Julia's powerful GPU backends offer exceptional performance, but often result in code that is tightly coupled to a specific vendor's hardware. Join Tim Besard in this webinar to explore vendor-neutral GPU programming in Julia, using KernelAbstractions.jl and Julia's array abstractions.
Key Takeaways:
- Write flexible array code that can target different GPU architectures.
- Leverage KernelAbstractions.jl to create high-performance kernels that work across various vendors.
- Gain valuable insights into writing efficient and portable GPU code in Julia.
Don’t miss this opportunity to simplify your GPU programming workflow while expanding its flexibility.
