2 citations · 3 across the 3 of their papers we have counts for
5 papers
A Mixed Precision, Multi-GPU Design for Large-scale Top-K Sparse Eigenproblems
Francesco Sgherzi, Alberto Parravicini, Marco Domenico Santambrogio
Graph analytics techniques based on spectral methods process extremely large sparse matrices with millions or even billions of non-zero values. Behind these algorithms lies the Top…
Solving Large Top-K Graph Eigenproblems with a Memory and Compute-optimized FPGA Design
Francesco Sgherzi, Alberto Parravicini, Marco Siracusa +1
Large-scale eigenvalue computations on sparse matrices are a key component of graph analytics techniques based on spectral methods. In such applications, an exhaustive computation…
A reduced-precision streaming SpMV architecture for Personalized PageRank on FPGA
Alberto Parravicini, Francesco Sgherzi, Marco D. Santambrogio
Sparse matrix-vector multiplication is often employed in many data-analytic workloads in which low latency and high throughput are more valuable than exact numerical convergence. F…
Learning Lines with Ordinal Constraints
Bohan Fan, Diego Ihara Centurion, Neshat Mohammadi +3
We study the problem of finding a mapping from a set of points into the real line, under ordinal triple constraints. An ordinal constraint for a triple of points asse…
Robust Mahalanobis Metric Learning via Geometric Approximation Algorithms
Diego Ihara, Neshat Mohammadi, Francesco Sgherzi +1
Learning Mahalanobis metric spaces is an important problem that has found numerous applications. Several algorithms have been designed for this problem, including Information Theor…