Zobrazeno 1 - 10
of 45
pro vyhledávání: '"VELLA, FLAVIO"'
Autor:
Unat, Didem, Turimbetov, Ilyas, Issa, Mohammed Kefah Taha, Sağbili, Doğan, Vella, Flavio, De Sensi, Daniele, Ismayilov, Ismayil
In recent years, GPUs have become the preferred accelerators for HPC and ML applications due to their parallelism and fast memory bandwidth. While GPUs boost computation, inter-GPU communication can create scalability bottlenecks, especially as the n
Externí odkaz:
http://arxiv.org/abs/2409.09874
Autor:
De Sensi, Daniele, Pichetti, Lorenzo, Vella, Flavio, De Matteis, Tiziano, Ren, Zebin, Fusco, Luigi, Turisini, Matteo, Cesarini, Daniele, Lust, Kurt, Trivedi, Animesh, Roweth, Duncan, Spiga, Filippo, Di Girolamo, Salvatore, Hoefler, Torsten
Publikováno v:
Published in Proceedings of The International Conference for High Performance Computing Networking, Storage, and Analysis (SC '24) (2024)
Multi-GPU nodes are increasingly common in the rapidly evolving landscape of exascale supercomputers. On these systems, GPUs on the same node are connected through dedicated networks, with bandwidths up to a few terabits per second. However, gauging
Externí odkaz:
http://arxiv.org/abs/2408.14090
Autor:
Okanovic, Patrik, Kwasniewski, Grzegorz, Labini, Paolo Sylos, Besta, Maciej, Vella, Flavio, Hoefler, Torsten
High-performance sparse matrix-matrix (SpMM) multiplication is paramount for science and industry, as the ever-increasing sizes of data prohibit using dense data structures. Yet, existing hardware, such as Tensor Cores (TC), is ill-suited for SpMM, a
Externí odkaz:
http://arxiv.org/abs/2408.11551
Autor:
Giusto, Edoardo, Nuñez-Corrales, Santiago, Cao, Phuong, Cilardo, Alessandro, Iyer, Ravishankar K., Jiang, Weiwen, Rech, Paolo, Vella, Flavio, Montrucchio, Bartolomeo, Dasgupta, Samudra, Humble, Travis S.
Quantum Computing (QC) offers the potential to enhance traditional High-Performance Computing (HPC) workloads by leveraging the unique properties of quantum computers, leading to the emergence of a new paradigm: HPC-QC. While this integration present
Externí odkaz:
http://arxiv.org/abs/2408.10484
This paper introduces cuVegas, a CUDA-based implementation of the Vegas Enhanced Algorithm (VEGAS+), optimized for multi-dimensional integration in GPU environments. The VEGAS+ algorithm is an advanced form of Monte Carlo integration, recognized for
Externí odkaz:
http://arxiv.org/abs/2408.09229
Reliability is fundamental for developing large-scale quantum computers. Since the benefit of technological advancements to the qubit's stability is saturating, algorithmic solutions, such as quantum error correction (QEC) codes, are needed to bridge
Externí odkaz:
http://arxiv.org/abs/2407.10841
We propose Gauss-Newton's method in function space for the solution of the Navier-Stokes equations in the physics-informed neural network (PINN) framework. Upon discretization, this yields a natural gradient method that provably mimics the function s
Externí odkaz:
http://arxiv.org/abs/2402.10680
The frontier of quantum computing (QC) simulation on classical hardware is quickly reaching the hard scalability limits for computational feasibility. Nonetheless, there is still a need to simulate large quantum systems classically, as the Noisy Inte
Externí odkaz:
http://arxiv.org/abs/2401.06188
Autor:
Labini, Paolo Sylos, Jurco, Andrej, Ceccarello, Matteo, Guarino, Stefano, Mastrostefano, Enrico, Vella, Flavio
Centrality measures are fundamental tools of network analysis as they highlight the key actors within the network. This study focuses on a newly proposed centrality measure, Expected Force (EF), and its use in identifying spreaders in network-based e
Externí odkaz:
http://arxiv.org/abs/2306.00606
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems (2023)
We present and release in open source format a sparse linear solver which efficiently exploits heterogeneous parallel computers. The solver can be easily integrated into scientific applications that need to solve large and sparse linear systems on mo
Externí odkaz:
http://arxiv.org/abs/2303.02352