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Skel: Generative Software for Producing Skeletal I/O Applications

https://doi.org/10.1109/esciencew.2011.26
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-39953-4_3openalex-snapshot
Generative Programming and Active Libraries
inferredcites →https://doi.org/10.1063/1.2338775openalex-snapshot
Gyro-kinetic simulation of global turbulent transport properties in tokamak experiments
inferredcites →https://doi.org/10.1088/1742-6596/125/1/012035openalex-snapshot
Design issues for numerical libraries on scalable multicore architectures
inferredcites →https://doi.org/10.1109/cluster.2011.18openalex-snapshot
EDO: Improving Read Performance for Scientific Applications through Elastic Data Organization
inferredcites →https://doi.org/10.1109/sc.2010.32openalex-snapshot
Managing Variability in the IO Performance of Petascale Storage Systems
inferredcites →https://doi.org/10.1145/1048935.1050175openalex-snapshot
Grid -Based Parallel Data Streaming implemented for the Gyrokinetic Toroidal Code
inferredcites →https://doi.org/10.1145/1089014.1089021openalex-snapshot
An overview of the Trilinos project
inferredcites →https://doi.org/10.1145/1383529.1383533openalex-snapshot
Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS)
inferredcites →https://doi.org/10.1145/1551609.1551618openalex-snapshot
DataStager
inferredcites →https://doi.org/10.1145/1713072.1713079openalex-snapshot
...and eat it too
inferredcites →https://doi.org/10.1145/1713072.1713080openalex-snapshot
Scalable I/O tracing and analysis
inferredcites →https://doi.org/10.1145/1996130.1996139openalex-snapshot
Six degrees of scientific data
inferredcites →https://doi.org/10.2172/993908openalex-snapshot
Improving performance via mini-applications.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/cpe.3125openalex-snapshot
Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks
inferred← citeshttps://doi.org/10.1007/978-3-642-32820-6_10openalex-snapshot
Understanding I/O Performance Using I/O Skeletal Applications
inferred← citeshttps://doi.org/10.1016/j.future.2013.12.039openalex-snapshot
A case study of large-scale parallel I/O analysis and optimization for numerical weather prediction system
inferred← citeshttps://doi.org/10.1016/j.jpdc.2018.10.004openalex-snapshot
Automatic generation of benchmarks for I/O-intensive parallel applications
inferred← citeshttps://doi.org/10.1109/cluster.2015.37openalex-snapshot
Dynamic Model-Driven Parallel I/O Performance Tuning
inferred← citeshttps://doi.org/10.1109/cluster.2017.30openalex-snapshot
Extending Skel to Support the Development and Optimization of Next Generation I/O Systems
inferred← citeshttps://doi.org/10.1109/cse.2013.24openalex-snapshot
Performance Impact of I/O on QMCPack Simulations at the Petascale and Beyond
inferred← citeshttps://doi.org/10.1109/escience.2019.00037openalex-snapshot
Scalable Performance Awareness for In Situ Scientific Applications
inferred← citeshttps://doi.org/10.1109/icdcs.2019.00179openalex-snapshot
A Vision for Managing Extreme-Scale Data Hoards
inferred← citeshttps://doi.org/10.1109/pdsw-discs.2016.007openalex-snapshot
Replicating HPC I/O Workloads with Proxy Applications
inferred← citeshttps://doi.org/10.1109/pdsw.2014.6openalex-snapshot
Automatic Generation of I/O Kernels for HPC Applications
inferred← citeshttps://doi.org/10.1145/3309205openalex-snapshot
Optimizing I/O Performance of HPC Applications with Autotuning
inferred← citeshttps://doi.org/10.5555/2688400.2688409openalex-snapshot
Automatic generation of I/O kernels for HPC applications
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