Re-posted from: https://info.juliahub.com/blog/bridging-julia-and-c-with-cxxwrap.jl
The Julia programming language is gaining traction as a powerful tool for modeling and simulation due to its speed, ease of use, and dynamic nature. However, many legacy simulation libraries are written in C++, a language known for its efficiency and extensive ecosystem. The challenge arises when trying to integrate high-performance C++-based simulation tools into Julia workflows.
By: Jasmine Chokshi
Re-posted from: https://info.juliahub.com/blog/c-code-generation-julia
For years, a sort of dogma has prevailed in the world of embedded systems and real-time control: if you’re using a high-level language like Python or R for modeling and simulation, you must rewrite your code in C for deployment. But what if there was a better way? What if you could leverage the productivity of high-level languages without sacrificing performance?
Re-posted from: https://info.juliahub.com/blog/vendor-neutral-gpu-programming-in-julia
GPUs are at the heart of scientific computing, AI, and high-performance computing (HPC), yet most GPU programming remains tied to vendor-specific frameworks like CUDA (NVIDIA), ROCm (AMD), and oneAPI (Intel). While these frameworks offer highly optimized performance, they create major portability issues for developers who need their code to run across different hardware.