Grid: A next generation data parallel C++ QCD library
December 10, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Peter Boyle, Azusa Yamaguchi, Guido Cossu, Antonin Portelli
arXiv ID
1512.03487
Category
hep-lat
Cross-listed
cs.DC,
cs.MS
Citations
151
Venue
arXiv.org
Last Checked
3 months ago
Abstract
In this proceedings we discuss the motivation, implementation details, and performance of a new physics code base called Grid. It is intended to be more performant, more general, but similar in spirit to QDP++\cite{QDP}. Our approach is to engineer the basic type system to be consistently fast, rather than bolt on a few optimised routines, and we are attempt to write all our optimised routines directly in the Grid framework. It is hoped this will deliver best known practice performance across the next generation of supercomputers, which will provide programming challenges to traditional scalar codes. We illustrate the programming patterns used to implement our goals, and advances in productivity that have been enabled by using new features in C++11.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β hep-lat
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Aspects of scaling and scalability for flow-based sampling of lattice QCD
R.I.P.
π»
Ghosted
Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation
R.I.P.
π»
Ghosted
Job Management and Task Bundling
R.I.P.
π»
Ghosted
Simulating the weak death of the neutron in a femtoscale universe with near-Exascale computing
R.I.P.
π»
Ghosted
Towards meaningful physics from generative models
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted