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A Simplified and Accurate Model of Power-Performance Efficiency on Emergent GPU Architectures

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What this work says

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

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Power Consumption of GPUs from a Software Perspective
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An ISO-Energy-Efficient Approach to Scalable System Power-Performance Optimization
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Statistical power modeling of GPU kernels using performance counters
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A quantitative performance analysis model for GPU architectures
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Performance Analysis of MPI Collective Operations
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On the energy efficiency of graphics processing units for scientific computing
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A cross-input adaptive framework for GPU program optimizations
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Barra: A Parallel Functional Simulator for GPGPU
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Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community
inferredcites →https://doi.org/10.1109/micro.2003.1253186openalex-snapshot
Runtime power monitoring in high-end processors: methodology and empirical data
inferredcites →https://doi.org/10.1109/sc.2005.57openalex-snapshot
Performance-constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters
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A flexible simulation framework for graphics architectures
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inferredcites →https://doi.org/10.1145/1454115.1454151openalex-snapshot
Prediction models for multi-dimensional power-performance optimization on many cores
inferredcites →https://doi.org/10.1145/1542275.1542313openalex-snapshot
Performance modeling and automatic ghost zone optimization for iterative stencil loops on GPUs
inferredcites →https://doi.org/10.1145/1555754.1555775openalex-snapshot
An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness
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The Scalable Heterogeneous Computing (SHOC) benchmark suite
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An integrated GPU power and performance model
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Bounding the effect of partition camping in GPU kernels
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A performance analysis framework for identifying potential benefits in GPGPU applications
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Runtime power monitoring in high-end processors: methodology and empirical data

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