EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Jul 31·5 min read
Full flattening of nested data parallelism
TL;DR
Futhark has achieved full flattening of nested data parallelism, enabling the compilation of any Futhark program to parallel GPU code, overcoming previous limitations.
✦ Why It Matters
Start using Futhark for your GPU programming needs to leverage its full parallelism capabilities today.
Key Takeaways
- Utilize Futhark's full flattening for GPU compilation.
- Implement segmented arrays for irregular data handling.
- Maintain performance for existing Futhark programs.
- Explore new capabilities for complex parallel programming.
How It Works
The new flattening transformation in Futhark uses fission to break down complex nested operations into simpler, flat operations. This allows the compiler to generate efficient GPU code by transforming nested parallelism into a series of segmented operations, which can be executed in parallel without the overhead of irregular data structures.