HIGH PERFORMANCE COMPUTING AND SIMULATIONS

Deterministic particle simulation algorithms

• Fast multipole method: multiresolution in space
  • A fast algorithm for particle simulations, L. Greengard and V. Rokhlin, J. Comput. Phys. 73, 325 (1987)
  • Parallel multilevel preconditioned conjugate-gradient approach to variable-charge molecular dynamics, A. Nakano, Comput. Phys. Commun. 104, 59 (1997)
  • Scalable and portable implementation of the fast multipole method on parallel computers, S. Ogata, et al., Comput. Phys. Commun. 153, 445 (2003), source code available at the CPC Program Library
  • A massively parallel adaptive fast multipole method on heterogeneous architectures, I. Lashuk, et al., Commun. ACM 55, 101 (2012)
  • 2HOT: an improved parallel hashed oct-tree N-body algorithm for cosmological simulation, M. S. Warren, in Proc. of Supercomputing (SC13) (ACM/IEEE, 2013)
  • Comparison of scalable fast methods for long-range interactions, A. Arnold, et al., Phys. Rev. E 88, 063308 (2013)
  • Multilevel summation with B-spline interpolation for pairwise interactions in molecular dynamics simulations, D. J. Hardy, et al., J. Chem. Phys. 144, 114112 (2016)
  • Occupied-orbital fast multipole method for efficient exact exchange evaluation, H.-A. Le and T. Shiozaki, J. Chem. Theory Comput. 14, 1228 (2018)
  • A GPU-accelerated fast multipole method for GROMACS: performance and accuracy, B. Kohnke, et al., J. Chem. Theory Comput. 16, 6938 (2020)
  • FFT, FMM, and multigrid on the road to exascale: performance challenges and opportunities, H. Ibeid, et al., J. Par. Distrib. Comput. 136, 63 (2020)
  • ExaFMM: a high-performance fast multipole method library with C++ and Python interfaces, T. Wang, et al., J. Open Source Software 6, 3145 (2021)
  • Quantum algorithm for dense and full-rank kernels using hierarchical matrices, Q. T. Nguyen, et al., arXiv, 2201.11329 (2022)
• Multiple time stepping: multiresolution in time
  • Reversible multiscale molecular dynamics, M. Tuckerman, B. J. Berne, and G. J. Martyna, J. Chem. Phys. 97, 1990 (1992)
  • Multiresolution molecular dynamics algorithm for realistic materials modeling on parallel computers, A. Nakano, R. K. Kalia, and P. Vashishta, Comput. Phys. Commun. 83, 197 (1994)
  • Fuzzy clustering approach to hierarchical molecular dynamics simulation of multiscale materials phenomena, A. Nakano, Comput. Phys. Commun. 105, 139 (1997)
  • A massively space-time parallel N-body solver, R. Speck, et al., in Proc. of Supercomputing (SC12) (IEEE/ACM, 2012)
  • Speeding-up ab initio molecular dynamics with hybrid functionals using adaptively compressed exchange operator based multiple timestepping, S. Mandal and N. N. Nair, J. Chem. Phys. 151, 151102 (2019)

