Speaker
Description
An overview of recent advances in tensor network state (TNS) methods are
presented that have the potential to broaden their scope of application
radically for strongly correlated quantum many body systems. Novel
mathematical models for hybrid multiNode-multiGPU parallelization on
high-performance computing (HPC) infrastructures will be discussed.
Scaling analysis on NVIDIA DGX-A100 and DXG-H100 platforms reaching
quarter petaflops performance on a single node will also be presented.
Finally, we discuss cutting edge performance results via mixed precision
spin adapted ab initio Density Matrix Renormalization Group (DMRG)
electronic structure calculations utilizing the Ozaki scheme for emulating
FP64 arithmetic using 8-bit integer logic. By approximating the underlying
matrix and tensor algebra via finite number of INT8 slices we demonstrate
for chemical benchmark systems that chemical accuracy can be reached even
with mixed precision arithmetic. We also show that due to its variational
nature, DMRG provides an ideal tool to benchmark accuracy domains and
performance of new hardware developments and related numerical libraries.
Detailed numerical error analysis and performance assessment are presented
also for subcomponents of the DMRG algebra by interpolating systematically
between double and single precision. Our analysis paves the way for
utilization of state-of-the-art Blackwell technology in tree-like tensor
network state calculations opening new research directions in material
sciences and beyond.
