6 papers
Accelerating Density Fitting with Adaptive-precision and 8-bit Integer on AI Accelerators
Hua Huang, Wenkai Shao, Jeff Hammond
The emergence of artificial intelligence (AI) accelerators like NVIDIA Tensor Cores offers new opportunities to speed up tensor-heavy scientific computations. However, applying the…
Hunting for quantum advantage in electronic structure calculations is a highly non-trivial task
Ãrs Legeza, Andor Menczer, Miklós Antal Werner +7
In light of major developments over the past decades in both quantum computing and simulations on classical hardware, it is a serious challenge to identify a real-world problem whe…
Efficient Coupled-Cluster Python Frameworks for Next-Generation GPUs: A Comparative Study of CuPy and PyTorch on the Hopper and Grace Hopper Architecture
Antonina Dobrowolska, Julian ÅwierczyÅski, PaweÅ Tecmer +6
In this work, we introduce new batching algorithms to effectively handle large contractions encountered in coupled-cluster singles and doubles (CCSD) implementations in Python on t…
Tensor Algebra Processing Primitives (TAPP): Towards a Standard for Tensor Operations
Jan Brandejs, Niklas Hörnblad, Edward F. Valeev +4
To address the absence of a universal standard interface for tensor operations, we introduce the Tensor Algebra Processing Primitives (TAPP), a C-based interface designed to decoup…
Mixed-precision ab initio tensor network state methods adapted for NVIDIA Blackwell technology via emulated FP64 arithmetic
Cole Brower, Samuel Rodriguez Bernabeu, Jeff Hammond +5
We report cutting-edge performance results via mixed-precision spin adapted ab initio Density Matrix Renormalization Group (DMRG) electronic structure calculations utilizing the Oz…
Orbital optimization of large active spaces via AI-accelerators
Ãrs Legeza, Andor Menczer, Ãdám Ganyecz +6
We present an efficient orbital optimization procedure that combines the highly GPU accelerated, spin-adapted density matrix renormalization group (DMRG) method with the complete a…