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Massaro, D., Karp, M., Jansson, N., Markidis, S. & Schlatter, P. (2024). Direct numerical simulation of the turbulent flow around a Flettner rotor. Scientific Reports, 14(1), Article ID 3004.
Open this publication in new window or tab >>Direct numerical simulation of the turbulent flow around a Flettner rotor
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2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 3004Article in journal (Refereed) Published
Abstract [en]

The three-dimensional turbulent flow around a Flettner rotor, i.e. an engine-driven rotating cylinder in an atmospheric boundary layer, is studied via direct numerical simulations (DNS) for three different rotation speeds (α). This technology offers a sustainable alternative mainly for marine propulsion, underscoring the critical importance of comprehending the characteristics of such flow. In this study, we evaluate the aerodynamic loads produced by the rotor of height h, with a specific focus on the changes in lift and drag force along the vertical axis of the cylinder. Correspondingly, we observe that vortex shedding is inhibited at the highest α values investigated. However, in the case of intermediate α, vortices continue to be shed in the upper section of the cylinder (y/h>0.3). As the cylinder begins to rotate, a large-scale motion becomes apparent on the high-pressure side, close to the bottom wall. We offer both a qualitative and quantitative description of this motion, outlining its impact on the wake deflection. This finding is significant as it influences the rotor wake to an extent of approximately one hundred diameters downstream. In practical applications, this phenomenon could influence the performance of subsequent boats and have an impact on the cylinder drag, affecting its fuel consumption. This fundamental study, which investigates a limited yet significant (for DNS) Reynolds number and explores various spinning ratios, provides valuable insights into the complex flow around a Flettner rotor. The simulations were performed using a modern GPU-based spectral element method, leveraging the power of modern supercomputers towards fundamental engineering problems.

Place, publisher, year, edition, pages
Springer Nature, 2024
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-344051 (URN)10.1038/s41598-024-53194-x (DOI)2-s2.0-85184207516 (Scopus ID)
Funder
KTH Royal Institute of TechnologyKTH Royal Institute of Technology
Note

QC 20240301

Available from: 2024-02-29 Created: 2024-02-29 Last updated: 2024-04-22Bibliographically approved
Jansson, N., Karp, M., Podobas, A., Markidis, S. & Schlatter, P. (2024). Neko: A modern, portable, and scalable framework for high-fidelity computational fluid dynamics. Computers & Fluids, 275, 106243-106243, Article ID 106243.
Open this publication in new window or tab >>Neko: A modern, portable, and scalable framework for high-fidelity computational fluid dynamics
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2024 (English)In: Computers & Fluids, ISSN 0045-7930, E-ISSN 1879-0747, Vol. 275, p. 106243-106243, article id 106243Article in journal (Refereed) Published
Abstract [en]

Computational fluid dynamics (CFD), in particular applied to turbulent flows, is a research area with great engineering and fundamental physical interest. However, already at moderately high Reynolds numbers the computational cost becomes prohibitive as the range of active spatial and temporal scales is quickly widening. Specifically scale-resolving simulations, including large-eddy simulation (LES) and direct numerical simulations (DNS), thus need to rely on modern efficient numerical methods and corresponding software implementations. Recent trends and advancements, including more diverse and heterogeneous hardware in High-Performance Computing (HPC), are challenging software developers in their pursuit for good performance and numerical stability. The well-known maxim “software outlives hardware” may no longer necessarily hold true, and developers are today forced to re-factor their codebases to leverage these powerful new systems. In this paper, we present Neko, a new portable framework for high-order spectral element discretization, targeting turbulent flows in moderately complex geometries. Neko is fully available as open software. Unlike prior works, Neko adopts a modern object-oriented approach in Fortran 2008, allowing multi-tier abstractions of the solver stack and facilitating hardware backends ranging from general-purpose processors (CPUs) down to exotic vector processors and FPGAs. We show that Neko’s performance and accuracy are comparable to NekRS, and thus on-par with Nek5000’s successor on modern CPU machines. Furthermore, we develop a performance model, which we use to discuss challenges and opportunities for high-order solvers on emerging hardware

Place, publisher, year, edition, pages
Elsevier BV, 2024
National Category
Fluid Mechanics and Acoustics Computational Mathematics Computer Sciences
Identifiers
urn:nbn:se:kth:diva-344896 (URN)10.1016/j.compfluid.2024.106243 (DOI)2-s2.0-85189508362 (Scopus ID)
Funder
Swedish Research Council, 2019-04723EU, Horizon 2020, 823691EU, Horizon 2020, 801039
Note