Parallel computing frameworks

• Big picture
  • The landscape of parallel computing research: a view from Berkeley, K. Asanovic, et al., UC Berkeley Tech. Rep. (2006)
  • The promise and perils of the coming multicore revolution and its impact, J. J. Dongarra, ed., CT Watch Quarterly 3(1) (Feb., 2007)
  • Exascale computing and big data, D. A. Reed and J. Dongarra, Commun. ACM 58(7), 56 (2015)
  • Design for U.S. exascale computer takes shape, R. F. Service, Science 359, 617 (2018)
  • Compute Cambrian explosion of computing and big data in the post-Moore era, S. Matsuoka, Proc. of High-Perf. Par. Distrib. Comp. (HPDC), 105 (ACM, 2018); Compute Cambrian explosion, T. Coughlin, Forbes, Apr. 26 (2019)
• Parallel computing basics and parallel molecular dynamics
  • Fast parallel algorithms for short-range molecular dynamics, S. Plimpton, J. Comput. Phys. 117, 1 (1995)
  • NAMD2: greater scalability for parallel molecular dynamics, L. V. Kale, et al., J. Comput. Phys. 151, 283 (1999); NAMD class hierarchy; NAMD files
  • Hybrid message-passing and shared-memory programming in a molecular dynamics application on multicore clusters, M. J. Chorley, et al., Int'l J. High Performance Comput. Appl. 23, 196 (2009)
  • Efficient parallel implementation of molecular dynamics with embedded atom method on multicore platforms, C. Hu, et al., in Proc. of Int'l Conf. on Parallel Processing (IEEE, 2009)
  • Extending the generality of molecular dynamics simulations on a special-purpose machine, D. P. Scarpazza, et al., in Proc. of Int'l Parallel & Distributed Processing Symp. (IPDPS 2013) (IEEE, 2013); Millisecond-scale molecular dynamics simulations on Anton, D. E. Shaw, et al., in Proc. of Supercomputing (SC09) (ACM/IEEE, 2009); A fast, scalable method for the parallel evaluation of distance-limited pairwise particle interactions D. E. Shaw, J. Comput. Chem. 26, 1318 (2005)
  • Analysis of scalable data-privatization threading algorithms for hybrid MPI/OpenMP parallelization of molecular dynamics, M. Kunaseth, et al., J. Supercomput. 66, 406 (2013)
  • Performance characteristics of hardware transactional memory for molecular dynamics application on BlueGene/Q: toward efficient multithreading strategies for large-scale scientific applications, M. Kunaseth, et al., in Proc. of Int'l Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC-13) (IEEE, 2013)
  • A scalable parallel algorithm for dynamic range-limited n-tuple computation in many-body molecular dynamics simulation, M. Kunaseth, et al., in Proc. of Supercomputing (SC13) (ACM/IEEE, 2013)
  • Metascalable quantum molecular dynamics simulations of hydrogen-on-demand, K. Nomura, et al., in Proc. of Supercomputing (SC14) (IEEE/ACM, 2014)
  • Kokkos: enabling manycore performance portability through polymorphic memory access patterns, H. C. Edwards, et al., J. Par. Distrib. Comput. 74, 3202 (2014)
  • GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers, M. J. Abraham, et al., SoftwareX 1-2, 19 (2015)
  • Order-invariant real number summation: circumventing accuracy loss for multimillion summands on multiple parallel architectures, P. E. Small, et al., in Proc. of Int'l Parallel & Distributed Processing Symp. (IPDPS 2016) (IEEE, 2016)
  • Redesigning LAMMPS for peta-scale and hundred-billion-atom simulation on Sunway TaihuLight, X. Duan, et al., in Proc. of Supercomputing (SC18) (IEEE/ACM, 2018)
  • Shift-collapse acceleration of generalized polarizable reactive molecular dynamics for machine learning-assisted computational synthesis of layered materials, K. Liu, et al., in Proc. of ScalA18, p. 41 (IEEE/ACM, 2018)
  • Shift/collapse on neighbor list (SC-NBL): fast evaluation of dynamic many-body potentials in molecular dynamics simulations, M. Kunaseth, et al., Comput. Phys. Commun. 235, 88 (2019)
  • SW_GROMACS: Accelerate GROMACS on Sunway TaihuLight, T. Zhang, et al., in Proc. of Supercomputing (SC19) (ACM/IEEE, 2019)
  • Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning, W. Jia, et al., in Proc. of Supercomputing (SC20) (IEEE/ACM, 2020)--Liqiu (10/21)
  • Billion atom molecular dynamics simulations of carbon at extreme conditions and experimental time and length scales, K. Nguyen-Cong, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)--Emily (11/7)
• Divide-conquer-"recombine" parallelism
  • A divide-and-conquer/cellular-decomposition framework for million-to-billion atom simulations of chemical reactions, A. Nakano, et al., Comput. Mater. Sci. 38, 642 (2007)
  • De novo ultrascale atomistic simulations on high-end parallel supercomputers, A. Nakano, et al., Int'l J. High Performance Comput. Appl. 22, 113 (2008)
  • A metascalable computing framework for large spatiotemporal-scale atomistic simulations, K. Nomura, et al., in Proc. of Int'l Parallel & Distributed Processing Symp. (IPDPS 2009) (IEEE, 2009)
  • Nanoscopic mechanisms of singlet fission in amorphous molecular solid, W. Mou, et al., Appl. Phys. Lett. 102, 173301 (2013)
  • A divide-conquer-recombine algorithmic paradigm for large spatiotemporal quantum molecular dynamics simulations, F. Shimojo, et al., J. Chem. Phys. 140, 18A529 (2014)
  • Quantum molecular dynamics in the post-petaflop/s era, N. A. Romero, et al., IEEE Computer 48(11), 33 (2015)
• Load balancing
  • Performance of dynamic load balancing algorithms for unstructured mesh calculations, R. D. Williams, Concurrency: Practice and Experience 3, 457 (1991)
  • Provably good partitioning and load balancing algorithms for parallel adaptive N-body simulation, S.-H. Teng, SIAM J. Sci. Comput. 19, 635 (1998)
  • A fast and high quality multilevel scheme for partitioning irregular graphs, G. Karypis and V. Kumar, SIAM J. Sci. Comput. 20, 359 (1998)
  • Multiresolution load balancing in curved space: the wavelet representation, A. Nakano, Concurrency: Practice and Experience 11, 343 (1999)
  • New challenges in dynamic load balancing, K. D. Devine, et al., Applied Numerical Mathematics 52, 133 (2005)
  • Hypergraph-based dynamic load balancing for adaptive scientific computations, U. V. Catalyurek, et al., in Proc. of Int'l Parallel & Distributed Processing Symp. (IPDPS 2007) (IEEE, 2007)
  • A repartitioning hypergraph model for dynamic load balancing, U. V. Catalyurek, et al., J. Parallel Distrib. Comput. 69, 711 (2009)
