Description
Riken Center for Computational Science continues to advance high-performance computing architectures, moving from the benchmark Arm-based Fugaku system toward next-generation hybrid infrastructure and AI for Science frameworks. At the exhibition booth, computational scientist Ryosuke Maruyama details current research initiatives focused on expanding autonomous scientific discovery, uniting classical supercomputing resources with heterogeneous accelerator clusters and external quantum processing units. https://www.r-ccs.riken.jp/
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The computing roadmap highlights the transition from the original 48-core Fujitsu A64FX Arm processor architecture deployed in Fugaku to accelerated GPU clusters and the upcoming FugakuNEXT initiative. FugakuNEXT is being planned as an integrated simulation and artificial intelligence platform, co-locating high-throughput general-purpose CPUs and dedicated graphics processing units within unified compute nodes to deliver low-latency simulation coupling for complex scientific workloads.
Beyond traditional exascale high-performance computing, Riken is actively developing quantum-classical hybrid computing platforms. Experimental setups connect classical supercomputing nodes with superconducting quantum processors from IBM and trapped-ion quantum systems from Quantinuum located at Riken facilities in Wako, Saitama. These systems communicate over secure networking layers, enabling researchers to run specialized hybrid algorithms that partition problem spaces between quantum processing units and classical vector processors.
A central focus of the computational research program is building AI for Science tools powered by autonomous software agents. Researchers are developing agentic AI frameworks designed to help domain scientists synthesize complex datasets, orchestrate multi-parameter physical experiments, and construct predictive simulations. These specialized agents operate across multidisciplinary fields to compress discovery cycles that traditionally required years of manual modeling.
Practical application tracks demonstrate AI agent deployments across immunology and structural biology, targeting dynamic antibody modeling and novel therapeutic discovery. Parallel efforts in computational materials science utilize accelerated simulation pipelines to predict molecular properties and plan physical laboratory validation. By combining dense heterogeneous compute clusters with automated agent workflows, the platform provides scalable infrastructure for next-generation scientific inquiry.