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Quantitative Performance Assessment of Proxy Apps and Parents

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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

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

inferredcites →https://doi.org/10.1016/j.jpdc.2014.07.003openalex-snapshot
Kokkos: Enabling manycore performance portability through polymorphic memory access patterns
inferredcites →https://doi.org/10.1109/ccgrid.2012.123openalex-snapshot
Performance Modeling and Comparative Analysis of the MILC Lattice QCD Application su3_rmd
inferredcites →https://doi.org/10.1109/co-hpc.2014.11openalex-snapshot
Application Characterization Using Oxbow Toolkit and PADS Infrastructure
inferredcites →https://doi.org/10.1109/icctac.2017.8249996openalex-snapshot
Application of citation network analysis for improved similarity index estimation of legal case documents : A study
inferredcites →https://doi.org/10.1109/pmbs49563.2019.00016openalex-snapshot
Fine-Grained Analysis of Communication Similarity between Real and Proxy Applications
inferredcites →https://doi.org/10.1109/sc.2014.18openalex-snapshot
The Lightweight Distributed Metric Service: A Scalable Infrastructure for Continuous Monitoring of Large Scale Computing Systems and Applications
inferredcites →https://doi.org/10.1177/1094342013492179openalex-snapshot
Multi-core performance studies of a Monte Carlo neutron transport code

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-030-28596-8_21openalex-snapshot
Cache Line Sharing and Communication in ECP Proxy Applications
inferred← citeshttps://doi.org/10.1007/978-3-030-50371-0_19openalex-snapshot
heFFTe: Highly Efficient FFT for Exascale
inferred← citeshttps://doi.org/10.1109/escience55777.2022.00032openalex-snapshot
SIM-SITU: A Framework for the Faithful Simulation of in situ Processing
inferred← citeshttps://doi.org/10.1109/exampi49596.2019.00007openalex-snapshot
Impacts of Multi-GPU MPI Collective Communications on Large FFT Computation
inferred← citeshttps://doi.org/10.1109/exampi54564.2021.00011openalex-snapshot
Accelerating Multi - Process Communication for Parallel 3-D FFT
inferred← citeshttps://doi.org/10.1109/hipc.2019.00044openalex-snapshot
Evaluating the Impact of Energy Efficient Networks on HPC Workloads
inferred← citeshttps://doi.org/10.1109/hipc62374.2024.00011openalex-snapshot
HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach
inferred← citeshttps://doi.org/10.1109/iiswc66894.2025.00036openalex-snapshot
miniGiraffe: A Pangenomic Mapping Proxy App
inferred← citeshttps://doi.org/10.1109/isca52012.2021.00031openalex-snapshot
SATORI: Efficient and Fair Resource Partitioning by Sacrificing Short-Term Benefits for Long-Term Gains
inferred← citeshttps://doi.org/10.1145/3453483.3454109openalex-snapshot
Bliss: auto-tuning complex applications using a pool of diverse lightweight learning models
inferred← citeshttps://doi.org/10.1145/3581784.3607069openalex-snapshot
Application Performance Modeling via Tensor Completion
inferred← citeshttps://doi.org/10.1145/3605573.3605578openalex-snapshot
CoTuner: A Hierarchical Learning Framework for Coordinately Optimizing Resource Partitioning and Parameter Tuning
inferred← citeshttps://doi.org/10.1177/10943420211029302openalex-snapshot
Co-design Center for Exascale Machine Learning Technologies (ExaLearn)
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