Gianmarco Accordi 💻
Gianmarco Accordi

PhD Student

DEIB - Politecnico di Milano

About Me

I’ve completed my Master’s and Bachelor’s degrees at Politecnico di Milano, with a thesis titled “A High-throughput pose selection method for extreme scale virtual screening in drug discovery.” 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. During the LIGATE project, I was involved in developing a portable drug discovery platform on the EuroHPC infrastructure.

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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
Crisci L., Carpentieri L., Cosenza B., Accordi G., Gadioli D., Vitali E., Palermo G., Beccari A. R. (2024). Enabling performance portability on the LiGen drug discovery pipeline. In FGCS.
Cite DOI
Gadioli D., Accordi G., Krenek J., Golasowski M., Foltyn L., Martinovic J., Beccari A. R., Palermo G. (2024). A Portable Drug Discovery Platform for Urgent Computing. Procedia Computer Science.
Cite DOI
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
Accordi G., Gadioli D., Seguini G., Beccari A. R., Palermo G. (2024). ZSMILES: An Approach for Efficient SMILES Storage for Random Access in Virtual Screening. In HiCOMB IPDPS ‘24.
Cite DOI
Vitali E., Ficarelli F., Bisson M., Gadioli D., Accordi G., Fatica M., Beccari R. A., Palermo G. (2024). GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis. In JPDC.
Cite DOI
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