From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon
Figure 1: CUDA-to-MLX optimization translation map. CUDA optimization knowledge can be translated into architecture-native MLX strategies rather than copied instruction-for-instruction. We face a new epoch in computing. Hardware is changing rapidly — not just faster GPUs, but a growing range of chips from different vendors, each with its own architecture and often tailored to specific AI…
From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon
The CUDA ecosystem has amassed decades of kernel optimization knowledge, but transferring that expertise to new hardware, such as Apple Silicon, is challenging. K-Search, an evolutionary kernel optimization framework developed by Shiyi Cao at UC Berkeley Sky Lab, aims to bridge this gap. By combining AI-driven optimization with Apple's MLX framework, K-Search can adapt existing CUDA kernels for high-performance execution on Apple Silicon chips.
Written by urgent.news from Berkeley AI Research's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
