Esteban Ginez
I am a software engineer living in Seattle, with extensive experience at Amazon working on devices and cloud computing. At Oracle, I worked on developer tooling and novel compiler research for Java. Currently, I apply my classical computing and compiler expertise to quantum computing infrastructure challenges at Q-CTRL, where I oversee the technical direction of the quantum computing teams.
Session
Traditional subgraph isomorphism algorithms like VF2 rely on sequential tree-search that can't leverage parallel computing. This talk introduces Δ-Motif, a data-centric approach that transforms graph matching into data operations using Python's data science stack.
Δ-Motif decomposes graphs into small "motifs" to reconstruct matches. By representing graphs as tabular data with RAPIDS cuDF and Pandas, we achieve 10-595X speedups over VF2 without custom GPU kernels.
I'll demonstrate practical applications from social networks to quantum computing, and show when GPU acceleration provides the biggest benefits for graph analysis problems. Perfect for data scientists working with network analysis, recommendation systems, or pattern matching at scale