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Auto-research with codex: How I achieved a 232x Faster Kernel
#gpu#qr decomposition#optimization#codex#auto-research
English
In a recent GPU Mode contest, a participant achieved a 232x speedup in implementing batched square compact-Householder QR factorization using Codex. The competition emphasized the importance of iterative learning and optimization in GPU kernel development, allowing for over 1500 submissions during the 14-day event.
中文
在最近的GPU模式比赛中，一位参与者通过使用Codex实现了批量平方紧凑Householder QR分解的232倍加速。比赛强调了在GPU内核开发中迭代学习和优化的重要性，在为期14天的活动中允许提交超过1500次。