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我們能利用machine learning去幫助compiler的optimization演算法變強嗎？

ML通常是拿來幫忙做predict和decisions 透過大量training data  (input: feature,  output: predict a value or classification) 對於沒有看過的feature做predict和decisio ns 我對於ML經驗只有半年多 以下是我的一些看法 1.  如果是對特定benchmark的話 用ML能幫忙找出不錯的heuristic值 (ex.  --param,  --regalloc=[ basic|fast|greedy|pbqp] 等等) google也滿多的 Automatic Feature Generation for Machine Learning Based Optimizing Compilation 但直到看到"MILEPOST GCC"  覺得圖很眼熟... 就是interactive compiler! 2.  如果想把machine learning的training也做進compiler內 看來就只能做在JIT了 google了一下也有人做到VM Using machines to learn method-specific compilation strategies (IBM) Method-Specific Dynamic Compilation using Logistic Regression 也有人拿來做CPU/GPU decisions XD Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection 如果做在AOT compiler, 感覺就像PGO, 看起來也不需ML 3.  如果要用machine learning去學出某隻benchmark/ function最好的instruction sequence 感覺可以用reinforcement learning去做 但是用interactive compiler去挑heuristic就可能做...
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RISC-V NaN Generation and Propagation

1. RISC-V spec said NaN propagation should generate canonical NaN Except when otherwise stated, if the result of a floating-point operation is NaN, it is the canonical NaN. The canonical NaN has a positive sign and all significand bits clear except the MSB, a.k.a. the quiet bit. For single-precision floating-point, this corresponds to the pattern 0x7fc00000. 2. IEEE 754 2018 standard recommends that operations on NaNs could propagate the input's NaN, but it's not required. 3. LLVM NaN propagation implementation follows IEEE rule which does not match RISC-V spec. https://github.com/llvm/llvm-project/blob/main/llvm/lib/Analysis/InstructionSimplify.cpp#L4966-L4968 The simplest test is adding two constant floating values and one is NaN, and the result of  -O0 and -O3 are different due to the NaN's fraction bit. So software could use C++ isnan() to check NaN before comparing, it's why we could not use memcmp to compare two floating values, not only accuracy issue, but also n...
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