  • Load balancing N-body simulations with highly non-uniform density, O. Pearce, et al., in Proc. of Int'l Conf. on Supercomputing (ICS'14) (ACM, 2014)--Aoyan (11/21)
• Optimizing parallel MD
  • Improving memory hierarchy performance for irregular applications, J. Mellor-Crummey, D. Whalley, and K. Kennedy, in Proc. of Int'l Conf. on Supercomputing (ACM, 1999); Improving memory hierarchy performance for irregular applications using data and computation reorderings, Int'l J. Parallel Prog. 29, 217 (2001)
  • Cache-oblivious algorithms, M. Frigo, et al., in Proc. of Symp. on Foundation of Computer Science (FOCS) (IEEE, 1999)
  • Analysis of the clustering properties of the Hilbert space-filling curve, B. Moon, et al., IEEE Trans. Knowledge Data Eng. 13, 124 (2001)
  • Metrics and models for reordering transformations, M. M. Strout and P. D. Hovland, in Proc. of Workshop on Memory System Performance (ACM, 2004)
  • Recursive blocked algorithms and hybrid data structures for dense matrix library software, E. Elmroth, et al., SIAM Rev. 46, 3 (2004)
  • Roofline: an insightful visual performance model for multicore architectures, S. Williams, et al., Commun. ACM 52, 65 (2009)
  • Performance modeling, analysis, and optimization of cell-list based molecular dynamics, M. Kunaseth, et al., in Proc. of Int'l Conf. on Scientific Comp. (CSC'10) (2010)
  • Exploiting hierarchical parallelisms for molecular dynamics simulation on multicore clusters, L. Peng, et al., J. Supercomput. 57, 20 (2011)
  • Hierarchical parallelization and optimization of high-order stencil computations on multicore clusters, H. Dursun, et al., J. Supercomput. 62, 946 (2012)
  • On using the roofline model with lower bounds on data movement, V. Elango, et al., ACM. T. Arch. Code Opt. 11, 67 (2015)
  • Mixed data layout kernels for vectorized complex arithmetic, D. T. Popovici, et al., in Proc. of HPEC (IEEE, 2017)
  • Practical implementation of lattice QCD simulation on SIMD machines with Intel AVX-512, I. Kanamori, et al., in Proc. of ICCSA (2018)
• New architectures
  • A programming example: large FFT on the cell broadband engine, A. C. Chow, et al., IBM Tech. Rep. (2005)
  • A rough guide to scientific computing on the Playstation3, A. Buttari, et all., Univ. of Tennessee, Knoxville Technical report (2007)
  • Accelerating molecular modeling applications with graphics processors, J. E. Stone, et al., J. Comput. Chem. 28, 2618 (2007)
  • Parallel lattice Boltzmann flow simulation on a low-cost PlayStation3 cluster, K. Nomura, et al., Int'l J. Comput. Sci. 2, 437 (2008)
  • Harvesting graphics power for MD simulations, J. A. van Meel, et al., Mol. Sim. 34, 259 (2008)
  • An MPI performance monitoring interface for Cell based compute nodes, H. Dursun, et al., Parallel Processing Lett. 19, 535 (2009)
  • A massively parallel adaptive fast-multipole method on heterogeneous architectures, I. Lashuk, et al., in Proc. of Supercomputing (SC09) (ACM/IEEE, 2009)
  • Dynamic load balancing on single- and multi-GPU systems, L. Chen, et al., in Proc. of Int'l Parallel & Distributed Processing Symp. (IPDPS 2010) (IEEE, 2010)
  • Preliminary investigation of optimizing molecular dynamics simulation on Godson-T many-core processor, L. Peng, et al., in Proc. of Workshop on Unconventional High Performance Comp. (2010)
  • Enhanced molecular dynamics performance with a programmable graphics processor, D. C. Rapaport, Comput. Phys. Commun. 182, 926 (2011)
  • Exploring SIMD for molecular dynamics, using Intel Xeon processors and Intel Xeon Phi coprocessors, S. J. Pennycook, et al., in Proc. of Int'l Parallel & Distributed Processing Symp. (IPDPS 2013) (IEEE, 2013)
  • Scalability study of molecular dynamics simulation on Godson-T many-core architecture, L. Peng, et al., J. Par. Distrib. Comput. 73, 1469 (2013)
  • PuReMD-GPU: a reactive molecular dynamics simulation package for GPUs, S. B. Kylasa, et al., J. Comput. Phys. 272, 343 (2014)
  • Knights Landing (KNL): 2nd generation Intel Xeon Phi processor, A. Sodani, et al., Hot Chips (IEEE/ACM, 2015)
  • Optimizing non-contiguous memory access on Intel Xeon Phi coprocessors, M. Ma, et al., in Proc. of Int'l Conf. High Perform. Comput. Commun. (HPCC) (IEEE, 2015)
  • Strong scaling of general-purpose molecular dynamics simulations on GPUs, J. Glaser, et al., Comput. Phys. Commun. 192, 97 (2015)
  • The Sunway TaihuLight supercomputer: system and applications, H. Fu, et al., Sci. China Inf. Sci. 59, 072001 (2016); Report on the Sunway TaihuLight system, J. Dongarra, Univ. of Tennessee Tech. Rep., UT-EECS-16-742 (2016); China inches toward the exascale, R. Courtland, IEEE Spectrum, 53(8), 14 (2016)
  • MPI-ACC: accelerator-aware MPI for scientific applications, A. M. Aji, et al., IEEE T. Par. Distrib. Sys. 27, 1401 (2016); Evolving MPI+X toward exascale, D. A. Bader, IEEE Computer 49(8), 10 (2016); MPI+X, M. Wolfe, HPC Wire (2014); MPI+MPI, T. Hoefler et al., Computing 95, 1121 (2013)
  • Breadth first search vectorization on the Intel Xeon Phi, M. Paredes, et al., in Proc. of Int'l Conf. Computing Frontiers (CF) (ACM, 2016)
  • A GPU-accelerated machine learning framework for molecular simulation: Hoomd-blue with TensorFlow, R. Barrett, et al., ChemrXiv, 8019527 (2019)
  • Towards artificial general intelligence with hybrid Tianjic chip architecture, J. Pei, et al., Nature 572, 106 (2019)
  • GPU acceleration of extreme scale pseudo-spectral simulations of turbulence using asynchronism, K. Ravikumar, et al., in Proc. of Supercomputing (SC19) (ACM/IEEE, 2019)
  • Accelerating large-scale excited-state GW calculations on leadership HPC systems, M. Del Ben, et al., in Proc. of Supercomputing (SC20) (IEEE/ACM, 2020)
  • Scalable molecular dynamics on CPU and GPU architectures with NAMD, J. C. Phillips, et al., J. Chem. Phys. 153, 044130 (2020)
  • Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS, S. Pall, et al., J. Chem. Phys. 153, 134110 (2020)
  • Assessment of NVSHMEM for high performance computing, C. H. Hsu, et al., Int. J. Network Comput. 11, 78 (2021)--Raghav (11/16)
  • GPU acceleration of large-scale full-frequency GW calculations, V. W. Yu, et al., J. Chem. Theory Comput. 18, 4690 (2022)

