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Matrix Is All You Need: Rearchitecting Quantum Chemistry to Scale on AI Accelerators
DescriptionScientific computing remains misaligned with the execution paradigm of modern AI accelerators, which favor structured, low-precision matrix operations. Quantum chemistry exemplifies this gap, with irregular computations, fragmented utilization, and limited support for high-complexity systems.

We present Mako, a matrix-centric system that rearchitects quantum chemistry to scale on AI accelerators. Mako comprises three components: KernelMako reformulates ERI evaluation into composable MatMul pipelines using CUTLASS; QuantMako introduces physics-informed quantization to exploit low-precision potential; and CompilerMako automates kernel fusion and architecture-tuned specialization.

Mako achieves up to ~20× speedup on high-angular-momentum basis sets. It sustains over 90% parallel efficiency on a single node and 70% across 64 GPUs, completing the accurate simulation of ubiquitin (1,231 atoms, def2-TZVP) from days to just 58 minutes. Mako demonstrates how scientific workloads can be restructured to inherit the scalability of deep learning—repurposing AI accelerators and their ecosystems to scale quantum chemistry beyond traditional limits.