On the public citation graph

Exploiting Performance Portability in Search Algorithms for Autotuning

https://doi.org/10.1109/ipdpsw.2016.85
0 signed · 49 inferred · view as graph

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.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/3-540-44905-1_23openalex-snapshot
Compile-Time Based Performance Prediction
inferredcites →https://doi.org/10.1007/978-3-319-17353-5_7openalex-snapshot
Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM
inferredcites →https://doi.org/10.1007/978-3-540-69330-7_10openalex-snapshot
A Language for the Compact Representation of Multiple Program Versions
inferredcites →https://doi.org/10.1007/978-3-642-38718-0_26openalex-snapshot
An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning
inferredcites →https://doi.org/10.1016/j.procs.2012.04.214openalex-snapshot
SPAPT: Search Problems in Automatic Performance Tuning
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1109/cluster.2013.6702683openalex-snapshot
Active-learning-based surrogate models for empirical performance tuning
inferredcites →https://doi.org/10.1109/clustr.2008.4663796openalex-snapshot
Intelligent compilers
inferredcites →https://doi.org/10.1109/clustr.2008.4663803openalex-snapshot
A comparison of search heuristics for empirical code optimization
inferredcites →https://doi.org/10.1109/icpp.2015.106openalex-snapshot
Generating Efficient Tensor Contractions for GPUs
inferredcites →https://doi.org/10.1109/inpar.2012.6339597openalex-snapshot
Policy-based tuning for performance portability and library co-optimization
inferredcites →https://doi.org/10.1109/ipdps.2007.370637openalex-snapshot
POET: Parameterized Optimizations for Empirical Tuning
inferredcites →https://doi.org/10.1109/ipdps.2009.5161004openalex-snapshot
Annotation-based empirical performance tuning using Orio
inferredcites →https://doi.org/10.1109/ipdps.2009.5161054openalex-snapshot
A scalable auto-tuning framework for compiler optimization
inferredcites →https://doi.org/10.1109/ipdps.2014.59openalex-snapshot
Nitro: A Framework for Adaptive Code Variant Tuning
inferredcites →https://doi.org/10.1109/pact.2000.888348openalex-snapshot
Combined selection of tile sizes and unroll factors using iterative compilation
inferredcites →https://doi.org/10.1137/s1052623400369235openalex-snapshot
Warm-Start Strategies in Interior-Point Methods for Linear Programming
inferredcites →https://doi.org/10.1145/1065910.1065921openalex-snapshot
ACME
inferredcites →https://doi.org/10.1145/1065910.1065922openalex-snapshot
Probabilistic source-level optimisation of embedded programs
inferredcites →https://doi.org/10.1145/1070891.1065921openalex-snapshot
ACME
inferredcites →https://doi.org/10.1145/1070891.1065922openalex-snapshot
Probabilistic source-level optimisation of embedded programs
inferredcites →https://doi.org/10.1145/1216993.1217006openalex-snapshot
Model-Based performance prediction with the palladio component model
inferredcites →https://doi.org/10.1145/1454115.1454155openalex-snapshot
A tuning framework for software-managed memory hierarchies
inferredcites →https://doi.org/10.1145/1498765.1498785openalex-snapshot
Roofline
inferredcites →https://doi.org/10.1145/2038698.2038711openalex-snapshot
An evaluation of different modeling techniques for iterative compilation
inferredcites →https://doi.org/10.1145/2063384.2063402openalex-snapshot
GROPHECY
inferredcites →https://doi.org/10.1145/2503210.2503268openalex-snapshot
A large-scale cross-architecture evaluation of thread-coarsening
inferredcites →https://doi.org/10.1145/2597917.2597928openalex-snapshot
SKOPE
inferredcites →https://doi.org/10.1145/2628071.2628092openalex-snapshot
OpenTuner
inferredcites →https://doi.org/10.1177/109434200001400306openalex-snapshot
Pace—A Toolset for the Performance Prediction of Parallel and Distributed Systems
inferredcites →https://doi.org/10.1201/b10509openalex-snapshot
Performance Tuning of Scientific Applications
inferredcites →https://doi.org/10.1201/b10509-7openalex-snapshot
Large-Scale Numerical Simulations on High-End Computational Platforms
inferredcites →https://doi.org/10.2307/2530848openalex-snapshot
Simulation and the Monte Carlo Method.
inferredcites →https://doi.org/10.5555/517554.825767openalex-snapshot
Combined Selection of Tile Sizes and Unroll Factors Using Iterative Compilation

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.5962openalex-snapshot
Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps
inferred← citeshttps://doi.org/10.1002/cpe.6683openalex-snapshot
Autotuning PolyBench benchmarks with LLVM Clang/Polly loop optimization pragmas using Bayesian optimization
inferred← citeshttps://doi.org/10.1002/cpe.8322openalex-snapshot
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
inferred← citeshttps://doi.org/10.1007/978-3-030-48340-1_23openalex-snapshot
Exploiting Historical Data: Pruning Autotuning Spaces and Estimating the Number of Tuning Steps
inferred← citeshttps://doi.org/10.1016/j.jpdc.2021.10.003openalex-snapshot
Using hardware performance counters to speed up autotuning convergence on GPUs
inferred← citeshttps://doi.org/10.1016/j.parco.2025.103126openalex-snapshot
Estimating resource budgets to ensure autotuning efficiency
inferred← citeshttps://doi.org/10.1109/cluster51413.2022.00049openalex-snapshot
HPC Storage Service Autotuning Using Variational- Autoencoder -Guided Asynchronous Bayesian Optimization
inferred← citeshttps://doi.org/10.1109/ipdps47924.2020.00090openalex-snapshot
Auto-tuning Parameter Choices in HPC Applications using Bayesian Optimization
inferred← citeshttps://doi.org/10.1109/ipdpsw.2017.107openalex-snapshot
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper
inferred← citeshttps://doi.org/10.1109/jproc.2018.2841200openalex-snapshot
Autotuning in High-Performance Computing Applications
inferred← citeshttps://doi.org/10.1109/pmbs51919.2020.00012openalex-snapshot
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
inferred← citeshttps://doi.org/10.1145/3126908.3126969openalex-snapshot
Performance modeling under resource constraints using deep transfer learning
inferred← citeshttps://doi.org/10.1145/3205289.3205321openalex-snapshot
Bootstrapping Parameter Space Exploration for Fast Tuning
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/10.48550/arxiv.2010.08040openalex-snapshot
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.