Deterministic continuum simulation algorithms

• Multiresolution numerical methods
  • Massively parallel algorithms for computational nanoelectronics based on quantum molecular dynamics, A. Nakano, R. K. Kalia, and P. Vashishta, Comput. Phys. Commun. 83, 181 (1994)
  • Wavelets for computer graphics: a primer, E. J. Stollnitz, et al., IEEE Computer Graphcs Appl. 15(3), 76 (1995)
  • Embedded divide-and-conquer algorithm on hierarchical real-space grids: parallel molecular dynamics simulation based on linear-scaling density functional theory, F. Shimojo, et al., Comput. Phys. Commun. 167, 151 (2005)
  • Autotuning multigrid with PetaBricks, C. Chan, et al., in Proc. of Supercomputing (SC09) (ACM/IEEE, 2009)
  • Parallel geometric-algebraic multigrid on unstructured forests of octrees, H. Sundar, in Proc. of Supercomputing (SC12) (IEEE/ACM, 2012)
  • Distributed multigrid neural solvers on megavoxel domains, A. Balu, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)
• Continuum simulations and parallel implementation
  • The density matrix renormalization group in quantum chemistry, G. K.-L. Chan and S. Sharma, Annu. Rev. Phys. Chem. 62, 465 (2011)
  • Graph-based linear scaling electronic structure theory, A. M. N. Niklasson, et al., J. Chem. Phys. 144, 234101 (2016)
  • QXMD: An open-source program for nonadiabatic quantum molecular dynamics, F. Shimojo, et al., SoftwareX 10, 100307 (2019)
  • Parallel transport time-dependent density functional theory calculations with hybrid functional on Summit, W. Jia, et al., in Proc. of Supercomputing (SC19) (ACM/IEEE, 2019)
  • OpenFPCI: A parallel fluid–structure interaction framework, S. Hewitt, et al., Comput. Phys. Commun. 244, 469 (2019)
  • A 400 trillion-grid Vlasov simulation on Fugaku supercomputer: large-scale distribution of cosmic relic neutrinos in a six-dimensional phase space, K. Yoshikawa, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)
  • Quantum-inspired method for solving the Vlasov-Poisson equations, E. Ye and N. F. G. Loureiro, Phys. Rev. E 106, 035208 (2022)--Kevin and Ziyu (11/18)
  • 2.5 million-atom ab initio electronic-structure simulation of complex metallic heterostructures with DGDFT, W. Hu, et al., in Proc. of Supercomputing (SC22) (IEEE/ACM, 2022)--Taufeq (11/21)

Hybrid particle/continuum simulation methods

• Multiscale simulation methods
  • Linear-scaling relaxation of the atomic positions in nanostructures, S. Goedecker, et al., Phys. Rev. B 64, 161102(R) (2001)
  • Hybrid finite-element/molecular-dynamics/electronic-density-functional approach to materials simulations on parallel computers, S. Ogata, et al., Comput. Phys. Commun. 138, 143 (2001)
  • Equation-free: the computer-aided analysis of complex multiscale systems, I. G. Kevrekidis, C. W. Gear, and G. Hummer, AlChE. J. 50, 1346 (2004)
  • Learning on the fly: a hybrid classical and quantum-mechanical molecular dynamics simulation, G. Csanyi, et al., Phys. Rev. Lett. 93, 175503 (2004)--Goran and Madhubani (10/21)
  • Multiscale modeling of the dynamics of solids at finite temperature, X. Li and W. E, J. Mech. Phys. Solids 53, 1650 (2005)
  • A python approach to multi-code simulations: CHIMPS, J. U. Schlutter, et al., Annual Research Briefs of the Center for Turbulence Research (Stanford Univ., 2005)
  • Generalized mathematical homogenization of atomistic media at finite temperatures in three dimensions, J. Fish, W. Chen, and R. Li, Comput. Meth. Appl. Mech. Eng. 196, 908 (2007)
  • Equation of motion for coarse-grained simulation based on microscopic description, T. Kinjo and S. Hyodo, Phys. Rev. E 75, 051109 (2007)
  • A hybrid multi-loop genetic-algorithm/simplex/spatial-grid method for locating the optimum orientation of an adsorbed protein on a solid surface, T. Wei, et al., Comput. Phys. Commun. 180, 669 (2009)
  • Hybrid lattice-Boltzmann/level-set method for liquid simulation and visualization, Y. Kwak, et al., Int'l J. Comput. Sci. 3, 579 (2009)
  • Efficient ab initio modeling of random multicomponent alloys, C. Jiang and B. P. Uberuaga, Phys. Rev. Lett. 116, 105501 (2016)
  • Multiscale time-dependent density functional theory for a unified description of ultrafast dynamics: Pulsed light, electron, and lattice motions in crystalline solids, A. Yamada and K. Yabana, Phys. Rev. B 99, 245103 (2019)
  • Extreme scalability of DFT-based QM/MM MD simulations using MiMiC, V. Bolnykh, et al., J. Chem. Theory Comput. 15, 5601 (2019)
  • Intelligent resolution: integrating cryo-EM with AI-driven multi-resolution simulations to observe the SARS-CoV-2 replication-transcription machinery in action, A. Trifan, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)
  • Generalizable coordination of large multiscale workflows: challenges and learnings at scale, H. Bhatia, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)
  • Multi-scale modeling of ionic electrospray emission, J. Asher, et al., J. Appl. Phys. 131, 014902 (2022)

