New techniques for error-independent unified path variation, non-degenerate batched sampling, and flexible contraction accelerate tensor network quantum trajectory simulations by more than 10^8 times.
Legate numpy: Accelerated and distributed array computing
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
PICO is a benchmarking framework for collective operations that decouples portable setup from platform execution, supplies reference MPI implementations, and shows default choices can be up to 5x slower with up to 44% end-to-end training time reductions in simulator replays.
CoVer extended to Fortran preserves analysis accuracy, reveals a bug in MPI-BugBench, and runs substantially faster than MUST while supporting multiple languages.
citing papers explorer
-
Accelerating Quantum Tensor Network Simulations with Unified Path Variations and Non-Degenerate Batched Sampling
New techniques for error-independent unified path variation, non-degenerate batched sampling, and flexible contraction accelerate tensor network quantum trajectory simulations by more than 10^8 times.
-
PICO: Performance Insights for Collective Operations
PICO is a benchmarking framework for collective operations that decouples portable setup from platform execution, supplies reference MPI implementations, and shows default choices can be up to 5x slower with up to 44% end-to-end training time reductions in simulator replays.
-
Extending Contract Verification for Parallel Programming Models to Fortran
CoVer extended to Fortran preserves analysis accuracy, reveals a bug in MPI-BugBench, and runs substantially faster than MUST while supporting multiple languages.