Gianmarco Accordi 💻
Gianmarco Accordi

PhD Student

DEIB - Politecnico di Milano

About Me

I’ve completed my Master’s and Bachelor’s degree at Politecnico di Milano, with a thesis titled “A High-throughput pose selection method for extreme scale virtual screening in drug discovery”. Currently, I am a PhD student at Politecnico di Milano, and my research thesis is about the acceleration and performance portability of virtual screening applications on emerging HPC architectures. Specifically, I am part of the development team of LiGen, a high-throughput, extreme-scale virtual screening pipeline. LiGen has been used in the largest virtual screening campaign against SARS-CoV-2, the context of the European project EXSCALATE4CoV. In particular, I focus on the porting and optimization of computation kernels on GPUs. My research interests also include the integration of AI and quantum modules into virtual screening applications and the analysis of their accuracy and performance.

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Interests
  • High Performance Computing
  • Virtual Screening
  • Drug Discovery
  • Supercomputing
  • Heterogenous Computing
  • Performance Portability
Education

  • Bachelor's degree in Computer Science and Engineering

    Politecnico di Milano

  • Master's degree in Computer Science and Engineering

    Politecnico di Milano

  • PhD student in Computer Science and Engineering

    Politecnico di Milano

📚 My Research

I’m a PhD student at Politecnico di Milano, where I also work as a research fellow and teaching assistant. My research focuses on developing high-throughput virtual screening software for extreme-scale applications. I’m particularly interested in speeding up computations on supercomputing heterogeneous architectures and exploring performance-portable techniques.

Additionally, my work involves analyzing AI models and quantum computing algorithms for use in high-performance virtual screening software.

Recent Publications
Palermo, G., Accordi, G., Gadioli, D., Zhang, Y., Vitali, E., Guindani, B., Ardagna, D., Silvano, C., Beccari, A. R., Bonanni, D., Talarico, C., Lughini, F., Biswas, A. D., Martinovic, J., Golasowski, M., Silva, P., Bohm, A., Beranek, J., Krenek, J., Jansik, B., Thoman, P., Salzmann, P., Fahringer, T., Schwede, T., Alexander, L. T., Tauriello, G., Durairaj, J., Cosenza, B., Crisci, L., Emerson, A., Ficarelli, F., Lindahl, E., Wingbermuhle, S., Gregori, D., Coletti, S., Sana, E., Gschwandtner, P. (2024). LIGATE - LIgand Generator and portable drug discovery platform AT Exascale. In CF ‘24.
Cite DOI
Emanuele Vitali, Federico Ficarelli, Mauro Bisson, Davide Gadioli, Gianmarco Accordi, Massimiliano Fatica, Ea R. Beccari, Gianluca Palermo (2024). GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis. In JPDC.
Cite DOI
Accordi, Gianmarco, Gadioli, Davide, Palermo, Gianluca, Crisci, Luigi, Carpentieri, Lorenzo, Cosenza, Biagio, Beccari, Andrea R. (2024). Unlocking performance portability on LUMI-G supercomputer: A virtual screening case study. In IWOCL ‘24.
Cite DOI
Accordi, Gianmarco, Gadioli, Davide, Vitali, Emanele, Crisci, Luigi, Cosenza, Biagio, Beccari, Andrea, Palermo, Gianluca (2024). Out of kernel tuning and optimizations for portable large-scale docking experiments on GPUs. In JoS.
Cite DOI
Carpentieri, Lorenzo, D'Antonio, Marco, Fan, Kaijie, Crisci, Luigi, Cosenza, Biagio, Ficarelli, Federico, Cesarini, Daniele, Accordi, Gianmarco, Gadioli, Davide, Palermo, Gianluca, Thoman, Peter, Salzmann, Philip, Gschwandtner, Philipp, Wippler, Markus, Marchetti, Filippo, Gregori, Daniele, Beccari, Andrea Rosario (2023). Domain-Specific Energy Modeling for Drug Discovery and Magnetohydrodynamics Applications. In SC ‘24.
Cite DOI