17 citations · 32 across the 6 of their papers we have counts for
6 papers
High Performance Unstructured SpMM Computation Using Tensor Cores
Patrik Okanovic, Grzegorz Kwasniewski, Paolo Sylos Labini +3
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, e…
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…
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…
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…
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…