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GPUMech: GPU Performance Modeling Technique Based on Interval Analysis
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0743-7315(91)90005-topenalex-snapshot
An analytical approach to performance/ cost modeling of parallel computers
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A performance study of general-purpose applications on graphics processors using CUDA
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A first-order fine-grained multithreaded throughput model
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Graphite: A distributed parallel simulator for multicores
inferredcites →https://doi.org/10.1109/hpca.2010.5416636openalex-snapshot
Interval simulation: Raising the level of abstraction in architectural simulation
inferredcites →https://doi.org/10.1109/hpca.2011.5749745openalex-snapshot
A quantitative performance analysis model for GPU architectures
inferredcites →https://doi.org/10.1109/hpca.2014.6835955openalex-snapshot
A detailed GPU cache model based on reuse distance theory
inferredcites →https://doi.org/10.1109/iccad.1999.810662openalex-snapshot
Analytical macromodeling for high-level power estimation
inferredcites →https://doi.org/10.1109/icdcs.2011.16openalex-snapshot
Cache Miss Analysis for GPU Programs Based on Stack Distance Profile
inferredcites →https://doi.org/10.1109/iiswc.2009.5306797openalex-snapshot
Rodinia: A benchmark suite for heterogeneous computing
inferredcites →https://doi.org/10.1109/isca.2004.1310786openalex-snapshot
A first-order superscalar processor model
inferredcites →https://doi.org/10.1109/ispass.2012.6189201openalex-snapshot
Stargazer: Automated regression-based GPU design space exploration
inferredcites →https://doi.org/10.1109/ispass.2012.6189202openalex-snapshot
A mechanistic performance model for superscalar in-order processors
inferredcites →https://doi.org/10.1109/micro.2008.4771779openalex-snapshot
Hybrid analytical modeling of pending cache hits, data prefetching, and MSHRs
inferredcites →https://doi.org/10.1109/micro.2012.16openalex-snapshot
Cache-Conscious Wavefront Scheduling
inferredcites →https://doi.org/10.1145/1028176.1006729openalex-snapshot
A First-Order Superscalar Processor Model
inferredcites →https://doi.org/10.1145/1168857.1168880openalex-snapshot
A performance counter architecture for computing accurate CPI components
inferredcites →https://doi.org/10.1145/1250662.1250712openalex-snapshot
Automated design of application specific superscalar processors
inferredcites →https://doi.org/10.1145/1508244.1508260openalex-snapshot
Per-thread cycle accounting in SMT processors
inferredcites →https://doi.org/10.1145/1534909.1534910openalex-snapshot
A mechanistic performance model for superscalar out-of-order processors
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/10.1145/1693453.1693470openalex-snapshot
An adaptive performance modeling tool for GPU architectures
inferredcites →https://doi.org/10.1145/2019608.2019609openalex-snapshot
Hybrid analytical modeling of pending cache hits, data prefetching, and MSHRs
inferredcites →https://doi.org/10.1145/2145816.2145819openalex-snapshot
A performance analysis framework for identifying potential benefits in GPGPU applications
inferredcites →https://doi.org/10.1145/2145816.2145820openalex-snapshot
Efficient performance evaluation of memory hierarchy for highly multithreaded graphics processors
inferredcites →https://doi.org/10.1145/360128.360148openalex-snapshot
A static power model for architects
inferredcites →https://doi.org/10.5555/339492.340027openalex-snapshot
Analytical macromodeling for high-level power estimation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Performance modeling of graphics processing unit application using static and dynamic analysis
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Timing Models for Fast Embedded Software Performance Analysis
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A Quantitative Study of Locality in GPU Caches for Memory-Divergent Workloads
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SDAM: a combined stack distance-analytical modeling approach to estimate memory performance in GPUs
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Using hardware performance counters to speed up autotuning convergence on GPUs
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Analyzing data locality in GPU kernels using memory footprint analysis
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SKERD: Reuse distance analysis for simultaneous multiple GPU kernel executions
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CUDAsap: Statically-Determined Execution Statistics as Alternative to Execution-Based Profiling
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Performance Modeling for Optimal Data Placement on GPU with Heterogeneous Memory Systems
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The Pipeline Performance Model: A Generic Executable Performance Model for GPUs
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Need for Speed: Experiences Building a Trustworthy System-Level GPU Simulator
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VLAG: A very fast locality approximation model for GPU kernels with regular access patterns
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Mocktails: Capturing the Memory Behaviour of Proprietary Mobile Architectures
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AIO: An Abstraction for Performance Analysis Across Diverse Accelerator Architectures
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sCROOGe: Circuit-level Design and Optimization Framework for RISC-V Out-of-Order GPUs
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Sieve: Stratified GPU-Compute Workload Sampling
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Noah: Tile-Level Interval Analysis for NPU Performance Modeling
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Modeling Emerging Memory-Divergent GPU Applications
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RDMKE: Applying Reuse Distance Analysis to Multiple GPU Kernel Executions
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Efficient Cache Performance Modeling in GPUs Using Reuse Distance Analysis
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Lessons from Teaching Analytical Performance Modeling
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Forêt Aléatoire, Hyperparamètres d'Optimisation, Parallélisation de GPU et Application à l'Analyse de Sol pour l'Optimisation des Cultures
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