Tootfinder

Decentralised self-organisation of pivoting cube ensembles using geometric deep learning
Nadezhda Dobreva, Emmanuel Blazquez, Jai Grover, Dario Izzo, Yuzhen Qin, Dominik Dold
https://arxiv.org/abs/2509.03140

Decentralised self-organisation of pivoting cube ensembles using geometric deep learning
We present a decentralized model for autonomous reconfiguration of homogeneous pivoting cube modular robots in two dimensions. Each cube in the ensemble is controlled by a neural network that only gains information from other cubes in its local neighborhood, trained using reinforcement learning. Furthermore, using geometric deep learning, we include the grid symmetries of the cube ensemble in the neural network architecture. We find that even the most localized versions succeed in reconfiguring…

Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE
Akash Poptani, Alireza Khadem, Scott Mahlke, Jonah Miller, Joshua Dolence, Reetuparna Das
https://arxiv.org/abs/2509.19701

Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE
Hero-class HPC simulations rely on Adaptive Mesh Refinement (AMR) to reduce compute and memory demands while maintaining accuracy. This work analyzes the performance of Parthenon, a block-structured AMR benchmark, on CPU-GPU systems. We show that smaller mesh blocks and deeper AMR levels degrade GPU performance due to increased communication, serial overheads, and inefficient GPU utilization. Through detailed profiling, we identify inefficiencies, low occupancy, and memory access bottlenecks. W…