Cristóbal A. Navarro

Enzo Meneses, Cristóbal A. Navarro, Héctor Ferrada et al.

Recent research on ray tracing cores has explored repurposing these cores to solve non-graphical problems by reformulating them as geometric queries, leveraging the inherent parallelism of ray tracing. Although successful in specific cases, these applications lack a clear pattern, and the conditions under which RT cores can provide computational benefits are still not clearly understood. The objective of this literature review is to examine diverse applications of ray tracing cores in general-purpose computation, identifying common features, performance gains, and limitations. By categorizing these efforts, the review aims to provide guidance on the types of problems that can effectively exploit ray tracing hardware beyond traditional rendering tasks. This is achieved with a blibliometric review based on 59 research articles indexed in Scopus, and a systematic literature review on 35 of them which propose new RT solutions and compare them with state-of-the-art methods to solve 32 distinct problems, in some works achieving up to $200\times$ speedup. Most of the problems analyzed in this work have applications in physics simulations and in solving some geometric queries, but problems with potential applications in databases and AI can also be found. Analyzing the characteristics of the problems, it was found that nearest neighbor search, including its variants, benefit the most from ray tracing cores as well as problems that rely on heuristic to diminish the necessary work. This is aligned with the biggest strength of RT cores; discarding tree branches when traversing a tree to avoid unnecessary work. Also, it was found that many short-length rays should be preferred over a few large rays. The results found in this work can serve as a guide for knowing beforehand which applications are better potential candidates to benefit from RT Core computation.

Felipe A. Quezada, Carlos F. Navarro, Cristian Muñoz et al.

Vision Transformers (ViTs) have successfully been applied to image classification problems where large annotated datasets are available. On the other hand, when fewer annotations are available, such as in biomedical applications, image augmentation techniques like introducing image variations or combinations have been proposed. However, regarding ViT patch sampling, less has been explored outside grid-based strategies. In this work, we propose Random Vision Transformer Tokens (RaViTT), a random patch sampling strategy that can be incorporated into existing ViTs. We experimentally evaluated RaViTT for image classification, comparing it with a baseline ViT and state-of-the-art (SOTA) augmentation techniques in 4 datasets, including ImageNet-1k and CIFAR-100. Results show that RaViTT increases the accuracy of the baseline in all datasets and outperforms the SOTA augmentation techniques in 3 out of 4 datasets by a significant margin +1.23% to +4.32%. Interestingly, RaViTT accuracy improvements can be achieved even with fewer tokens, thus reducing the computational load of any ViT model for a given accuracy value.

Jose Maureira, Cristóbal A. Navarro, Hector Ferrada et al.

Mapping parallel threads onto non-box-shaped domains is a known challenge in GPU computing; efficient mapping prevents performance penalties from unnecessary resource allocation. Currently, achieving this requires significant analytical human effort to manually derive bespoke mapping functions for each geometry. This work introduces a novel approach leveraging the symbolic reasoning of Large Language Models (LLMs) to automate this derivation entirely through in-context learning. Focusing on state-of-the-art open-weights models, we conducted a rigorous comparative analysis across spatial domains of increasing complexity. Our results demonstrate that modern local LLMs successfully infer exact O(1) and O(log N) mapping equations for complex 2D/3D dense domains and 2D fractals, vastly outperforming traditional symbolic regression methods. Crucially, we profile the energetic viability of this approach on high-performance infrastructure, distinguishing between the code-generation and execution phases. While one-time inference incurs a high energy penalty -- particularly for reasoning-focused models like DeepSeek-R1 -- this is a single upfront investment. Once integrated, the generated analytical kernels eliminate block waste entirely, yielding massive energy and time savings (e.g., up to 4833x speedup and 2890x energy reduction) during actual GPU workloads. Finally, we identify a current "reasoning ceiling" when these models face highly recursive 3D fractals (e.g., the Menger Sponge). This limitation benchmarks the present maturity of open-weight architectures, charting a viable path toward fully automated, energy-efficient GPU resource optimization.

Isidora Hernández, Héctor Ferrada, Cristóbal A. Navarro

The Minimum Set Cover Problem (MSCP) is a classical NP-hard combinatorial optimization problem with numerous applications in science and engineering. Although a wide range of exact, approximate, and metaheuristic approaches have been proposed, most methods implicitly treat MSCP instances as monolithic, overlooking potential intrinsic structural properties of the universe. In this work, we investigate the concept of \emph{universe segmentability} in the MSCP and analyze how intrinsic structural decomposition (universe segmentability) can be exploited to enhance heuristic optimization. We propose an efficient preprocessing strategy based on disjoint-set union (union--find) to detect connected components induced by element co-occurrence within subsets, enabling the decomposition of the original instance into independent subproblems. Each subproblem is solved using the GRASP metaheuristic, and partial solutions are combined without compromising feasibility. Extensive experiments on standard benchmark instances and large-scale synthetic datasets show that exploiting natural universe segmentation consistently improves solution quality and scalability, particularly for large and structurally decomposable instances. These gains are supported by a succinct bit-level set representation that enables efficient set operations, making the proposed approach computationally practical at scale.