Tag: correlations

High Computing Room Requires

Posted on by lechuck park

With a Claude’s Help
Core Challenge:

  1. High Variability in GPU/HPC Computing Room
  • Dramatic fluctuations in computing loads
  • Significant variations in power consumption
  • Changing cooling requirements

Solution Approach:

  1. Establishing New Data Collection Systems
  • High Resolution Data: More granular, time-based data collection
  • New Types of Data Acquisition
  • Identification of previously overlooked data points
  1. New Correlation Analysis
  • Understanding interactions between computing/power/cooling
  • Discovering hidden patterns among variables
  • Deriving predictable correlations

Objectives:

  • Managing variability through AI-based analysis
  • Enhancing system stability
  • Improving overall facility operational efficiency

In essence, the diagram emphasizes that to address the high variability challenges in GPU/HPC environments, the key strategy is to collect more precise and new types of data, which enables the discovery of new correlations, ultimately leading to improved stability and efficiency.

This approach specifically targets the inherent variability of GPU/HPC computing rooms by focusing on data collection and analysis as the primary means to achieve better operational outcomes.

Time Series Data

Posted on by lechuck park

From Claude with some prompting

  1. Raw Time Series Data:
    • Data Source: Sensors or meters operating 24/7, 365 days a year
    • Components: a. Point: The data point being measured b. Metric: The measurement value for each point c. Time: When the data was recorded
    • Format: (Point, Value, Time)
    • Additional Information: a. Config Data: Device name, location, and other setup information b. Tag Info: Additional metadata or classification information for the data
    • Characteristics:
      • Continuously updated based on status changes
      • Automatically changes over time
  2. Processed Time Series Data (2nd logical Data):
    • Processing Steps: a. ETL (Extract, Transform, Load) operations b. Analysis of correlations between data points (Point A and Point B) c. Data processing through f(x) function
      • Creating formulas through correlations using experience and AI learning
    • Result:
      • Generation of new data points
      • Includes original point, related metric, and time information
    • Characteristics:
      • Provides more meaningful and correlated information than raw data
      • Reflects relationships and influences between data points
      • Usable for more complex analysis and predictions

Through this process, Raw Time Series Data is transformed into more useful and insightful Processed Time Series Data. This aids in understanding data patterns and predicting future trends.