Dmitry I. Lyakh

Orcid: 0000-0002-1851-2974

Affiliations:
  • NVIDIA Corp, Santa Clara, CA, USA
  • Oak Ridge National Laboratory, Oak Ridge, TN, USA


According to our database1, Dmitry I. Lyakh authored at least 16 papers between 2015 and 2023.

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Bibliography

2023
cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

2022
ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale.
Frontiers Appl. Math. Stat., 2022

2021
QuaSiMo: A composable library to program hybrid workflows for quantum simulation.
IET Quantum Commun., December, 2021

Quantum Computers for High-Performance Computing.
IEEE Micro, 2021

Quantum Circuit Transformations with a Multi-Level Intermediate Representation Compiler.
CoRR, 2021

Composable Programming of Hybrid Workflows for Quantum Simulation.
Proceedings of the 18th IEEE International Conference on Software Architecture Companion, 2021

2020
Pre-exascale accelerated application development: The ORNL Summit experience.
IBM J. Res. Dev., 2020

Really Embedding Domain-Specific Languages into C++.
CoRR, 2020

2019
XACC: A System-Level Software Infrastructure for Heterogeneous Quantum-Classical Computing.
CoRR, 2019

Establishing the Quantum Supremacy Frontier with a 281 Pflop/s Simulation.
CoRR, 2019

2018
A language and hardware independent approach to quantum-classical computing.
SoftwareX, 2018

Hybrid Programming for Near-Term Quantum Computing Systems.
Proceedings of the 2018 IEEE International Conference on Rebooting Computing, 2018

2017
Massively parallel and linear-scaling algorithm for second-order Møller-Plesset perturbation theory applied to the study of supramolecular wires.
Comput. Phys. Commun., 2017

cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs.
CoRR, 2017

Aces4: A Platform for Computational Chemistry Calculations with Extremely Large Block-Sparse Arrays.
Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium, 2017

2015
An efficient tensor transpose algorithm for multicore CPU, Intel Xeon Phi, and NVidia Tesla GPU.
Comput. Phys. Commun., 2015


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