Akshita Sure
I’m Akshita, a Computer Science undergraduate at IIIT Gwalior, and I’m really passionate about building systems that are both technically strong and meaningful in impact. A big part of my journey has been through open source. I also enjoy competitive programming and problem-solving. I’m particularly drawn to spaces where I can learn deeply, collaborate with others, and contribute meaningfully. Beyond tech, I’ve trained in Carnatic music for over 10 years and have won several competitions. Overall, I see myself as someone who takes initiative, brings clarity to a team, and is constantly looking to grow — not just as an engineer, but as someone who can create impact and uplift others along the way :)
She/Her
IIIT Gwalior
BTech student
Session
Hi! Have you ever run a NetworkX algorithm on a large graph and watched it take… longer than you expected? You look at your machine, see all those CPU cores sitting idle, and wonder — shouldn’t this be faster?
NetworkX is one of the most widely used graph analysis libraries in Python. But as the graph sizes become more realistic and huge, the performance becomes a bottleneck. So what if we could make NetworkX faster — without rewriting it in C, and without giving up its philosophy?
In this talk, I’ll introduce nx-parallel, a backend that brings multi-core parallelism to NetworkX algorithms with the help of Joblib. But parallelism isn’t just a magic switch you turn on. We’ll dig into what actually makes a graph algorithm embarrassingly parallel, why only certain algorithms qualify, and how design decisions determine whether parallelism truly scales.