Author

Shriram Chandran

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Preprint Oct 2026

What Bloats Your Floats? Right-Sizing Numerical Precision for Scientific Computing

While FP64 (binary64) remains the standard representation of real numbers in scientific computing, hardware trends increasingly favor low-precision formats optimized for AI workloads. This shift prioritizes throughput and energy efficiency over full-precision capabilities. We challenge the necessity of high-precision a...

Pratyai Mazumder, A. Calotoiu, Marcin Copik et al. · 0 citations

Preprint Sep 2026

Extreme-Scale Linear-Scaling Kohn-Sham DFT at 100 Million Atoms: Bridging Quantum Simulations and Experiments

Kohn-Sham density functional theory (DFT) remains the workhorse of ab initio materials simulation, yet cubic computational and quadratic memory scaling have confined calculations to a few hundred to thousands of atoms, spanning only nanometers, far below experimentally relevant length scales. We introduce XLSDFT, a lin...

Qi-Men Xu, Yu Zhang, Di-Xing Ni et al. · 0 citations

#machine learning Preprint Sep 2026

Hardware-Aware Features for CUTLASS Kernel Selection

A hardware-aware representation for CUTLASS kernel selection is introduced that augments candidate configurations with statically computable estimates of induced hardware behavior, showing that explicitly representing candidate-induced hardware behavior provides a useful inductive bias for learned kernel selection.

Shriram Chandran, Dominic Rinderer, Yakup Budanaz et al. · 0 citations