Floating Point Errors and Large Scale Worlds

Floating Point Error Demonstration

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Floating-Point Numbers – Real Python

Floating-Point Representation Error – Real Python

Java: Floating Point Numbers

A weather forecasting computation requires 250 billion floating-point operations. The problem is processed in a supercomputer that can perform 100 megaflops. How long will it take to do these calculations? | Numerade

1.1 quintillion operations per second: US has world’s fastest supercomputer

Artificial Intelligence | AI on Instagram: "Neural scaling laws describe how a model’s performance improves (predictably) as we increase the amount of training data, the size of the model (in terms of parameters), or the total compute used (measured in FLOPs or floating point operations per second). The scaling laws are power laws, meaning that as we scale things up, the performance increases proportionally. For instance, as we increase the size of the model, we should expect better performance