That modern biomedical research thrives on data is an understatement. As increasingly complex biological systems are unlocked, researchers are now studying processes that span from the molecular to the societal level. In this context, powerful computing capacity is no longer a mere advantage but an indispensable requirement forstate-of-the-art research.
With sequencing technologies becoming more affordable and data collection methods more sophisticated, researchers must contend with petabytes of genomic, transcriptomic, and proteomic data. Machine learning and large-scale simulations are now integral to biomedical discoveries, requiring robust computing systems to support these operations.
To remain competitive as a source of groundbreaking medical research, the Hungarian Center of Excellence for Molecular Medicine has taken a significant step forward by establishing a dedicated computing cluster. This infrastructure significantly enhances HCEMM’s ability to explore age-related diseases, process large datasets, model, and develop novel bioinformatics approaches. The new system also provides secure on-site storage for critical research data.
While negotiating access to a major supercomputer, such as the Komondor (operated by the Governmental Information Technology Development Agency and based 225 km northeast of Szeged by road in Debrecen), is often a valid solution, and one HCEMM did employ in the past, investing in a self-hosted system offers surprisingly human-centered advantages.
One key benefit is ease of use. External computing centers often require data to be formatted and submitted in a certain way, forcing scientists to “translate” their requests into a different format. In contrast, a local computing cluster interfaces seamlessly with ongoing research.
Additionally, with both the hardware and the experts managing it at HCEMM, local research groups gain the unique ability to discuss and adjust their projects in real-time. On-site computing enables a constant loop of feedback and modifications, something that is difficult to achieve with distant supercomputing centers. Even though large external systems often surpass local capacity, a research project supported by on-site resources may progress faster toward its goals thanks to this direct, hands-on access.
HCEMM’s system, which is currently being configured for general use and has run its first test code (a simulation of a monkeypox outbreak), is a cluster containing 13 nodes (individual computers), collectively featuring 26 high-performance processors, three terabytes of DDR4 RAM, 300 terabytes of storage, and two NVIDIA A40 graphics processing units, or GPUs.
The components of this system have been selected to cater specifically to the needs of research undertaken by HCEMM research groups, which often involve managing large datasets and machine learning solutions.
GPUs play a pivotal role in biomedical research due to their superior parallel processing capabilities. Tasks such as protein folding simulations, deep learning for image recognition, and next-generation sequencing analysis benefit significantly from the massive throughput of GPU-based computing.
While traditional CPUs are limited to handling a few hundred tasks in parallel, high-end GPUs can process tens or even hundreds of thousands of simultaneous computations, making them ideal for the heavy lifting in bioinformatics.
HCEMM maximizes this strategic investment by ensuring that research groups can leverage the system to its full potential. The computer cluster forms the foundation of the Scientific Computing Advanced Core Facility, led by Dr. João Sequeira, who ensures that scientists in the institute receive guidance and support from study planning to execution.
Rather than simply providing access to a computer for research projects, HCEMM ensures that experts with a deep understanding of the computer cluster’s capabilities work alongside researchers toward a common goal.
Additionally, a new research group, Computational Medicine, led by Dr. Gergely Röst, has been established to advance the mathematical tools needed to tackle current challenges in medical research. By developing innovative approaches in research design, modeling and statistics, the group serves as a force multiplier for HCEMM’s new system, accelerating scientific discovery while optimizing efficiency and cost-effectiveness.
Perhaps surprisingly, in an increasingly online and networked environment, investing in physically bringing infrastructure and experts together offers advantages that are hard to match with raw computing power alone.
The Hungarian Center of Excellence for Molecular Medicine is a distributed institute whose scientists develop advanced diagnostics and treatment options supporting healthy aging. The HCEMM program is currently funded by an H2020 Teaming Grant (where Semmelweis University, the University of Szeged and the Hun-Ren Biological Research Center, Szeged, cooperate with their advanced partner, the European Molecular Biology Laboratory, headquartered in Heidelberg, Germany) and a thematic excellence award, as well as a national laboratory award from the Hungarian Government. The various activities are coordinated by HCEMM Nonprofit Kft., headquartered in Szeged, Hungary. HCEMM works at the interface of academic and industrial research on topics related to translational medicine. The goal is to improve the quality of life for an aging Hungarian population while at the same time lowering the cost of healthcare provision through novel applications in the field of molecular medicine.
The Scientific Computing Advanced Core Facility at HCEMM aims to position itself as a cornerstone for local bioinformatics development. It intends to facilitate collaboration by connecting the varied computational expertise of research groups and promoting the exchange of technical knowledge. Plans include expanding the technical infrastructure and streamlining workflows to improve the research efficiency and impact of scientific discoveries.
This article was first published in the Budapest Business Journal print issue of April 4, 2025.
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