Abstract: Many graph-based algorithms in high performance computing (HPC) use approximate solutions due to having algorithms that are computationally expensive or serial in nature. Neural acceleration ...

This repository contains all the resources used for the design and evaluation of an MLIR-based FPGA toolchain for Graph Neural Network acceleration using High-Level Synthesis. This work represents the ...

On the algorithm level, GCoD integrates a split and conquer training strategy to polarize the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, ...

NVIDIA introduces GPU acceleration for NetworkX using cuGraph, offering significant speed improvements in graph analytics without code changes, ideal for large-scale data processing. NVIDIA has ...

The gradient of a displacement-time graph at a particular time gives the velocity of the object at that time. The gradient of a velocity-time graph at a particular time gives the acceleration of the ...