9 citations · 15 across the 8 of their papers we have counts for
9 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…
Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding
Edoardo Ramalli, Alberto Parravicini, Guido Walter Di Donato +3
Drug repurposing is more relevant than ever due to drug development's rising costs and the need to respond to emerging diseases quickly. Knowledge graph embedding enables drug repu…
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…
Scaling up HBM Efficiency of Top-K SpMV for Approximate Embedding Similarity on FPGAs
Alberto Parravicini, Luca Giuseppe Cellamare, Marco Siracusa +1
Top-K SpMV is a key component of similarity-search on sparse embeddings. This sparse workload does not perform well on general-purpose NUMA systems that employ traditional caching…
DAG-based Scheduling with Resource Sharing for Multi-task Applications in a Polyglot GPU Runtime
Alberto Parravicini, Arnaud Delamare, Marco Arnaboldi +1
GPUs are readily available in cloud computing and personal devices, but their use for data processing acceleration has been slowed down by their limited integration with common pro…
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…
LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment
Alberto Zeni, Giulia Guidi, Marquita Ellis +6
Pairwise sequence alignment is one of the most computationally intensive kernels in genomic data analysis, accounting for more than 90% of the runtime for key bioinformatics applic…
A Framework For Identifying Group Behavior Of Wild Animals
Guido Muscioni, Riccardo Pressiani, Matteo Foglio +3
Activity recognition and, more generally, behavior inference tasks are gaining a lot of interest. Much of it is work in the context of human behavior. New available tracking techno…
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Yunseong Lee, Alberto Scolari, Byung-Gon Chun +3
Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction ser…