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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Adaptive variable sampling model for performance analysis in high cache-performance computing environments.

Mincheol Shin1, Mucheol Kim1, Geunchul Park2

  • 1Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea.

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|June 26, 2023
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Summary
This summary is machine-generated.

This study introduces an adaptive model for high-performance computing (HPC) that automatically selects key variables for performance prediction. This approach enhances efficiency and accuracy in HPC environments without needing expert knowledge.

Keywords:
Data scienceDecision supportHigh performance computingMachine learningPerformance prediction

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Area of Science:

  • Computer Science
  • Computational Science

Background:

  • High-performance computing (HPC) is crucial for scientific advancement, but optimizing its performance and resource utilization is challenging.
  • Predicting system states aids scheduling, yet current hardware performance monitors demand expert knowledge and lack standardization.

Purpose of the Study:

  • To develop an adaptive variable sampling model for performance analysis in HPC environments.
  • To automate the selection of optimal variables for performance prediction, reducing reliance on expert knowledge.

Main Methods:

  • Proposed an adaptive variable sampling model for HPC performance analysis.
  • Developed a method to automatically classify optimal variables from a large set of performance-related parameters.
  • Validated the model across diverse architectures and applications.

Main Results:

  • The model automatically identifies optimal variables without requiring expert input during sampling.
  • Achieved performance improvements ranging from 24.25% to 58.75% across various tests.
  • Maintained prediction accuracy while significantly increasing speed.

Conclusions:

  • The adaptive variable sampling model offers an efficient and accurate solution for HPC performance analysis.
  • Automated variable selection democratizes performance optimization, making it accessible beyond specialized expertise.
  • This method enhances HPC resource management and accelerates scientific discovery.