Scientific data visualization and analytics

• Massive dataset visualization
  • Immersive and interactive exploration of billion-atom systems, A. Sharma, et al., Presence: Teleoperators and Virtual Environments 12, 85 (2003)
  • From mesh generation to scientific visualization: an end-to-end approach to parallel supercomputing, T. Tu, et al., in Proc. of Supercomputing (SC06) (IEEE/ACM, 2006)
  • ParaViz: a spatially decomposed parallel visualization algorithm using hierarchical visibility ordering, C. Zhang, et al., Int'l J. Computat. Sci. 1, 407 (2007)
  • Next-generation visualization technologies: enabling discoveries at extreme scale, K.-L. Ma, et al., SciDAC Review 12, 12 (2009)
  • Scalable computation of streamlines on very large datasets, D. Pugmire, et al., in Proc. of Supercomputing (SC09) (ACM/IEEE, 2009)
  • Terascale data organization for discovering multivariate climatic trends, W. Kendall, et al., in Proc. of Supercomputing (SC09) (ACM/IEEE, 2009)
  • Real-time ray tracing with CUDA, M. Shih, et al., in Proc. of Int'l Conf. on Algorithms and Architectures for Parallel Processing (ICA3PP '09) (2009)
  • Multi-GPU volume rendering using MapReduce, J. A. Stuart, et al., in Proc. of Int'l Workshop on MapReduce and its Applications (MAPREDUCE 2010), Int'l Symp. on High Performance Distributed Comput. (HPDC'2010) (2010)
  • Parallel I/O, analysis, and visualization of a trillion particle simulation, S. Byna, et al., in Proc. of Supercomputing (SC12) (IEEE/ACM, 2012)
  • METAGUI. a VMD interface for analyzing metadynamics and molecular dynamics simulations, X. Biarnes, et al., Comput. Phys. Commun. 183, 203 (2012)
  • Massively parallel inverse rendering using multi-objective particle swarm optimization, K. Nagano, et al., J. Vis. 20, 195 (2017)
  • Visualizing the loss landscape of neural nets, H. Li, et al., in Proc. of Neural Info. Proc. Sys. (NeurIPS18) (2018)--Vishrut (10/26)
• Virtual reality and 3D display
  • A head-mounted three dimensional display, I. E. Sutherland, in Proc. of AFIPS, p. 757 (ACM, 1968)
  • Surround-screen projection-based virtual reality: the design and implementation of the CAVE, C. Cruz-Neira, et al., in Proc. of SIGGRAPH, p. 135 (ACM, 1993)
  • Rendering for an interactive 360° light field display, A. Jones, et al., in Proc. of SIGGRAPH (ACM, 2007)
  • An autostereoscopic projector array optimized for 3d facial display, K. Nagano, et al., in Proc. of SIGGRAPH (ACM, 2013)
  • Three-dimensional volume containing multiple two-dimensional information patterns, H. Nakayama, et al., Sci. Rep. 3, 1931 (2013)
  • In-line digital holographic microscopy using a consumer scanner, T. Shimobaba, et al., Sci. Rep. 3, 2664 (2013)
  • iBET: immersive visualization of biological electron-transfer dynamics, C. M. Nakano, et al., J. Mol. Graph. Model. 65, 94 (2016)
  • Game-Engine-Assisted Research platform for Scientific computing (GEARS) in virtual reality, B. Horton, et al., SoftwareX 9, 112 (2019)
• Scientific machine learning and big data analytics
  • Graph-based data mining, D. J. Cook and L. B. Holder, IEEE Intelligent Systems 15(2), 32 (2000)
  • Mining scientific data, N. Ramakrishnan and A. Grama, Adv. Comput. 55, 119 (2001)
  • State of the art of graph-based data mining, T. Washio and H. Motoda, ACM SIGKDD Explorations 5(1), 59 (2003)
  • Change detection in time series data using wavelet footprints, M. Sharifzadeh, F. Azmoodeh, and C. Shahabi, Lecture Notes in Computer Science, 3633, 127 (2005)
  • Map-reduce for machine learning on multicore, C. T. Chu, et al., in Proc. of Neural Information Processing Systems (NIPS) (2006)
  • Generalized neural-network representation of high-dimensional potential-energy surfaces, J. Behler & M. Parrinello, Phys. Rev. Lett. 98, 146401 (2007)
  • Towards the computational design of solid catalysts, J. K. Norskov, et al., Nature Chem. 1, 37 (2009)
  • Dynamic structure learning of factor graphs and parameter estimation of a constrained nonlinear predictive model for oilfield optimization H. Lee, et al., in Proc. of Int'l Conf. on Artificial Intelligence (ICAI'10) (Las Vegas, NV, 2010)
  • DNA sequencing via quantum mechanics and machine learning, H. Yuen, et al., Int'l J. Comput. Sci. 4, 352 (2010)
  • Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons, A. P. Bartok, et al., Phys. Rev. Lett. 104, 136403 (2010)
  • Nonlinear dimensionality reduction in molecular simulation: the diffusion map approach, A. Ferguson, et al., Chem. Phys. Lett. 509, 1 (2011)
  • The Harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community Grid, J. Hachmann, et al., J. Phys. Chem. Lett. 2, 2241 (2011)
  • Nonlinear dimensionality reduction for nonadiabatic dynamics: the influence of conical intersection topography on population transfer rates, A. M. Virshup, et al., J. Chem. Phys. 137, 22A519 (2012)
  • Using sketch-map coordinates to analyze and bias molecular dynamics simulations, G. A. Tribello, et al., Proc. Nat. Acad. Sci. 109, 5196 (2012)
  • Python Materials Genomics (pymatgen): a robust, open-source Python library for materials analysis, S. P. Ong, et al., Comput. Mater. Sci. 68, 314 (2013); Opportunities and challenges for first-principles materials design and applications to Li battery materials, G. Ceder, MRS Bulletin 35, 693 (2010)
  • Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds, A. M. Virshup, et al., J. Am. Chem. Soc. 135, 7296 (2013)
  • Amplify scientific discovery with artificial intelligence, Y. Gil, et al., Science 346, 171 (2014)
  • General multiobjective force field optimization framework, with application to reactive force fields for silicon carbide, A. Jaramillo-Botero, et al., J. Chem. Theory Comput. 10, 1426 (2014)
  • Human-level concept learning through probabilistic program induction, B. M. Lake, et al., Science 350, 1332 (2015)
  • Constructing high-dimensional neural network potentials, J. Behler, Int. J. Quant. Chem. 115, 1032 (2015)
  • Identifying structural flow defects in disordered solids using machine-learning methods, E. D. Cubuk, et al., Phys. Rev. Lett. 114, 108001 (2015)
  • Maximally informative hierarchical representations of high-dimensional data, G. Ver Steeg & A. Galstyan, in Proc. of Artificial Intelligence and Statistics (AISTATS) (2015)--Dan (11/18)
  • New opportunities for materials informatics: resources and data mining techniques for uncovering hidden relationships, A. Jain, et al., J. Mater. Res. 31, 977 (2016)
