17 citations · 47 across the 9 of their papers we have counts for
12 papers
Scaling Expected Force: Efficient Identification of Key Nodes in Network-based Epidemic Models
Paolo Sylos Labini, Andrej Jurco, Matteo Ceccarello +3
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, Expect…
Multi-GPU aggregation-based AMG preconditioner for iterative linear solvers
Massimo Bernaschi, Alessandro Celestini, Pasqua D'Ambra +1
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…
Towards a learning-based performance modeling for accelerating Deep Neural Networks
Damiano Perri, Paolo Sylos Labini, Osvaldo Gervasi +2
Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs us…
ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations
Maciej Besta, Cesare Miglioli, Paolo Sylos Labini +11
Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enab…
Asynchronous Distributed-Memory Triangle Counting and LCC with RMA Caching
András Strausz, Flavio Vella, Salvatore Di Girolamo +2
Triangle count and local clustering coefficient are two core metrics for graph analysis. They find broad application in analyses such as community detection and link recommendation…
Blocking Techniques for Sparse Matrix Multiplication on Tensor Accelerators
Paolo Sylos Labini, Massimo Bernaschi, Francesco Silvestri +1
Tensor accelerators have gained popularity because they provide a cheap and efficient solution for speeding up computational-expensive tasks in Deep Learning and, more recently, in…
GPU-based parallelism for ASP-solving
Agostino Dovier, Andrea Formisano, Flavio Vella
Answer Set Programming (ASP) has become, the paradigm of choice in the field of logic programming and non-monotonic reasoning. Thanks to the availability of efficient solvers, ASP…
A Computational Model for Tensor Core Units
Rezaul Chowdhury, Francesco Silvestri, Flavio Vella
To respond to the need of efficient training and inference of deep neural networks, a plethora of domain-specific hardware architectures have been introduced, such as Google Tensor…
A model-driven approach for a new generation of adaptive libraries
Marco Cianfriglia, Flavio Vella, Cedric Nugteren +2
Efficient high-performance libraries often expose multiple tunable parameters to provide highly optimized routines. These can range from simple loop unroll factors or vector sizes…
Accelerating Energy Games Solvers on Modern Architectures
Andrea Formisano, Raffaella Gentilini, Flavio Vella
Quantitative games, where quantitative objectives are defined on weighted game arenas, provide natural tools for designing faithful models of embedded controllers. Instances of the…
Scaling betweenness centrality using communication-efficient sparse matrix multiplication
Edgar Solomonik, Maciej Besta, Flavio Vella +1
Betweenness centrality (BC) is a crucial graph problem that measures the significance of a vertex by the number of shortest paths leading through it. We propose Maximal Frontier Be…
Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems
Flavio Vella, Giancarlo Carbone, Massimo Bernaschi
Betweenness Centrality (BC) is steadily growing in popularity as a metrics of the influence of a vertex in a graph. The BC score of a vertex is proportional to the number of all-pa…