R.I.P.
π»
Ghosted
Job Management and Task Bundling
October 05, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Evan Berkowitz, Gustav R. Jansen, Kenneth McElvain, AndrΓ© Walker-Loud
arXiv ID
1710.01986
Category
hep-lat
Cross-listed
cs.DC,
physics.comp-ph
Citations
18
Venue
arXiv.org
Last Checked
3 months ago
Abstract
High Performance Computing is often performed on scarce and shared computing resources. To ensure computers are used to their full capacity, administrators often incentivize large workloads that are not possible on smaller systems. Measurements in Lattice QCD frequently do not scale to machine-size workloads. By bundling tasks together we can create large jobs suitable for gigantic partitions. We discuss METAQ and mpi_jm, software developed to dynamically group computational tasks together, that can intelligently backfill to consume idle time without substantial changes to users' current workflows or executables.
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
Lattice gauge equivariant convolutional neural networks
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
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