  • ZNN - a fast and scalable algorithm for training 3D convolutional networks on multi-core and many-core shared memory machines, A. Zlateski, et al., in Proc. of Int'l Parallel & Distributed Processing Symp. (IPDPS 2016) (IEEE, 2016); ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs, A. Zlateski, et al., in Proc. of Supercomputing (SC16) (ACM/IEEE, 2016)
  • Information leverage in interconnected ecosystems: overcoming the curse of dimensionality, H. Ye and G. Sugihara, Science 353, 922 (2016)
  • Probing the metabolic heterogeneity of live Euglena gracilis with stimulated Raman scattering microscopy, Y. Wakisaka, et al., Nature Microbiol. 1, 16124 (2016); see also serendipiter
  • ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost, J. S. Smith, et al., Chem. Sci., 8, 3192 (2017)
  • Accurate, large minibatch SGD: training imagenet in 1 hour, P. Goyal, et al., arXiv, 1706.02677 (2017)
  • Deep learning at 15PF, T. Kurth, et al., in Proc. of Supercomputing (SC17) (ACM/IEEE, 2017)
  • Exascale deep learning for climate analytics, T. Kurth, et al., in Proc. of Supercomputing (SC18) (IEEE/ACM, 2018)
  • Anatomy of high-performance deep learning convolutions on SIMD architectures, E. Georganas, et al., in Proc. of Supercomputing (SC18) (IEEE/ACM, 2018)
  • ImageNet training in minutes, Y. You, et al., in Proc. of International Conference on Parallel Processing (ICPP18) (ACM, 2018)
  • Multiobjective genetic training and uncertainty quantification of reactive force fields, A. Mishra, et al., npj Comput. Mater. 4, 42 (2018)
  • Active learning for accelerated design of layered materials, L. Bassman, et al., npj Comput. Mater. 4, 74 (2018)
  • Automatic chemical design using a data-driven continuous representation of molecules, R. Gomez-Bombarelli, et al., ACS Cent. Sci. 4, 268 (2018)
  • Horovod: fast and easy distributed deep learning in TensorFlow, A. Sergeev, et al., arXiv, 1802.05799 (2018)
  • MXNET-MPI: embedding MPI parallelism in parameter server task model for scaling deep learning, A. R. Mamidala, et al., arXiv, 1801.03855v1 (2018)--Saghar and Zhaorui (11/18)
  • Parallax: sparsity-aware data parallel training of deep neural networks, S. Kim, et al., in Proc. of EuroSys (2019)--Saghar and Zhaorui (11/18)
  • Structural phase transitions in a MoWSe2 monolayer: molecular dynamics simulations and variational autoencoder analysis, P. Rajak, et al., Phys. Rev. B 100, 014108 (2019)
  • Active learning of uniformly accurate interatomic potentials for materials simulation, L. Zhang, et al., Phys. Rev. Mater. 3, 023804 (2019)
  • On-the-fly Bayesian active learning of interpretable force-fields for atomistic rare events, J. Vandermause, et al., arXiv: 1904.02042v1a (2019)
  • A 20-Year Community Roadmap for Artificial Intelligence Research in the US, Y. Gill & B. Selman (CCC/AAAI, 2019)
  • A Survey on distributed machine learning, J. Verbraeken, et al., ACM Comput. Surv. 53, 30 (2020)--Saghar and Zhaorui (11/18)
  • Deep learning enabled inverse design in nanophotonics, S. So, et al., Nanophoton. 9, 1041 (2020)--Ho-Chun (11/14)
  • SE(3)-transformers: 3D roto-translation equivariant attention networks, F. B. Fuchs, et al., in Proc. of Neural Info. Proc. Sys. (NeurIPS20) (2020)--Jordy (11/16)
  • Efficient large-scale language model training on GPU clusters using Megatron-LM, D. Narayanan, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)
  • Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials, P. Rajak, et al., npJ Comput. Mater. 7, 102 (2021)
  • Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials, P. Rajak, et al., npJ Comput. Mater. 7, 108 (2021)
  • Dielectric polymer property prediction using recurrent neural networks with optimizations, A. Nazarova, et al., J. Chem. Info. Model. 61, 2175 (2021)
  • Dielectric constant of liquid water determined with neural network quantum molecular dynamics, A. Krishnamoorthy, et al., Phys. Rev. Lett. 126, 216403 (2021)--Ruru (11/28)
  • Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods, D. Montes de Oca Zapiain, et al., npJ Comput. Mater. 7, 3 (2021)
  • Identifying shifts in collective attention to topics on social media, Y. He, et al., SBP-BRiSM, LNCS 12720, 224 (2021)--Kai (11/21)
  • Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators, L. Lu, et al., Nature Machine Intell. 3, 218 (2021)--Haotian and Chenchen (11/28)
  • Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture, C. W. Park, et al., npJ Comput. Mater. 7, 73 (2021)--Hoda and Samprita (11/18)
  • Learning local equivariant representations for large-scale atomistic dynamics, A. Musaelian, et al., arXiv, 2204.05249v1 (2022)--Hoda and Samprita (11/18)
  • Inferring topological transitions in pattern-forming processes with self-supervised learning, M. Abram, et al., npJ Comput. Mater. 8, 205 (2022)
  • Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics, T. Linker, et al., Science Adv. 8, eabk2625 (2022)
• Graph data
  • Analytic and algorithmic solution of random satisfiability problems, M. Mezard, et al., Science 297, 812 (2002)
  • A scalable distributed parallel breadth-first search algorithm on BlueGene/L, A. Yoo, et al., in Proc. of Supercomputing (SC05) (ACM/IEEE, 2005)
  • Collision-free spatial hash functions for structural analysis of billion-vertex chemical bond networks, C. Zhang, et al., Comput. Phys. Commun. 175, 339 (2006)
  • Finding long cycles in graphs, E. Marinari, et al., Phys. Rev. E 75, 066708 (2007)
  • Hypergraphs and cellular networks, S. Klamt, et al., PLoS Comput. Biol. 5(5), e1000385 (2009)
  • The emerging field of signal processing on graphs, D. I. Shuman, et al., IEEE Signal Proc. Mag. 2013(5), 84 (2013)
  • Graph curvature for differentiating cancer networks, R. Sandhu, et al., Sci. Rep. 5, 12323 (2015)
  • Synthesized classifiers for zero-shot learning, S. Changpinyo, et al., in Proc. of Computer Vision & Pattern Recognition (CVPR) (IEEE, 2016)--Iurii (11/28)
  • Neural message passing for quantum chemistry, J. Gilmer, et al., in Proc. Int'l Conf. on Machine Learning, ICML (2017)
  • Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties, T. Xie and J. C. Grossman, Phys. Rev. Lett. 120, 145301 (2018)
  • Graph neural network analysis of layered material phases, K. Liu, et al., in Proc. SpringSim-HPC (SCS, 2019)
  • Decoding molecular graph embeddings with reinforcement learning, S. Kearnes, et al., in Proc. Int'l Conf. on Machine Learning, ICML (2019)
  • Efficient scaling of dynamic graph neural networks, V. Chakaravarthy, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)
  • DistGNN: scalable distributed training for large-scale graph neural networks, Vasimuddin Md, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)--Jared (11/7)

