# Posits - a new approach could sink floating point computation

**URL:** <https://discourse.julialang.org/t/posits-a-new-approach-could-sink-floating-point-computation/26176>\
**Category:** Numerics\
**Created:** [July 9, 2019, 8:02am UTC](https://discourse.julialang.org/t/posits-a-new-approach-could-sink-floating-point-computation/26176 "2019-07-09T08:02:41Z")\
**Posts on this page:** 1\
**Showing post:** 44

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**Author:** ![StefanKarpinski](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stefankarpinski/32/24_2.png) [@StefanKarpinski](https://discourse.julialang.org/u/StefanKarpinski)\
**Post date:** [July 16, 2019, 7:14pm UTC](https://discourse.julialang.org/t/posits-a-new-approach-could-sink-floating-point-computation/26176/44 "2019-07-16T19:14:27Z")

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Adding hardware “quire” accumulators for IEEE floats would be a killer hardware feature. There’s an ongoing [conversation](https://discourse.julialang.org/t/julia-equivalent-of-pythons-fsum-for-floating-point-summation/17785) about Python’s fsum and superaccumulators, which are surprisingly fast on modern hardware, with various people trying to optimize them better. It seems like something where a hardware mechanims for getting guaranteed exact sums would be a game changer.

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