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Parallel Computing and Computer Systems

The Scalable Computing Group ( ScaLab)  designs system solutions for scalable and efficient parallel computing, powered by emerging processor and memory technologies including GPUs, RISC-V, smartNICs, Quantum Processing Units (QPUs), and heterogeneous memory. Through hardware-software co-design, we tackle the growing computational demands of scientific simulation, machine learning, quantum computing, and cloud applications, with an emphasis on long-term sustainability.

Projects

VR: System-informed Disaggregated Memory

Role: PI

The project aims to enable the efficient execution of industrial and scientific workloads on future computing infrastructures with disaggregated memory. The primary approach combines workload characteristics and runtime system states to optimize data placement and movement. This project will conduct four research tasks: composable memory requirements, transient data caching services, along-the-path data reduction, and global coordination.

EU Horizon: OpenCUBE  Open-Source Cloud-Based Services on EPI Systems

Role: Coordinator

OpenCUBE is missioned to deliver the first deployment-ready cloud platform built on processors made in Europe. In synergy with the efforts of the European Processor Initiative (EPI) in designing and developing European chips, the OpenCUBE project aims to develop a full software stack for promoting performance, energy efficiency, and programming efficacy in heterogeneous data centers. Together with pilot use cases, OpenCube seeks to streamline and encourage user adoption toward the forthcoming European cloud platform.

EU Horizon: HIGHER  - European Heterogeneous Cloud/Edge Infrastructures for Next Generation Hybrid Services

Role: KTH PI

The HIGHER project will develop the first European energy- efficient OCP-compliant modules utilized in cloud and edge infrastructure, powering a real pre-production architecture, developed, among others, on top of the outcomes of the EPI and EUPilot projects.

SeRC SESSI Malleability and Elasticity for HPC Applications

Role: PI

This project develops software to enable runtime autoscaling of compute resources for HPC simulations. Positioned at the convergence of HPC and cloud computing, it investigates the HPC schedulers Slurm and Flux alongside the Kubernetes autoscaler. We design new algorithms using heuristics and reinforcement learning to improve resource allocation decisions. By shifting from rigid HPC resource allocation toward adaptive resource provisioning, the project improves both resource efficiency and the malleability of HPC applications.

Vinnova: QC-CC Pilot

Role: KTH PI

This project seeks to establish a systematic pathway for optimizing applications that can efficiently leverage large-scale hybrid Quantum - Classical computing systems.