonstration on how to benchmark, optimize, parallelize and GPU-port a simple toy problem.
First ENCCS workshop on the Julia language
Julia is a free and open-source programming language designed from the ground up to offer high performance while being simple to write.
Julia for High-Performance Scientific Computing
Julia is a modern high-level programming language which is both fast (on par with traditional HPC languages like Fortran and C) and relatively easy to write like Python or Matlab. It thus solves the “two language problem”, i.e. when prototype code in a high-level language needs to be combined with or rewritten in a lower-level language to improve performance. Although Julia is a general purpose language, many of its features are particularly useful for numerical scientific computation, and a wide range of both domain-specific and general libraries are available for statistics, machine learning and numerical modeling. The language supports parallelisation for both shared-memory and distributed HPC architectures, and native Julia libraries are available for running on GPUs from different vendors.