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GPU Performance and Power Tuning Using Regression Trees
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9780470061572.eqr492openalex-snapshot
Classification and Regression Tree Methods
inferredcites →https://doi.org/10.1007/978-3-642-01970-8_89openalex-snapshot
A Note on Auto-tuning GEMM for GPUs
inferredcites →https://doi.org/10.1016/0020-0190(76)90095-8openalex-snapshot
Constructing optimal binary decision trees is NP-complete
inferredcites →https://doi.org/10.1080/01621459.1958.10501479openalex-snapshot
On Grouping for Maximum Homogeneity
inferredcites →https://doi.org/10.1109/greencomp.2010.5598315openalex-snapshot
Statistical power modeling of GPU kernels using performance counters
inferredcites →https://doi.org/10.1109/hpca.2005.11openalex-snapshot
Proceedings. 11th International Symposium on High-Performance Computer Architecture
inferredcites →https://doi.org/10.1109/hpca.2006.1598116openalex-snapshot
Construction and Use of Linear Regression Models for Processor Performance Analysis
inferredcites →https://doi.org/10.1109/hpcsim.2011.5999886openalex-snapshot
Understanding the impact of CUDA tuning techniques for Fermi
inferredcites →https://doi.org/10.1109/iccd.2011.6081376openalex-snapshot
Tree structured analysis on GPU power study
inferredcites →https://doi.org/10.1109/iiswc.2009.5306797openalex-snapshot
Rodinia: A benchmark suite for heterogeneous computing
inferredcites →https://doi.org/10.1109/inpar.2012.6339587openalex-snapshot
Machine learning for predictive auto-tuning with boosted regression trees
inferredcites →https://doi.org/10.1109/ispass.2012.6189201openalex-snapshot
Stargazer: Automated regression-based GPU design space exploration
inferredcites →https://doi.org/10.1109/pact20333.2013openalex-snapshot
Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques
inferredcites →https://doi.org/10.1145/1168857.1168881openalex-snapshot
Accurate and efficient regression modeling for microarchitectural performance and power prediction
inferredcites →https://doi.org/10.1145/1356058.1356084openalex-snapshot
Program optimization space pruning for a multithreaded gpu
inferredcites →https://doi.org/10.1145/1555754.1555775openalex-snapshot
An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness
inferredcites →https://doi.org/10.1145/1693453.1693471openalex-snapshot
Model-driven autotuning of sparse matrix-vector multiply on GPUs
inferredcites →https://doi.org/10.1145/1815961.1815998openalex-snapshot
An integrated GPU power and performance model
inferredcites →https://doi.org/10.1145/1941553.1941589openalex-snapshot
Auto-tuning of fast fourier transform on graphics processors
inferredcites →https://doi.org/10.1145/2145816.2145819openalex-snapshot
A performance analysis framework for identifying potential benefits in GPGPU applications
inferredcites →https://doi.org/10.1145/2503210.2503268openalex-snapshot
A large-scale cross-architecture evaluation of thread-coarsening
inferredcites →https://doi.org/10.1145/3179541openalex-snapshot
Proceedings of the 2018 International Symposium on Code Generation and Optimization
inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot
R: A Language and Environment for Statistical Computing
inferredcites →https://doi.org/10.5555/1413370.1413375openalex-snapshot
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
inferredcites →https://doi.org/10.5555/2523721.2523757openalex-snapshot
Starchart: hardware and software optimization using recursive partitioning regression trees
inferredcites →https://doi.org/10.5860/choice.45-6515openalex-snapshot
Encyclopedia of statistics in quality and reliability
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10586-019-03011-2openalex-snapshot
Comprehensive regression-based model to predict performance of general-purpose graphics processing unit
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2020.106965openalex-snapshot
An accurate model to predict the performance of graphical processors using data mining and regression theory
inferred← citeshttps://doi.org/10.1109/ispass.2019.00015openalex-snapshot
Full-System Simulation of Mobile CPU/GPU Platforms
inferred← citeshttps://doi.org/10.1109/ispass64960.2025.00028openalex-snapshot
PowerSensor3: A Fast and Accurate Open Source Power Measurement Tool
inferred← citeshttps://doi.org/10.1109/pmbs56514.2022.00010openalex-snapshot
Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning
inferred← citeshttps://doi.org/10.1109/sbac-pad49847.2020.00035openalex-snapshot
Predicting the Energy Consumption of CUDA Kernels using SimGrid
inferred← citeshttps://doi.org/10.1109/tcad.2017.2698026openalex-snapshot
QIG: Quantifying the Importance and Interaction of GPGPU Architecture Parameters
inferred← citeshttps://doi.org/10.1109/tpds.2017.2758781openalex-snapshot
MIA: Metric Importance Analysis for Big Data Workload Characterization
inferred← citeshttps://doi.org/10.1145/3078633.3081040openalex-snapshot
Adaptive optimization for OpenCL programs on embedded heterogeneous systems
inferred← citeshttps://doi.org/10.1145/3140582.3081040openalex-snapshot
Adaptive optimization for OpenCL programs on embedded heterogeneous systems
inferred← citeshttps://doi.org/10.1145/3603533openalex-snapshot
Program Analysis and Machine Learning–based Approach to Predict Power Consumption of CUDA Kernel
inferred← citeshttps://doi.org/10.32920/26883748openalex-snapshot
Power and Performance Based Autotuning of Heterogeneous Applications for CPU-GPU Systems
inferred← citeshttps://doi.org/10.32920/26883748.v1openalex-snapshot
Power and Performance Based Autotuning of Heterogeneous Applications for CPU-GPU Systems
inferred← citeshttps://doi.org/10.48550/arxiv.1907.02894openalex-snapshot
RegDem: Increasing GPU Performance via Shared Memory Register Spilling
inferred← citeshttps://doi.org/10.5194/isprs-archives-xlvi-4-w3-2021-295-2022openalex-snapshot
GPU UTILIZATION IN GEOPROCESSING BIG GEODATA: A REVIEW
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