|
You are here |
marc-b-reynolds.github.io | ||
| | | | |
nhigham.com
|
|
| | | | | The 2008 revision of the IEEE Standard for Floating-Point Arithmetic introduced a half precision 16-bit floating point format, known as fp16, as a storage format. Various manufacturers have adopted fp16 for computation, using the obvious extension of the rules for the fp32 (single precision) and fp64 (double precision) formats. For example, fp16 is supported by... | |
| | | | |
orlp.net
|
|
| | | | | [AI summary] A deep dive into the complexities and pitfalls of comparing different numeric types (integers and floating-point) across various programming languages and the CPU architecture | |
| | | | |
codingnest.com
|
|
| | | | | There is a lot of confusion about floating-point numbers and a lot of bad advice going around. IEEE-754 floating-point numbers are a complex beast, and comparing them is not always easy, but in this post, we will take a look at different approaches and their tradeoffs. | |
| | | | |
pavpanchekha.com
|
|
| | | [AI summary] The blog post discusses a floating-point error example where the expression sqrt(x^2) leads to significant inaccuracies due to numerical overflow and underflow, highlighting how even accurate operations can compose into an inaccurate result. | ||