A degree gets you in the door, but data-driven career prep keeps you in the room. Don't just graduate; optimize your ...

Logging my full journey of learning DSA with Python — from foundational concepts to advanced topics like dynamic programming and graph algorithms.

Abstract: Pathfinding is widely applied when encountering autonomous driving, mobile robot pathfinding, and so on. Traditional pathfinding algorithms have certain limitations such as high ...

Part of the DynGraphLab — Dynamic Graph Algorithms open source framework. Developed at the Algorithm Engineering Group, Heidelberg University. Python Interface: An easy-to-use Python interface for ...

In recent years, the Massively Parallel Computation (MPC) model has gained significant attention. However, most of distributed and parallel graph algorithms in the MPC model are designed for static ...

Abstract: In previous work, algorithms have been decomposed into basic algorithmic components and then recomposed into brand new algorithms using a genetic algorithm. The algorithm is composed in full ...

Presented at the 2015 Supercomputing Conference, this paper shows that dynamic parallelism enables relatively high-performance graph algorithms for GPUs. Dynamic parallelism allows GPU kernels to ...

Dynamic programming is a method frequently applied to optimization problems, problems where we are looking for the best solution to a problem. A famous example of an optimization problem is the ...