Explicit integration with GPU acceleration for large kinetic networks

Explicit Integration with GPU Acceleration for Large Kinetic Networks

We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability t...

GPU-acceleration for Large-scale Tree Boosting

In this paper, we present a novel massively parallel algorithm for accelerating the decision tree building procedure on GPUs (Graphics Processing Units), which is a crucial step in Gradient Boosted Decision Tree (GBDT) and random forests training. Previous GPU based tree building algorithms are based on parallel multiscan or radix sort to find the exact tree split, and thus suffer from scalabil...

GPU Acceleration and Interactive Visualization for Spatio-Temporal Networks

Streaming parallel GPU acceleration of large-scale filter-based spiking neural networks.

The arrival of graphics processing (GPU) cards suitable for massively parallel computing promises affordable large-scale neural network simulation previously only available at supercomputing facilities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of magnitude, the challenge is to develop fine-grained parallel algorithms to fully exploit the particulars of GP...

LHCb GPU acceleration project

The LHCb detector is due to be upgraded for processing high-luminosity collisions, which will increase data bandwidth to the event filter farm from 100GB/s to 4 TB/s, encouraging us to look for new ways of accelerating Online reconstruction. The Coprocessor Manager is a new framework for integrating LHCb’s existing computation pipelines with massively parallel algorithms running on GPUs and oth...