1. Abbasi, Mahdi and Najafi, Ali and Rafiee, Milad and Khosravi, Mohammad R. and Menon, Varun G. and Muhammad, Ghulam (2021) Efficient Flow Processing in 5G-Envisioned SDN-Based Internet of Vehicles Using GPUs. IEEE Transactions on Intelligent Transportation Systems 22(8): 5283-5292 https://doi.org/10.1109/TITS.2020.3038250
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4. Matteo Bernardini and Davide Modesti and Francesco Salvadore and Sergio Pirozzoli (2021) STREAmS: A high-fidelity accelerated solver for direct numerical simulation of compressible turbulent flows. Computer Physics Communications 263: 107906 https://doi.org/https://doi.org/10.1016/j.cpc.2021.107906, We present STREAmS, an in-house high-fidelity solver for direct numerical simulations (DNS) of canonical compressible wall-bounded flows, namely turbulent plane channel, zero-pressure gradient turbulent boundary layer and supersonic oblique shock-wave/boundary layer interaction. The solver incorporates state-of-the-art numerical algorithms, specifically designed to cope with the challenging problems associated with the solution of high-speed turbulent flows and can be used across a wide range of Mach numbers, extending from the low subsonic up to the hypersonic regime. From the computational viewpoint, STREAmS is oriented to modern HPC platforms thanks to MPI parallelization and the ability to run on multi-GPU architectures. This paper discusses the main implementation strategies, with particular reference to the CUDA paradigm, the management of a single code for traditional and multi-GPU architectures, and the optimization process to take advantage of the latest generation of NVIDIA GPUs. Performance measurements show that single-GPU optimization more than halves the computing time as compared to the baseline version. At the same time, the asynchronous patterns implemented in STREAmS for MPI communications guarantee very good parallel performance especially in the weak scaling spirit, with efficiency exceeding 97% on 1024 GPUs. For overall evaluation of STREAmS with respect to other compressible solvers, comparison with a recent GPU-enabled community solver is presented. It turns out that, although STREAmS is much more limited in terms of flow configurations that can be addressed, the advantage in terms of accuracy, computing time and memory occupation is substantial, which makes it an ideal candidate for large-scale simulations of high-Reynolds number, compressible wall-bounded turbulent flows. The solver is released open source under GPLv3 license. Program summary Program Title: STREAmS CPC Library link to program files: https://doi.org/10.17632/hdcgjpzr3y.1 Developer ’s repository link: https://github.com/matteobernardini/STREAmS Code Ocean capsule: https://codeocean.com/capsule/8931507/tree/v2 Licensing provisions: GPLv3 Programming language: Fortran 90, CUDA Fortran, MPI Nature of problem: Solving the three-dimensional compressible Navier –Stokes equations for low and high Mach regimes in a Cartesian domain configured for channel, boundary layer or shock-boundary layer interaction flows. Solution method: The convective terms are discretized using a hybrid energy-conservative shock-capturing scheme in locally conservative form. Shock-capturing capabilities rely on the use of Lax –Friedrichs flux vector splitting and weighted essentially non-oscillatory (WENO) reconstruction. The system is advanced in time using a three-stage, third-order RK scheme. Two-dimensional pencil distributed MPI parallelization is implemented alongside different patterns of GPU (CUDA Fortran) accelerated routines., GPUs, CUDA, Compressible flows, Wall turbulence, Direct numerical simulation, Open source, https://www.sciencedirect.com/science/article/pii/S0010465521000473, 0010-4655
5. Borisov, Alexey (2020) Performance Evaluation of Kuznyechik and Magma Symmetric Ciphers on AMD and NVIDIA GPUs. 10.1109/USBEREIT48449.2020.9117771, 589-592, , , 2020 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)