Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon
arXiv:2605.09708v2 Announce Type: replace Abstract: We present Metal-Sci, a 10-task benchmark of scientific Apple Silicon Metal compute kernels spanning six optimization regimes (stencils, all-pairs in $n$-body problems, multi-field Boltzmann, neighbor-list molecular dynamics, multi-kernel PDE, FFT). Each task ships a CPU reference, a roofline-anchored fitness function, and a held-out generalization size. We pair the benchmark with a lightweight harness for automatic kernel search that runtime-compiles each candidate, scores it against the roofline across multiple sizes, and feeds structured c
The increasing focus on custom silicon for AI, particularly smaller, power-efficient chips like Apple's Metal, necessitates specialized benchmarks and optimization tools to maximize performance.
This benchmark facilitates targeted AI kernel development and optimization directly on Apple Silicon, potentially unlocking significant performance gains for localized AI applications and extending the capabilities of on-device AI.
The availability of a robust, roofline-anchored benchmark and an automated search harness for Apple Silicon introduces a more structured and efficient pathway for evolutionary LLM kernel optimization on this platform.
- · Apple
- · AI developers targeting Apple Silicon
- · On-device AI applications
- · Hardware-software co-design methodologies
- · Generic deep learning benchmarks
- · Less optimized AI model deployments
- · Cloud-dependent AI architectures for certain use cases
Improved performance and efficiency for large language models and other AI workloads running natively on Apple Silicon.
Accelerated innovation in efficient AI kernel design, potentially leading to new best practices portable to other custom silicon architectures.
Enhanced competitive advantage for platforms that can run sophisticated AI locally, reducing reliance on centralized cloud compute for certain tasks.
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Read at arXiv cs.LG