Stochastic simulation methods

• Monte Carlo (MC) simulation basics
  • Theoretical foundations of dynamical Monte Carlo simulations, K. A. Fichthorn and W. H. Weinberg, J. Chem Phys. 95, 1090 (1991)
  • Suppressing roughness of virtual times in parallel discrete-event simulations, G. Korniss, et al., Science 299, 677 (2003)
  • Optimal allocation of replicas to processors in parallel tempering simulations, D. J. Earlab and M. W. Deemr, J. Phys. Chem. B 108, 6844 (2004)
  • Parallel tempering: theory, applications, and new perspectives, D. J. Earlab and M. W. Deemr, Phys. Chem. Chem. Phys. 7, 3910 (2005)
  • A decentralized parallel implementation for parallel tempering algorithm, Y. Li, et al., Parallel Comput. 35, 269 (2009)
  • Towards optimal scaling of Metropolis-coupled Markov chain Monte Carlo, Y. F. Atchande, et al., Stat. Comput. 20 (2010)
  • Billion-atom synchronous parallel kinetic Monte Carlo simulations of critical 3D Ising systems, E. Martinez, et al., J. Comput. Phys. 230, 1359 (2011)
  • An Introduction to Monte Carlo Simulations of Surface Reactions, A. P. J. Jansen (Springer, 2012)
  • MEDYAN: mechanochemical simulations of contraction and polarity alignment in actomyosin networks, K. Popov, et al., PLOS Comput. Biol. 12, e1004877 (2016)--Carlos (11/21)
  • A derivation and scalable implementation of the synchronous parallel kinetic Monte Carlo method for simulating long-time dynamics, H. S. Byun, et al., Comput. Phys. Commun. 219, 246 (2017)
  • GPU accelerated population annealing algorithm, L. Y. Barash, et al., Comput. Phys. Commun. 220, 341 (2017)
  • TensorKMC: kinetic Monte Carlo simulation of 50 trillion atoms driven by deep learning on a new generation of Sunway supercomputer, H. Shang, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)
• Long time dynamics and global optimization
  • The topology of multidimensional potential energy surfaces: theory and application to peptide structure and kinetics, O. M. Becker and M. Karplus, J. Chem. Phys. 106, 1495 (1997)
  • Sampling activated mechanisms in proteins with the activation-relaxation technique, N. Mousseau, et al., J. Mol. Graph. Model. 19, 78 (2001); source codes
  • Self-learning kinetic Monte Carlo method: application to Cu(111), O. Trushin, et al., Phys. Rev. B 72, 115401 (2005)
  • Automated model reduction for complex systems exhibiting metastability, I. Horenko, et al., Multiscale Model. Sim. 5, 802 (2006)
  • Pathfinder: a parallel search algorithm for concerted atomistic events, A. Nakano, Comput. Phys. Commun. 176, 292 (2007)
  • Protein folding by zipping and assembly, S. B. Ozkan, et al., Proc. Nat'l Academy Sci. 104, 11987 (2007)
  • Computational linguistics: a new tool for exploring biopolymer structures and statistical mechanics, K. A. Dill, et al., Polymer 48, 4289 (2007)
  • A space-time-ensemble parallel nudged elastic band algorithm for molecular kinetics simulation, A. Nakano, Comput. Phys. Commun. 178, 280 (2008)
  • Accelerated molecular dynamics methods: introduction and recent developments, D. Perez, et al., Annu. Rep. Comput. Chem. 5, 79 (2009); Extending the time scale in atomistic simulation of materials, A. F. Voter, F. Montalenti, and T. C. Germann, Annu. Rev. Mater. Res. 32, 321 (2002)
  • Tracing conformational changes in proteins, N. Haspel, et al., BMC Struct. Biol. 10, S1 (2010)
  • Enhanced modeling via network theory: adaptive sampling of Markov state models, G. R. Bowman, et al., J. Chem. Theory Comput. 6, 787 (2010)
  • Optimizing transition states via kernel-based machine learning, Z. D. Pozun, et al., J. Chem. Phys. 136, 174101 (2012)
  • Reinforced dynamics for enhanced sampling in large atomic and molecular systems, L. Zhang, et al., J. Chem. Phys. 148, 124113 (2018)
  • Markov state models: from an art to a science, B. E. Husic and V. S. Pande, J. Am. Chem. Soc. 140, 2386 (2018)
  • Accelerating molecular dynamics simulations with population annealing, H. Christiansen, et al., Phys. Rev. Lett. 122, 060602 (2019)
  • Boltzmann generators: sampling equilibrium states of many-body systems with deep learning, F. Noe, et al., Science 365, 1001 (2019); see also Machine learning transforms how microstates are sampled, M. Tuckerman, Science 365, 982 (2019)
  • Coupling streaming AI and HPC ensembles to achieve 100-1000× faster biomolecular simulations, A. Brace, et al., in Proc. of Int'l Parallel & Distributed Processing Symp. (IPDPS 2022) (IEEE, 2022)
• Stochastic continuum simulations
  • Stochastic finite elements with multiple random non-Gaussian properties, R. Ghanem, J. Eng. Mech. 125, 26 (1999)
  • Parallel computing for option pricing based on the backward stochastic differential equation, Y. Peng, et al., in Proc. of High Performance Computing and Applications (HPCA 2009) (2009)
  • Stochastic time-dependent DFT with optimally tuned range-separated hybrids: application to excitonic effects in large phosphorene sheets, V. Vlcek, et al., J. Chem. Phys. 150, 184118 (2019)

