Abstract: Efficient graph processing is essential for a wide range of applications. Scalability and memory access patterns are still a challenge, especially with the Breadth-First Search algorithm.

Explore core physics concepts and graphing techniques in Python Physics Lesson 3! In this tutorial, we show you how to use Python to visualize physical phenomena, analyze data, and better understand ...

PythoC lets you use Python as a C code generator, but with more features and flexibility than Cython provides. Here’s a first look at the new C code generator for Python. Python and C share more than ...

Mock embeddings for testing and demonstration DJL-based local models (e.g., Qwen3-Embedding-0.6B) HTTP-based remote API services ...

Sometimes, reading Python code just isn’t enough to see what’s really going on. You can stare at lines for hours and still miss how variables change, or why a bug keeps popping up. That’s where a ...

In this tutorial, we implement an advanced graph-based AI agent using the GraphAgent framework and the Gemini 1.5 Flash model. We define a directed graph of nodes, each responsible for a specific ...

Given a graph, determine the distances from the start node to each of its descendants and return the list in node number order, ascending.

Abstract: This paper presents challenges encountered while parallelizing an existing sequential algorithm. A breadth-first search implementation in CUDA C++ of quadratic time complexity is used. Even ...