Alejandro Cano

Alejandro Cano, Cristóbal Camarero, Carmen Martínez et al.

As high-performance computing systems scale in size and complexity, efficient resource management is essential to minimize communication overhead. The HyperX is a richly connected, low-diameter network that offers a scalable and cost-effective alternative to traditional topologies. However, resource allocation in HyperX remains underexplored, and strategies designed for networks like Torus, Fat-tree, or Dragonfly do not directly transfer. In this work, we propose and formalize several resource allocation strategies for HyperX networks, categorized into linear, geometric, and stochastic functions. We characterize these strategies theoretically by analyzing their topological properties, including dilation, convexity, and partition bandwidth.Furthermore, we conduct an exhaustive experimental evaluation using synthetic traffic and application communication kernels to assess the impact of these strategies on performance under different routing algorithms. Our results indicate that partition bandwidth and switch locality are decisive factors in mitigating interferences. Notably, the Diagonal allocation strategy, which is not convex, consistently outperforms traditional approaches in most scenarios. Finally, we provide a set of lessons learned to guide the implementation of resource allocation policies in HPC systems based on HyperX networks.

Alejandro Cano, Cristina Brinza, Cristóbal Camarero et al.

Extreme-scale data centers are the backbone of next-generation computing, enabling breakthroughs in science, artificial intelligence, and global innovation through unprecedented processing power and scalability. This work examines leaf-spine network topologies that offer extreme scalability--connecting a vast number of endpoints--while delivering strong performance at low cost. It takes as a starting point two alternatives to the widely used Fat-Tree topology: the Orthogonal Fat-Tree and the Random Folded Clos. The resulting Multipass Random Leaf-Spine (MRLS) networks inherit their advantages and surpass Fat-Trees in both throughput and flexibility. To fully leverage the topological properties of these networks, various non-minimal routing strategies are considered. An exhaustive evaluation using an interconnection network simulator provides insight into the trade-offs and scalability of these topologies under realistic conditions, positioning them as a promising solution for extreme-scale systems. The MRLS achieves a 50% speedup against a Fat-Tree for an All2All collective comprising 100k endpoints, and 100% against Dragonfly networks for the same collective.

Antonio López Martínez, Alejandro Cano, Antonio Ruiz-Martínez

The advent of Generative Artificial Intelligence (GenAI) has brought a significant change to our society. GenAI can be applied across numerous fields, with particular relevance in cybersecurity. Among the various areas of application, its use in penetration testing (pentesting) or ethical hacking processes is of special interest. In this paper, we have analyzed the potential of leading generic-purpose GenAI tools-Claude Opus, GPT-4 from ChatGPT, and Copilot-in augmenting the penetration testing process as defined by the Penetration Testing Execution Standard (PTES). Our analysis involved evaluating each tool across all PTES phases within a controlled virtualized environment. The findings reveal that, while these tools cannot fully automate the pentesting process, they provide substantial support by enhancing efficiency and effectiveness in specific tasks. Notably, all tools demonstrated utility; however, Claude Opus consistently outperformed the others in our experimental scenarios.