PCG : A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation

By Melissa E. O'Neill. 2014

Subfields: Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Algorithms & Complexity

1. Matthew Brett
2. Chen-Chun Wu
3. Qinyan Tan
4. Lu Lu
5. Yadhu Kartha
6. Min Zhu
7. Houwen Peng
8. Jianlong Fu

Citations in the corpus

• Array programming with NumPy (2020, methodology): “The default bit generator is a 64-bit implementation of the Permuted Congruential Generator [62] (PCG64).”
• TinyViT: Fast Pretraining Distillation for Small Vision Transformers (2022, background): “In practice, a common choice for the decoder is the pseudo-random number generator (i.e. PCG [49]).”
• A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks (2022, methodology): “In practice, this is usually done using pseudo-random number generators such as the PCG-64 algorithm [45].”