The Latin American supercomputing ecosystem for science

LARGE, EXPENSIVE, COMPUTING­INTENSIVE research initiatives have historically promoted high-performance computing (HPC) in the wealthiest countries, most notably in the U.S., Europe, Japan, and China. The exponential impact of the Internet and of artificial intelligence (AI) has pushed HPC to a new level, affecting economies and societies worldwide. In Latin America, this was no different. Nevertheless, the use of HPC in science affected the countries in the region in a heterogeneous way. Since the first edition in 1993 of the TOP500 list of most powerful supercomputing systems in the world, only Mexico and Brazil (with 18 appearances each) made the list with research-oriented supercomputers. As of June 2020, Brazil was the only representative of Latin America on the list. HPC represents a strategic resource for Latin American researchers to respond to the economical and societal challenges in the region and to cross-fertilize with researchers in the rest of the world. Nevertheless, the Latin American countries still lag behind other countries in terms of size and regularity of investments in HPC. The table here compares the HPC capacity of the BRICS countries, which together represent almost half of the world population. As a reference, in 2018, South Africa’s GDP was 29.1% lower than Argentina’s and only 11.2% higher than Colombia’s, the two countries in Latin America with largest GDPs after Brazil and Mexico. In spite of the overall picture described here, the landscape of the Latin American HPC ecosystem for science is promising, with many initiatives and outstanding concrete results.

[1]  Laurent Emmanuel Dardenne,et al.  Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set , 2020, J. Chem. Inf. Model..

[2]  J. Klapp,et al.  FITspec: A New Algorithm for the Automated Fit of Synthetic Stellar Spectra for OB Stars , 2018, The Astrophysical Journal Supplement Series.

[3]  Rational Zika vaccine design via the modulation of antigen membrane anchors in chimpanzee adenoviral vectors , 2018, Nature Communications.

[4]  Lucas O. Müller,et al.  Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve , 2018, Scientific Reports.

[5]  Bo Göransson,et al.  Knowledge policies and universities in developing countries: Inclusive development and the “developmental university” , 2015 .

[6]  Split the Charge Difference in Two! a Rule of Thumb for Adding Proper Amounts of Ions in MD Simulations. , 2020, Journal of chemical theory and computation.

[7]  J. C. Arteaga-Velázquez,et al.  Very-high-energy particle acceleration powered by the jets of the microquasar SS 433 , 2018, Nature.

[8]  M. Cruchaga,et al.  Tsunami hydrodynamic force on a building using a SPH real-scale numerical simulation , 2019, Natural Hazards.

[9]  Luis Hernández-Callejo,et al.  Electricity demand forecasting in industrial and residential facilities using ensemble machine learning , 2020, Revista Facultad de Ingeniería Universidad de Antioquia.

[10]  Fabio Porto,et al.  BioinfoPortal: A scientific gateway for integrating bioinformatics applications on the Brazilian national high-performance computing network , 2020, Future Gener. Comput. Syst..

[11]  N Kalinin,et al.  Self-organized criticality and pattern emergence through the lens of tropical geometry , 2018, Proceedings of the National Academy of Sciences.