arXiv subjects

Roberto Carrasco

Publications and source records attributed to Roberto Carrasco.

7 recordsLinked to original sources

arXiv2026

Convex Hull 3D Filtering with GPU Ray Tracing and Tensor Cores

In recent years, applications such as real-time simulations, autonomous systems, and video games increasingly demand the processing of complex geometric models under stringent time constraints. Traditional geometric algorithms, including the convex hull, are subject to these challenges. A common approach to improve performance is scaling computational resources, which often results in higher energy consumption. Given the growing global concern regarding sustainable use of energy, this becomes a critical limitation. This work presents a 3D preprocessing filter for the convex hull algorithm using ray tracing and tensor core technologies. The filter builds a delimiter polyhedron based on Manhattan distances that discards points from the original set. The filter is evaluated on two point distributions: uniform and sphere. Experimental results show that the proposed filter, combined with convex hull construction, accelerates the computation of the 3D convex hull by up to 200x with respect to a CPU parallel implementation. This research demonstrates that geometric algorithms can be accelerated through massive parallelism while maintaining efficient energy utilization. Beyond execution time and speedup evaluation, we also analyze GPU energy consumption, showing that the proposed preprocessing filter not only reduces the computational workload but also achieves performance gains with controlled energy usage. These results highlight the dual benefit of the method in terms of both speed and energy efficiency, reinforcing its applicability in modern high-performance scenarios.

cs.CG↗

arXiv2025

Ultra-long-range spin coupling in graphene revealed by atomically resolved spin excitations

Magnetic interactions between localized spins-1/2 play a central role in quantum magnetism, spin-based quantum computing, and quantum simulation. The range and strength of these interactions are key figures of merit. Here, we probe exchange interactions in pairs and trimers of spins-1/2 introduced by chemisorption of individual hydrogen atoms on graphene. Using scanning tunneling microscopy and inelastic electron tunneling spectroscopy, supported by large-scale mean-field Hubbard calculations, we demonstrate 3 meV exchange couplings at separations beyond 10 nm, surpassing all prior systems. The couplings can be ferro- or antiferromagnetic depending on the relative sublattice arrangement. Real-space mapping of spin excitation amplitudes enables characterization with atomic-resolution. Through atomic manipulation we extend this control to spin trimers, revealing collective spin excitations when pairwise exchange couplings are comparable.

cond-mat.mes-hall↗

arXiv2025

Twistraintronics in Square Moire Superlattices of Stacked Graphene Layers

We report the first observation of controlled, strain-induced square moire patterns in stacked graphene. By selectively displacing native wrinkles, we drive a reversible transition from the usual trigonal to square moire order. Scanning tunneling microscopy reveals elliptically shaped AA domains, while spectroscopy shows strong electronic correlation in the form of narrow bands with split Van Hove singularities near the Fermi level. A continuum model with electrostatic interactions reproduces these features under the specific twist-strain combination that minimizes elastic energy. This work demonstrates that the combination of twist and strain, or twistraintronics, enables the realization of highly correlated electronic states in moire heterostructures with geometries that were previously inaccessible.

cond-mat.mes-hall↗

arXiv2024

GPolylla: Fully GPU-accelerated polygonal mesh generator

This work presents a fully GPU-accelerated algorithm for the polygonal mesh generator known as Polylla. Polylla is a tri-to-polygon mesh generator, which benefits from the half-edge data structure to manage any polygonal shape. The proposed parallel algorithm introduces a novel approach to modify triangulations to get polygonal meshes using the half-edge data structure in parallel on the GPU. By changing the adjacency values of each half-edge, the algorithm accomplish to unlink half-edges that are not used in the new polygonal mesh without the need neither removing nor allocating new memory in the GPU. The experimental results show a speedup, reaching up to $\times 83.2$ when compared to the CPU sequential implementation. Additionally, the speedup is $\times 746.8$ when the cost of copying the data structure from the host device and back is not included.

cs.GR↗

arXiv2023

An Evaluation of GPU Filters for Accelerating the 2D Convex Hull

The Convex Hull algorithm is one of the most important algorithms in computational geometry, with many applications such as in computer graphics, robotics, and data mining. Despite the advances in the new algorithms in this area, it is often needed to improve the performance to solve more significant problems quickly or in real-time processing. This work presents an experimental evaluation of GPU filters to reduce the cost of computing the 2D convex hull. The technique first performs a preprocessing of the input set, filtering all points within an eight-vertex polygon in logarithmic time, to obtain a reduced set of candidate points. We use parallel computation and the use of the Manhattan distance as a metric to find the vertices of the polygon and perform the point filtering. For the filtering stage we study different approaches; from custom CUDA kernels to libraries such as Thrust and CUB. Three types of point distributions are tested: a normal distribution (favorable case), circumference (the worst case), and a case where points are shifted randomly from the circumference (intermediate case). Experimental evaluation shows that the GPU filtering algorithm can be up to 23x faster than a sequential CPU implementation, and the whole convex hull computation can be up to 30x faster than the fastest implementation provided by the CGAL library.

cs.DC↗

arXiv2020

GPU Tensor Cores for fast Arithmetic Reductions

This work proposes a GPU tensor core approach that encodes the arithmetic reduction of $n$ numbers as a set of chained $m \times m$ matrix multiply accumulate (MMA) operations executed in parallel by GPU tensor cores. The asymptotic running time of the proposed chained tensor core approach is $T(n)=5 log_{m^2}{n}$ and its speedup is $S=\dfrac{4}{5} log_{2}{m^2}$ over the classic $O(n \log n)$ parallel reduction algorithm. Experimental performance results show that the proposed reduction method is $\sim 3.2 \times$ faster than a conventional GPU reduction implementation, and preserves the numerical precision because the sub-results of each chain of $R$ MMAs is kept as a 32-bit floating point value, before being all reduced into as a final 32-bit result. The chained MMA design allows a flexible configuration of thread-blocks; small thread-blocks of 32 or 128 threads can still achieve maximum performance using a chain of $R=4,5$ MMAs per block, while large thread-blocks work best with $R=1$. The results obtained in this work show that tensor cores can indeed provide a significant performance improvement to non-Machine Learning applications such as the arithmetic reduction, which is an integration tool for studying many scientific phenomena.

cs.DC↗

arXiv2019

Analyzing GPU Tensor Core Potential for Fast Reductions

The Nvidia GPU architecture has introduced new computing elements such as the \textit{tensor cores}, which are special processing units dedicated to perform fast matrix-multiply-accumulate (MMA) operations and accelerate \textit{Deep Learning} applications. In this work we present the idea of using tensor cores for a different purpose such as the parallel arithmetic reduction problem, and propose a new GPU tensor-core based algorithm as well as analyze its potential performance benefits in comparison to a traditional GPU-based one. The proposed method, encodes the reduction of $n$ numbers as a set of $m\times m$ MMA tensor-core operations (for Nvidia's Volta architecture $m=16$) and takes advantage from the fact that each MMA operation takes just one GPU cycle. When analyzing the cost under a simplified GPU computing model, the result is that the new algorithm manages to reduce a problem of $n$ numbers in $T(n) = 5\log_{m^2}(n)$ steps with a speedup of $S = \frac{4}{5}\log_2(m^2)$.

cs.DC↗