QC 20240403

Available from: 2024-04-02 Created: 2024-04-02 Last updated: 2024-04-22Bibliographically approved
Jansson, N., Karp, M., Perez, A., Mukha, T., Ju, Y., Liu, J., . . . Markidis, S. (2023). Exploring the Ultimate Regime of Turbulent Rayleigh–Bénard Convection Through Unprecedented Spectral-Element Simulations. In: SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis: . Paper presented at SC: The International Conference for High Performance Computing, Networking, Storage, and Analysis, NOV 12–17 DENVER, CO, USA (pp. 1-9). Association for Computing Machinery (ACM), Article ID 5.
Open this publication in new window or tab >>Exploring the Ultimate Regime of Turbulent Rayleigh–Bénard Convection Through Unprecedented Spectral-Element Simulations
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2023 (English)In: SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Association for Computing Machinery (ACM) , 2023, p. 1-9, article id 5Conference paper, Published paper (Refereed)
Abstract [en]

We detail our developments in the high-fidelity spectral-element code Neko that are essential for unprecedented large-scale direct numerical simulations of fully developed turbulence. Major inno- vations are modular multi-backend design enabling performance portability across a wide range of GPUs and CPUs, a GPU-optimized preconditioner with task overlapping for the pressure-Poisson equation and in-situ data compression. We carry out initial runs of Rayleigh–Bénard Convection (RBC) at extreme scale on the LUMI and Leonardo supercomputers. We show how Neko is able to strongly scale to 16,384 GPUs and obtain results that are not pos- sible without careful consideration and optimization of the entire simulation workflow. These developments in Neko will help resolv- ing the long-standing question regarding the ultimate regime in RBC. 

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
National Category
Computer Sciences Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-340333 (URN)10.1145/3581784.3627039 (DOI)2-s2.0-85179549233 (Scopus ID)
Conference
SC: The International Conference for High Performance Computing, Networking, Storage, and Analysis, NOV 12–17 DENVER, CO, USA
Funder
Swedish Research Council, 2019-04723Swedish e‐Science Research CenterEU, Horizon 2020, 101093393, 101092621, 956748
Note

Part of ISBN 9798400701092

QC 20231204

Available from: 2023-12-04 Created: 2023-12-04 Last updated: 2024-04-22Bibliographically approved
Chien, S. W. .., Sato, K., Podobas, A., Jansson, N., Markidis, S. & Honda, M. (2023). Improving Cloud Storage Network Bandwidth Utilization of Scientific Applications. In: Proceedings of the 7th Asia-Pacific Workshop on Networking, APNET 2023: . Paper presented at 7th Asia-Pacific Workshop on Networking, APNET 2023, Jun 29 - Jun 30 2023, Hong Kong, China, (pp. 172-173). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Improving Cloud Storage Network Bandwidth Utilization of Scientific Applications
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2023 (English)In: Proceedings of the 7th Asia-Pacific Workshop on Networking, APNET 2023, Association for Computing Machinery (ACM) , 2023, p. 172-173Conference paper, Published paper (Refereed)
Abstract [en]

Cloud providers began to provide managed services to attract scientific applications, which have been traditionally executed on supercomputers. One example is AWS FSx for Lustre, a fully managed parallel file system (PFS) released in 2018. However, due to the nature of scientific applications, the frontend storage network bandwidth is left completely idle for the majority of its lifetime. Furthermore, the pricing model does not match the scalability requirement. We propose iFast, a novel host-side caching mechanism for scientific applications that improves storage bandwidth utilization and end-to-end application performance: by overlapping compute and data writeback through inexpensive local storage. iFast supports the Massage Passing Interface (MPI) library that is widely used by scientific applications and is implemented as a preloaded library. It requires no change to applications, the MPI library, or support from cloud operators. We demonstrate how iFast can accelerate the end-to-end time of a representative scientific application Neko, by 13-40%.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
National Category
Computer Systems Computer Sciences
Identifiers
urn:nbn:se:kth:diva-338993 (URN)10.1145/3600061.3603122 (DOI)001147804500029 ()2-s2.0-85173833099 (Scopus ID)
Conference
7th Asia-Pacific Workshop on Networking, APNET 2023, Jun 29 - Jun 30 2023, Hong Kong, China,
Note