Distributed scientific computing

• Grid-enabling scientific applications and workflows
  • Screen savers of the world unite, M. Shirts and V. S. Pande, Science 290, 1903 (2000)
  • Supporting efficient execution in heterogeneous distributed computing environments with Cactus and Globus, G. Allen, et al., in Proc. of Supercomputing (SC01) (ACM/IEEE, 2001)
  • Collaborative simulation Grid: multiscale quantum-mechanical/classical atomistic simulations on distributed PC clusters in the US and Japan, H. Kikuchi, et al., in Proc. of Supercomputing (SC02) (IEEE/ACM, 2002)
  • A case for Grid computing on virtual machines, R. J. Figueiredo, P. A. Dinda, and J. A. B. Fortes, in Proc. of Int'l Conf. on Distributed Computing Systems (IEEE CS Press, 2003)
  • BOINC: A system for public-resource computing and storage, D. P. Anderson, in Proc. of 5th Int'l Workshop on Grid Computing (IEEE/ACM, 2004)
  • Sustainable adaptive Grid supercomputing: multiscale simulation of semiconductor processing across the Pacific, H. Takemiya, et al., in Proc. of Supercomputing (SC06) (IEEE/ACM, 2006)
  • Remote runtime steering of integrated terascale simulation and visualization, H. Yu, et al., in Proc. of Supercomputing (SC06) (IEEE/ACM, 2006)
  • SCEC CyberShake workflows - automating probabilistic seismic hazard analysis calculations, P. Maechling, et al., in Workflows for e-Science, eds. D. Gannon, et al. (Springer, 2007)
  • Grid enablement and sustainable simulation of multiscale physics applications, Y. Song, et al., in Proc. of CCGrid (2009)
  • Predicting protein structures with a multiplayer online game, S. Cooper, et al., Nature 466, 756 (2010)
  • SMV-HUNTER: Large scale, automated detection of SSL/TLS man-in-the-middle vulnerabilities in Android apps, D. Sounthiraraj, et al., in Proc. of Network & Distributed System Security (NDSS) (2014)
  • Workflows for science: a challenge when facing the convergence of HPC and Big Data, R. M. Badia, et al., Supercomp. Front. Innovations 4, 25 (2017)--Taina (11/7)
  • De novo protein design by citizen scientists, B. Koepnick, et al., Nature 570, 390 (2019)
  • Comparing the efficiency of in situ visualization paradigms at scale, J. Kress, et al., in Proc. of International Conference on High Performance Computing (ISC) (2019)
• Cloud computing
  • MapReduce: simplified data processing on large clusters, J. Dean and S. Ghemawat, Comm. ACM 51(1), 107 (2008)
  • Above the clouds: a Berkeley view of cloud computing, M. Armbrust, et al., Tech. Report, UC Berkeley (2009)
  • High-performance cloud computing: a view of scientific applications, C. Vecchiola, et al., in Proc. of PSAN (2010)
  • Distributed GraphLab: a framework for machine learning and data mining in the cloud, Y. Low, et al., Proc. VLDB 5, 716 (2012)
  • Container orchestration on HPC systems through Kubernetes, N. Zhou, et al., J. Cloud Comp.B 10, 16 (2021)--Khoi

Quantum parallel scientific computing

• Quantum computing
  • Quantum computing in the NISQ era and beyond, J. Preskill, Quantum 2, 79 (2018)
  • Quantum supremacy using a programmable superconducting processor, F. Arute, et al., Nature 574, 505 (2019)
  • Establishing the quantum supremacy frontier with a 281 Pflop/s simulation, B. Villalonga, et al., arXiv, 1905.00444v1 (2019)
  • Quantum-assisted genetic algorithm, J. King, et al., arXiv, 1907.00707 (2019)
  • Resource-efficient digital quantum simulation of d-level systems for photonic, vibrational, and spin-s Hamiltonians, N. P. D. Sawaya, et al., npJ Quantum Info. 6, 49 (2020)--Edward (11/28)
  • Quantum computing 40 years later, J. Preskill, arXiv, 2106.10522 (2021)
  • Distributed quantum computing with QMPI, T. Haner, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)
• Quantum simulation
  • Simulating physics with computers, R. P. Feynman, Int. J. Theor. Phys. 21, 467 (1982)
  • Universal quantum simulators, S. Lloyd, Science 273, 1073 (1996)
  • Simulated quantum computation of molecular energies, A. Aspuru-Guzik, et al., Science 309, 1704 (2005)
  • A variational eigenvalue solver on a photonic quantum processor, A. Peruzzo, et al., Nat. Commun. 5, 4213 (2014)
  • Simulation of nonequilibrium dynamics on a quantum computer, H. Lamm and S. Lawrence, Phys. Rev. Lett. 121, 170501 (2018)
  • Random compiler for fast Hamiltonian simulation, E. Campbell, Phys. Rev. Lett. 123, 070503 (2019)
  • Simulating quantum many-body dynamics on a current digital quantum computer, A. Smith, et al., npJ Quant. Info. 5, 106 (2019)
  • Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution, M. Motta, et al., Nat. Phys. 16, 205 (2020)
  • Towards dynamic simulations of materials on quantum computers, L. Bassman, et al., Phys. Rev. B 101, 184305 (2020)
  • Domain-specific compilers for dynamic simulations of quantum materials on quantum computers, L. Bassman, et al., Quant. Sci. Tech. 6, 014007 (2021)
  • MISTIQS: an open-source software for performing quantum dynamics simulations on quantum computers, C. Powers, et al., SoftwareX 14, 100696 (2021)
  • SW_Qsim: a minimize-memory quantum simulator with high-performance on a new Sunway supercomputer, F. Li, et al., in Proc. of Supercomputing (SC21) (ACM/IEEE, 2021)