In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here.

A parallel algorithm for unilateral contact problems 

A multidisciplinary team of Spanish researchers from the Barcelona Supercomputing Center, the Technical University of Catalonia, and the International Center for Numerical Methods in Engineering developed a parallel contact algorithm designed for high performance computing with a specific focus on its “computational implementation in a multiphysics finite element code.” Researchers based the algorithm on “the method of partial Dirichlet-Neumann boundary conditions.” It can “solve numerically a nonlinear contact problem between rigid and deformable bodies in a whole parallel framework.” Spanish researchers validated the algorithm by conducting benchmark tests and comparing the “proposed solution against theoretical and other numerical solutions.” For the benchmark tests, researchers used the MareNostrum4 supercomputer at BSC to conduct the simulations. They also “evaluated the parallel performance of the proposed algorithm in a real impact test to show its capabilities for large-scale applications.”

Authors: G. Guillameta, M. Riveroa, M. Zavala-Ake´, M. Vazquez, G. Houzeauxa, and S. Ollerb

Tensor network quantum virtual machine for simulating quantum circuits at Exascale

In this paper from a team of researchers from the Oak Ridge National Laboratory and Nvidia Corp., the authors introduce “a general tensor network based quantum circuit simulator capable of modeling both ideal and noisy quantum circuits as well as computing various experimentally accessible properties depending on the tensor network formalism used.” The new Tensor Network Quantum Virtual Machine (TNQVM) “serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework.” Researchers based the version “on the scalable tensor network processing library ExaTN (Exascale Tensor Networks).” The paper details the initial benchmarks of the “framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.”

Authors: Thien Nguyen, Dmitry Lyakh, Eugene Dumitrescu, David Clark, Jeff Larkin, and Alexander McCaskey

Optimized SWAP networks with equivalent circuit averaging for QAOA 

A multidisciplinary team of researchers from the University of California at Berkeley, the Lawrence Berkeley National Lab in California, Super.tech, a division of ColdQuanta in Illinois, and the University of Chicago, present two techniques to streamline the execution of SWAP networks for Quantum Approximate Optimization Algorithm (QAOA). A SWAP network is a qubit routing sequence used to executive the QAOA efficiently. The researcher’s “techniques are experimentally validated at the Advanced Quantum Testbed through the execution of QAOA circuits for finding the ground state of two- and four-node Sherrington-Kirkpatrick spin-glass models with various randomly sampled parameters.” Results showed “a ∼60% average reduction in error (total variation distance) for QAOA of depth p = 1 on four transmon qubits on a superconducting quantum processor.”

Authors: Akel Hashim, Rich Rines, Victory Omole, Ravi K. Naik, John M.Kreikebaum, David I. Santiago, Frederic T. Chong, Irfan Siddiqi, and Pranav Gokhale

Design and implementation of ShenWei Universal C/C++ 

Chinese researchers from Tsinghua University introduce ShenWei Universal C/C++(SWUC), which “ is a language extension for C/C++ [developed] to better support heterogeneous programming on ShenWei many-core processors.” The ShenWei many-core series processors (which now have SW26010 and SW26010pro) provide the necessary computing power the Sunway supercomputer needs. The language reduces the engineer’s efforts and SWUC “enables fluent programming across the boundary of Management Processing Element (MPE) and Compute Processing Element (CPE).” Researchers demonstrate that “through the use of several new attributes and compiler directives, users are able to write codes running on MPE and CPE in a single file.” In addition, SWUC enables the use of “Athread library interfaces available, easing the learning curve for original ShenWei users.”

Authors: Huanqi Cao and Jiajie Chen

Stable parallel training of Wasserstein Conditional Generative Adversarial Neural Networks

Researchers from the Oak Ridge National Laboratory in Tennessee develop a “stable, parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs) under the constraint of a fixed computational budget.” Their proposed approach “avoids inter-process communications, reduces the risk of mode collapse and enhances scalability by using multiple generators, each one of them concurrently trained on a single data label.” Numerical experiments and scalability tests were performed on the Summit supercomputer at the Oak Ridge Leadership Computing Facility. The researchers’ “use of the Wasserstein metric also reduces the risk of cycling by stabilizing the training of each generator.” Using the CIFAR10, CIFAR100, and ImageNet1k standard benchmark image datasets, the researchers were able to retain the “original resolution of the images for each dataset.

Authors: Massimiliano Lupo Pasini and Junqi Yin

Next generation computational tools for the modeling and design of particle accelerators at exascale

In this paper, researchers from Lawrence Berkeley National Laboratory (LBNL) detail three computation tools used for “the modeling and design of particle accelerators, readying codes up for next generation machines in the Exascale era.” First is the open source software toolkit Beam pLasma Accelerator Simulation Toolkit (BLAST) developed by LBNL researchers, which “provides modeling tools to model hybrid accelerators, containing both plasma and conventional beamline elements.” Second, ABLASTR “is a modern C++17 library used to share particle-in-cell routines between simulation codes.” Lastly, ImpactX was “developed to succeed IMPACT-Z as a new, s-based beam dynamics code with intrinsic GPU, mesh-refinement and tight coupling to time based codes and AI/ML capabilities.” Still in its early stages, ImpactX can already “model significantly larger particle ensembles than its predecessor codes,” the researchers conclude. Further developments are planned.

Authors: Axel Huebl, Remi Lehe, Chad E. Mitchell, Ji Qiang, Robert D. Ryne, Ryan T. Sandberg, and Jean-Luc Vay

Quantum algorithm implementations for beginners

Los Alamos National Laboratory researchers “review the principles of quantum programming, which are quite different from classical programming, with straightforward algebra that makes understanding of the underlying fascinating quantum mechanical principles optional.” In this paper, the authors provide an “introduction to quantum computing algorithms and their implementation on real quantum hardware.” They summarize 20 quantum algorithms with an overview on how to implement on IBM’s quantum computer, and then they examine the results of the “implementation with respect to differences between the simulator and the actual hardware runs.” The code is publicly available on GitHub at https://github.com/lanl/quantum_algorithms.

Authors: Abhijith J., Adetokunbo Adedoyin, John Ambrosiano, Petr Anisimov, William Casper, Gopinath Chennupati, Carleton Coffrin, Hristo Djidjev, David Gunter, Satish Karra, Nathan Lemons, Shizeng Lin, Alexander Malyzhenkov, David Mascarenas, Susan Mniszewski, Balu Nadiga, Daniel O’malley, Diane Oyen, Scott Pakin, Lakshman Prasad, Randy Roberts, Phillip Romero, Nandakishore Santhi, Nikolai Sinitsyn, Pieter J. Swart, James G. Wendelberger, Boram Yoon, Richard Zamora, Wei Zhu, Stephan Eidenbenz, Andreas Bärtschi, Patrick J. Coles, Marc Vuffray, and Andrey Y. Lokhov 

Do you know about research that should be included in next month’s list? If so, send us an email at [email protected]. We look forward to hearing from you.