Part of ISBN 9798400707827

QC 20231101

Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2024-02-27Bibliographically approved
Ju, Y., Li, M., Perez, A., Bellentani, L., Jansson, N., Markidis, S., . . . Laure, E. (2023). In-Situ Techniques on GPU-Accelerated Data-Intensive Applications. In: Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023: . Paper presented at 19th IEEE International Conference on e-Science, e-Science 2023, Limassol, Cyprus, Oct 9 2023 - Oct 14 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>In-Situ Techniques on GPU-Accelerated Data-Intensive Applications
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2023 (English)In: Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

The computational power of High-Performance Computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At the same time, checkpointing is crucial for long runs on HPC clusters, due to limited walltimes and/or failures of system components, and typically requires the storage of large amount of data. Thus, restricted IO performance and storage capacity can lead to bottlenecks for the performance of full application workflows (as compared to computational kernels without IO). In-situ techniques, where data is further processed while still in memory rather to write it out over the I/O subsystem, can help to tackle these problems. In contrast to traditional post-processing methods, in-situ techniques can reduce or avoid the need to write or read data via the IO subsystem. They offer a promising approach for applications aiming to leverage the full power of large scale HPC systems. In-situ techniques can also be applied to hybrid computational nodes on HPC systems consisting of graphics processing units (GPUs) and central processing units (CPUs). On one node, the GPUs would have significant performance advantages over the CPUs. Therefore, current approaches for GPU-accelerated applications often focus on maximizing GPU usage, leaving CPUs underutilized. In-situ tasks using CPUs to perform data analysis or preprocess data concurrently to the running simulation, offer a possibility to improve this underutilization.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
CPU, GPU, HPC, in-situ
National Category
Computer Sciences Computer Systems
Identifiers
urn:nbn:se:kth:diva-338984 (URN)10.1109/e-Science58273.2023.10254865 (DOI)2-s2.0-85174292669 (Scopus ID)
Conference
19th IEEE International Conference on e-Science, e-Science 2023, Limassol, Cyprus, Oct 9 2023 - Oct 14 2023
Note

Part of ISBN 9798350322231

QC 20231101

Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2023-12-04Bibliographically approved
Karp, M., Liu, F., Stanly, R., Rezaeiravesh, S., Jansson, N., Schlatter, P. & Markidis, S. (2023). Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow. In: Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023: . Paper presented at 2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023, Denver, United States of America, Nov 12 2023 - Nov 17 2023 (pp. 387-390). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Uncertainty Quantification of Reduced-Precision Time Series in Turbulent Channel Flow
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2023 (English)In: Proceedings of 2023 SC Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023, Association for Computing Machinery (ACM) , 2023, p. 387-390Conference paper, Published paper (Refereed)
Abstract [en]

With increased computational power through the use of arithmetic in low-precision, a relevant question is how lower precision affects simulation results, especially for chaotic systems where analytical round-off estimates are non-trivial to obtain. In this work, we consider how the uncertainty of the time series of a direct numerical simulation of turbulent channel flow at Ret = 180 is affected when restricted to a reduced-precision representation. We utilize a non-overlapping batch means estimator and find that the mean statistics can, in this case, be obtained with significantly fewer mantissa bits than conventional IEEE-754 double precision, but that the mean values are observed to be more sensitive in the middle of the channel than in the near-wall region. This indicates that using lower precision in the near-wall region, where the majority of the computational efforts are required, may benefit from low-precision floating point units found in upcoming computer hardware.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-341470 (URN)10.1145/3624062.3624105 (DOI)2-s2.0-85178155242 (Scopus ID)
Conference
2023 International Conference on High Performance Computing, Network, Storage, and Analysis, SC Workshops 2023, Denver, United States of America, Nov 12 2023 - Nov 17 2023
Note

QC 20240109

Part of ISBN 979-840070785-8

Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-04-22Bibliographically approved
Karp, M., Podobas, A., Kenter, T., Jansson, N., Plessl, C., Schlatter, P. & Markidis, S. (2022). A High-Fidelity Flow Solver for Unstructured Meshes on Field-Programmable Gate Arrays: Design, Evaluation, and Future Challenges. In: HPCAsia2022: International Conference on High Performance Computing in Asia-Pacific Region: . Paper presented at HPCAsia2022: International Conference on High Performance Computing in Asia-Pacific Region (pp. 125-136). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>A High-Fidelity Flow Solver for Unstructured Meshes on Field-Programmable Gate Arrays: Design, Evaluation, and Future Challenges
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2022 (English)In: HPCAsia2022: International Conference on High Performance Computing in Asia-Pacific Region, Association for Computing Machinery (ACM) , 2022, p. 125-136Conference paper, Published paper (Refereed)
Abstract [en]

The impending termination of Moore’s law motivates the search for new forms of computing to continue the performance scaling we have grown accustomed to. Among the many emerging Post-Moore computing candidates, perhaps none is as salient as the Field-Programmable Gate Array (FPGA), which offers the means of specializing and customizing the hardware to the computation at hand.

