var c uint for i := 0; i < size; i++ { _, c = bits.Sub(xLimbs[i], yLimbs[i], c) } // If there was a carry, then subtracting y underflowed, so // x is not greater than or equal to y. return not(choice(c)) } // assign sets x <- y if on == 1, and does nothing otherwise. // // Both operands must have the same announced length. // //go:norace func (x *Nat) assign(on choice, y *Nat) *Nat { // Eliminate bounds checks in the loop. size := len(x.limbs) xLimbs := x.limbs[:size] yLimbs := y.limbs[:size] mask :=...

in that most races have a limited number of outcomes,
and less like C and C++, where the meaning of any program with a race
is entirely undefined, and the compiler may do anything at all.
Go's approach aims to make errant programs more reliable and easier to debug,
while still insisting that races are errors and that tools can diagnose and report them.
</p>

<h2 id="model">Memory Model</h2>

var c uint for i := 0; i < size; i++ { _, c = bits.Sub(xLimbs[i], yLimbs[i], c) } // If there was a carry, then subtracting y underflowed, so // x is not greater than or equal to y. return not(choice(c)) } // assign sets x <- y if on == 1, and does nothing otherwise. // // Both operands must have the same announced length. // //go:norace func (x *Nat) assign(on choice, y *Nat) *Nat { // Eliminate bounds checks in the loop. size := len(x.limbs) xLimbs := x.limbs[:size] yLimbs := y.limbs[:size] mask :=...

  |   in as many languages as possible. (see MNG-3626)
  |
  | o annotation mechanism so that changes to the model can be accurately tracked.
  |
  | o need to clean up all the descriptions, matching anything to the current project-descriptor.xml file and
  |   improving on that
  |
  | o use enums where appropriate (eg dependency scope)
  |

      * Replace `LossScaleOptimizer(opt, "dynamic")` with `LossScaleOptimizer(opt)`. If you pass anything other than `"dynamic"` to the second argument, see (1) of the next section.
    * In the following rare cases, you need to make more changes when switching to the non-experimental API:
      * If you passed anything other than `"dynamic"` to the `loss_scale` argument (the second argument) of `LossScaleOptimizer`: