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Supercomputer Power and Moore’s Law Progression
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About
This data explores the foundational principle of technological growth, commonly known as Moore’s Law, by visualizing key performance metrics over time. It tracks the growth of transistor density and the corresponding advancement in supercomputing power, specifically the increase in Floating-Point Operations per Second (GFLOPS). This historical view demonstrates the constant exponential rate of technological progress that has guided the industry for over fifty years, from Gordon Moore's original observation in 1965 to modern supercomputer speeds.
Columns
The dataset includes four essential fields for tracking global supercomputer performance and technological scaling:
- country: Identifies the geographic scope of the data, which is consistently set to 'World'.
- codes: Provides a standardized code for the geographic entity, frequently 'OWID_WRL'.
- Years: The timeframe of observations, spanning the years 1993 to 2022.
- Floating-Point Operations per Second (GFLOPS): The primary metric for measuring computing speed, capturing the exponential growth in processing power, ranging from 124 GFLOPS to 1.10 billion GFLOPS.
Distribution
This data file, titled
supercomputerpowerflops new.csv, is structured with 4 columns and contains 30 valid records or observations. The total file size is approximately 913 B. The data is highly curated, with 100% validity across all columns and no missing or mismatched values recorded.Usage
Ideal applications include academic research into the history of computing and information technology; predictive modelling focused on future computing power trends; visualising the enduring nature of the exponential growth curve associated with Moore’s Law; and benchmarking historical advancements in supercomputer performance across the globe.
Coverage
The time range covered by the dataset extends from 1993 up to 2022. Geographically, the data is collected at a global level, designated explicitly as "World" in the records. The data focuses specifically on the measurement of transistor count progression and corresponding GFLOPS performance metrics during this period. The expected update frequency for this material is annually.
License
CC0: Public Domain
Who Can Use It
- Technology Historians: To document and analyse the continuous fulfilment of Moore’s prediction over decades.
- Data Scientists: For generating time-series models on exponential growth phenomena in technology.
- Industry Analysts: To benchmark the pace of innovation and assess potential physical or economic limits in semiconductor manufacturing.
Dataset Name Suggestions
- Supercomputer Power and Moore’s Law Progression
- Global GFLOPS Performance 1993-2022
- Exponential Technology Growth Data
- Transistor Density Historical Tracker
Attributes
Original Data Source: Supercomputer Power and Moore’s Law Progression
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