Abstract This paper presents the architecture, performance characterization, and early scientific impact of Toubkal, Africa’s most powerful supercomputer (TOP500 #356, Green500 #178 as of June 2025). Designed to support high-performance computing research across disciplines, Toubkal integrates CPU, high-memory, and GPU-accelerated nodes connected via a high-bandwidth InfiniBand fabric and powered in part by renewable energy sources. We benchmark Toubkal’s compute, memory, network, and storage subsystems using standard and reproducible tools (HPL, STREAM, IOR, OMB, HPL-MxP), reporting robust performance and energy-efficiency metrics. The CPU partition achieves 63% of its theoretical peak, while GPU-accelerated HPL-MxP benchmarks on H100 nodes exceed 209 TFLOP/s per GPU. We also introduce a supervised learning model that predicts GPU frequency ranges minimizing the energy-delay product (EDP) across diverse workloads. The model achieves over 92% accuracy, with average gains of 13% in EDP, 13% in energy consumption, and a 1.1% reduction in kernel time across all kernels. The paper also compares Toubkal to several US DoE systems, analyzes early user growth, and documents real-world adoption across domains including AI, bioinformatics, and material science. These results offer both a technical reference for reproducibility and a performance baseline for emerging HPC centers worldwide. Beyond presenting a system deployment, this work contributes an early blueprint for sustainable, regional supercomputing infrastructure aligned with global standards.

Quantum circuits are difficult to simulate, and their automated optimisation is complex as well. Significant optimisations have been achieved manually (pen and paper) and not by software. This is the first in-depth study on the cost of compiling and optimising large-scale quantum circuits with state-of-the-art quantum software. We propose a hierarchy of cost metrics covering the quantum software stack and use energy as the long-term cost of operating hardware. We are going to quantify optimisation costs by estimating the energy consumed by a CPU doing the quantum circuit optimisation. We use QUANTIFY, a tool based on Google Cirq, to optimise bucket brigade QRAM and multiplication circuits having between 32 and 8,192 qubits. Although our classical optimisation methods have polynomial complexity, we observe that their energy cost grows extremely fast with the number of qubits. We profile the methods and software and provide evidence that there are high constant costs associated to the operations performed during optimisation. The costs are the result of dynamically typed programming languages and the generic data structures used in the background. We conclude that state-of-the-art quantum software frameworks have to massively improve their scalability to be practical for large circuits.

Traditional replication protocols that arrange logically the replicas into a tree structure have reasonable availability, low communication costs but induce high system load. We propose in this paper the arbitrary protocol: a tree-based replica control protocol that can be configured based on the frequencies of read and write operations in order to provide lower system load than existing tree replication protocols, yet with comparable cost and availability. Our protocol enables the shifting from one configuration into another by just modifying the structure of the tree. There is no need to implement a new protocol whenever the frequencies of read and write operations change. At the heart of our protocol lies the new idea of logical and physical levels in a tree. In short, read operations are carried out on any physical node of every physical level of the tree whereas the write operation is performed on all physical nodes of a single physical level of the tree. We discuss optimal configurations, proving in particular a new lower bound, of independent interest, for the case of a binary tree.