Next-Gen Solutions for High-Performance Optimization

Modern computer systems are becoming more diverse as power efficiency is increasingly a first priority for their operation. Today, the main drivers are AI’s massive compute and storage demands and the need for energy-efficient solutions. Heavy compute is offloaded to accelerators (GPU, FPGA, AI chips). Fast intelligent networks run functions in the fabric, bringing processing nearer data and easing load on compute nodes. Object stores replace limited POSIX file systems. The three pillars, (1) heterogeneous processors, (2) smart high‑performance networks, and (3) new storage, force continual redesign of algorithms and software. Future architectures may require new mathematical algorithms and implementation strategies. To keep development costs manageable across varied hardware, performance portability and productivity are essential. Early decisions on algorithm design and variants help stay aligned with evolving hardware, meet performance goals, and ensure sustainability.

The HPCLab develops algorithmic and software solutions for the efficient implementation of data-driven optimization and decision workflows on near-future processor architectures and storage technologies. Our focus is on optimization methods and simulation workflows designed and developed in the MODAL Labs EnergyLab, MedLab, MobilityLab, NanoLab, and SynLab.