17 citations · 47 across the 9 of their papers we have counts for
4 papers · 1 filter
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…