Using Hierarchical Parallelism to Accelerate the Solution of Many Small Partial Differential Equations

05/05/2023
by   Jacob Merson, et al.
0

This paper presents efforts to improve the hierarchical parallelism of a two scale simulation code. Two methods to improve the GPU parallel performance were developed and compared. The first used the NVIDIA Multi-Process Service and the second moved the entire sub-problem loop into a single kernel using Kokkos hierarchical parallelism and a PackedView data structure. Both approaches improved parallel performance with the second method providing the greatest improvements.

READ FULL TEXT
research
12/02/2021

Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks

The Neural network-based approach to solving partial differential equati...
research
04/26/2023

Acceleration for Timing-Aware Gate-Level Logic Simulation with One-Pass GPU Parallelism

Witnessing the advancing scale and complexity of chip design and benefit...
research
03/27/2013

Evidential Reasoning in Parallel Hierarchical Vision Programs

This paper presents an efficient adaptation and application of the Demps...
research
10/20/2021

Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning

We present a novel characterization of the mapping of multiple paralleli...
research
11/20/2017

A Reliability Study of Parallelized VNF Chaining

In this paper, we study end-to-end service reliability in Data Center Ne...
research
02/02/2022

Accelerated Quality-Diversity for Robotics through Massive Parallelism

Quality-Diversity (QD) algorithms are a well-known approach to generate ...
research
07/10/2017

Exploiting Parallelism in Optical Network Systems: A Case Study of Random Linear Network Coding (RLNC) in Ethernet-over-Optical Networks

As parallelism becomes critically important in the semiconductor technol...

Please sign up or login with your details

Forgot password? Click here to reset