In this work, we design a custom FPGA-based accelerator for a computational fluid dynamics (CFD) code. Unlike prior work – which often focuses on accelerating small kernels – we target the entire Poisson solver on unstructured meshes based on the high-fidelity spectral element method (SEM) used in modern state-of-the-art CFD systems. We model our accelerator using an analytical performance model based on the I/O cost of the algorithm. We empirically evaluate our accelerator on a state-of-the-art Intel Stratix 10 FPGA in terms of performance and power consumption and contrast it against existing solutions on general-purpose processors (CPUs). Finally, we propose a data movement-reducing technique where we compute geometric factors on the fly, which yields significant (700+ Gflop/s) single-precision performance and an upwards of 2x reduction in runtime for the local evaluation of the Laplace operator.

We end the paper by discussing the challenges and opportunities of using reconfigurable architecture in the future, particularly in the light of emerging (not yet available) technologies.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2022
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-309190 (URN)10.1145/3492805.3492808 (DOI)2-s2.0-85122641610 (Scopus ID)
Conference
HPCAsia2022: International Conference on High Performance Computing in Asia-Pacific Region
Note

QC 20220223

Available from: 2022-02-22 Created: 2022-02-22 Last updated: 2024-04-22Bibliographically approved
Atzori, M., Köpp, W., Chien, W. D., Massaro, D., Mallor, F., Peplinski, A., . . . Weinkauf, T. (2022). In situ visualization of large-scale turbulence simulations in Nek5000 with ParaView Catalyst. Journal of Supercomputing, 78(3), 3605-3620
Open this publication in new window or tab >>In situ visualization of large-scale turbulence simulations in Nek5000 with ParaView Catalyst
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2022 (English)In: Journal of Supercomputing, ISSN 0920-8542, E-ISSN 1573-0484, Vol. 78, no 3, p. 3605-3620Article in journal (Refereed) Published
Abstract [en]

In situ visualization on high-performance computing systems allows us to analyze simulation results that would otherwise be impossible, given the size of the simulation data sets and offline post-processing execution time. We develop an in situ adaptor for Paraview Catalyst and Nek5000, a massively parallel Fortran and C code for computational fluid dynamics. We perform a strong scalability test up to 2048 cores on KTH’s Beskow Cray XC40 supercomputer and assess in situ visualization’s impact on the Nek5000 performance. In our study case, a high-fidelity simulation of turbulent flow, we observe that in situ operations significantly limit the strong scalability of the code, reducing the relative parallel efficiency to only ≈ 21 % on 2048 cores (the relative efficiency of Nek5000 without in situ operations is ≈ 99 %). Through profiling with Arm MAP, we identified a bottleneck in the image composition step (that uses the Radix-kr algorithm) where a majority of the time is spent on MPI communication. We also identified an imbalance of in situ processing time between rank 0 and all other ranks. In our case, better scaling and load-balancing in the parallel image composition would considerably improve the performance of Nek5000 with in situ capabilities. In general, the result of this study highlights the technical challenges posed by the integration of high-performance simulation codes and data-analysis libraries and their practical use in complex cases, even when efficient algorithms already exist for a certain application scenario.

Place, publisher, year, edition, pages
Springer, 2022
Keywords
Computational fluid dynamics, High-performance computing, In situ visualization, Catalysts, Data visualization, Efficiency, Image enhancement, Scalability, Supercomputers, Visualization, Application scenario, High performance computing systems, High-fidelity simulations, High-performance simulation, Large scale turbulence, Parallel efficiency, Relative efficiency, Technical challenges, In situ processing
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-311178 (URN)10.1007/s11227-021-03990-3 (DOI)000680293400003 ()35210696 (PubMedID)2-s2.0-85111797526 (Scopus ID)
Note

