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Program Analysis and Machine Learning–based Approach to Predict Power Consumption of CUDA Kernel
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Predicting the direction of stock market prices using tree-based classifiers
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.2018.00072openalex-snapshot
GPGPU Power Modeling for Multi-domain Voltage-Frequency Scaling
inferredcites →https://doi.org/10.1109/iccd.2011.6081376openalex-snapshot
Tree structured analysis on GPU power study
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Designing Power-Aware Collective Communication Algorithms for InfiniBand Clusters
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GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures
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Effects of Dynamic Voltage and Frequency Scaling on a K20 GPU
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Rodinia: A benchmark suite for heterogeneous computing
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Making a case for a Green500 list
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A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures
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An Approach to Estimate Power Consumption of a CUDA Kernel
inferredcites →https://doi.org/10.1109/ispass.2010.5452013openalex-snapshot
Demystifying GPU microarchitecture through microbenchmarking
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Tango: A Deep Neural Network Benchmark Suite for Various Accelerators
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The Green500 List: Encouraging Sustainable Supercomputing
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Unified power and energy measurement API for HPC co-processors
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Microbenchmarks for GPU Characteristics: The Occupancy Roofline and the Pipeline Model
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An Accurate GPU Performance Model for Effective Control Flow Divergence Optimization
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A flexible simulation framework for graphics architectures
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A view of the parallel computing landscape
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Real time power estimation and thread scheduling via performance counters
inferredcites →https://doi.org/10.1145/1810479.1810510openalex-snapshot
Towards optimizing energy costs of algorithms for shared memory architectures
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An integrated GPU power and performance model
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Power Modeling for GPU Architectures Using McPAT
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Effects of source-code optimizations on GPU performance and energy consumption
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GPU Performance and Power Tuning Using Regression Trees
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Improving GPU Performance Through Resource Sharing
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Characterizations of Reducible Flow Graphs
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Predictable GPUs Frequency Scaling for Energy and Performance
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AccelWattch: A Power Modeling Framework for Modern GPUs
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Performance and Power Prediction for Concurrent Execution on GPUs
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Greedy function approximation: A gradient boosting machine.
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Inspect-GPU: A Software to Evaluate Performance Characteristics of CUDA Kernels Using Microbenchmarks and Regression Models
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Comprehensive Analysis of Process Energy Consumption on Multi-socket Systems with GPUs
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Energy Aware GPU Kernel Optimization: a Hierarchical Approach
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PowerQuant: Architecture-Agnostic GPU Power Estimation via Quantile Regression
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CO 2 –N 2 Mixture Viscosity Modelling Using Machine Learning: Towards Sustainable Carbon Capture and Energy Efficiency
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Techniques Avancées D'analyse De Performance Pour Évaluer L'efficacité Des Accélérateurs Graphiques Pour Les Applications
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