JuliaCon Global 2026

Explore the development of a Reactant backend for ParallelStencil.jl, detailing kernel code generation and data structure optimization for efficient GPU, TPU, and CPU execution.

Investigate the precision of mathematical functions in Julia, presenting a methodology and software tool for exhaustive accuracy testing across various floating-point formats.

Explore the foundational concepts of neural networks, genetic algorithms, and neuroevolution, drawing from a new book on practical artificial intelligence in Julia.

Discover recent advancements in Chmy.jl, focusing on coordinate-independent tensor expressions and automatic optimization techniques for finite-difference codes to maximize memory throughput.

Craig Stanfill traces the evolution of high-performance parallel computing, enterprise data architectures, and large-scale vector search for modern AI systems.

Reflect on the development journey of Turing.jl towards its v1.0 release, highlighting insights into open-source software development within the context of probabilistic programming.