QC 20220502

Available from: 2022-05-02 Created: 2022-05-02 Last updated: 2024-01-19Bibliographically approved
Karp, M., Jansson, N., Podobas, A., Schlatter, P. & Markidis, S. (2022). Reducing Communication in the Conjugate Gradient Method: A Case Study on High-Order Finite Elements. In: Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2022: . Paper presented at 2022 Platform for Advanced Scientific Computing Conference, PASC 2022, 27 June 2022 through 29 June 2022, Basel, Switzerland. Association for Computing Machinery (ACM), Article ID 2.
Open this publication in new window or tab >>Reducing Communication in the Conjugate Gradient Method: A Case Study on High-Order Finite Elements
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2022 (English)In: Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2022, Association for Computing Machinery (ACM) , 2022, article id 2Conference paper, Published paper (Refereed)
Abstract [en]

Currently, a major bottleneck for several scientific computations is communication, both communication between different processors, so-called horizontal communication, and vertical communication between different levels of the memory hierarchy. With this bottleneck in mind, we target a notoriously communication-bound solver at the core of many high-performance applications, namely the conjugate gradient method (CG). To reduce the communication we present lower bounds on the vertical data movement in CG and go on to make a CG solver with reduced data movement. Using our theoretical analysis we apply our CG solver on a high-performance discretization used in practice, the spectral element method (SEM). Guided by our analysis, we show that for the Poisson equation on modern GPUs we can improve the performance by 30% by both rematerializing the discrete system and by reformulating the system to work on unique degrees of freedom. In order to investigate how horizontal communication can be reduced, we compare CG to two communication-reducing techniques, namely communication-avoiding and pipelined CG. We strong scale up to 4096 CPU cores and showcase performance improvements of upwards of 70% for pipelined CG compared to standard CG when applied on SEM at scale. We show that in addition to improving the scaling capabilities of the solver, initial measurements indicate that the convergence of SEM is largely unaffected by pipelined CG.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2022
National Category
Information Systems
Identifiers
urn:nbn:se:kth:diva-317542 (URN)10.1145/3539781.3539785 (DOI)2-s2.0-85134847143 (Scopus ID)
Conference
2022 Platform for Advanced Scientific Computing Conference, PASC 2022, 27 June 2022 through 29 June 2022, Basel, Switzerland
Note

QC 20220913

Part of proceedings: ISBN 978-145039410-9

Available from: 2022-09-13 Created: 2022-09-13 Last updated: 2024-04-22Bibliographically approved
Vincent, J., Gong, J., Karp, M., Peplinski, A., Jansson, N., Podobas, A., . . . Schlatter, P. (2022). Strong Scaling of OpenACC enabled Nek5000 on several GPU based HPC systems. In: HPCAsia2022: International Conference on High Performance Computing in Asia-Pacific Region. Paper presented at HPC Asia2022: International Conference on High Performance Computing in Asia-Pacific Region Virtual Event Japan January 12 - 14, 2022 (pp. 94-102). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Strong Scaling of OpenACC enabled Nek5000 on several GPU based HPC systems
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2022 (English)In: HPCAsia2022: International Conference on High Performance Computing in Asia-Pacific Region, Association for Computing Machinery (ACM) , 2022, p. 94-102Conference paper, Published paper (Refereed)
Abstract [en]

We present new results on the strong parallel scaling for the OpenACC-accelerated implementation of the high-order spectral element fluid dynamics solver Nek5000. The test case considered consists of a direct numerical simulation of fully-developed turbulent flow in a straight pipe, at two different Reynolds numbers Reτ = 360 and Reτ = 550, based on friction velocity and pipe radius. The strong scaling is tested on several GPU-enabled HPC systems, including the Swiss Piz Daint system, TACC's Longhorn, Jülich's JUWELS Booster, and Berzelius in Sweden. The performance results show that speed-up between 3-5 can be achieved using the GPU accelerated version compared with the CPU version on these different systems. The run-time for 20 timesteps reduces from 43.5 to 13.2 seconds with increasing the number of GPUs from 64 to 512 for Reτ = 550 case on JUWELS Booster system. This illustrates the GPU accelerated version the potential for high throughput. At the same time, the strong scaling limit is significantly larger for GPUs, at about 2000 - 5000 elements per rank; compared to about 50 - 100 for a CPU-rank.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2022
Series
ACM International Conference Proceeding Series
National Category
Computer Sciences Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:kth:diva-309189 (URN)10.1145/3492805.3492818 (DOI)2-s2.0-85122621284 (Scopus ID)
Conference
HPC Asia2022: International Conference on High Performance Computing in Asia-Pacific Region Virtual Event Japan January 12 - 14, 2022
Note

QC 20220223

Part of conference proceedings: ISBN 978-145038498-8

Available from: 2022-02-22 Created: 2022-02-22 Last updated: 2024-03-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5020-1631

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