EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 15·5 min read
Auto-research with codex: How I achieved a 232x Faster Kernel
TL;DR
Using Codex in GPU mode, a 232x speedup was achieved in implementing batched square compact-Householder QR factorization, significantly enhancing performance over the baseline.
✦ Why It Matters
Explore using Codex for your own computational optimizations to achieve significant performance improvements.
Key Takeaways
- Utilize AI tools for iterative testing and optimization.
- Implement the blocked Householder algorithm for matrix operations.
- Focus on real-time feedback to refine solutions.
- Explore auto-research contests to enhance skills and knowledge.
How It Works
The blocked Householder algorithm optimizes QR decomposition by processing a narrow panel of columns, allowing for efficient matrix multiplications. This approach confines serial work to a small area while leveraging GPU capabilities for larger operations, significantly speeding up the overall computation.