On Fri, Mar 6, 2015 at 4:50 PM, Micah Fedke <micah.fe...@collabora.co.uk> wrote: > So use the "max/min of all permutations" method for all ops? e.g.: > > def __mul__(self, other): > a = self.high * other.high > b = self.high * other.low > c = self.low * other.high > d = self.low * other.low > high = numpy.float32(numpy.amax([a, b, c, d])) > low = numpy.float32(numpy.amin([a, b, c, d])) > return ValueInterval(high, low) > > And tack on the tolerance at the end like this, for ops that have a > tolerance? Things should move in the right direction after high and low > have been determined, if I'm not mistaken. > > def __truediv__(self, other): > tol = numpy.float32(2.5) > a = self.high / other.high > b = self.high / other.low > c = self.low / other.high > d = self.low / other.low > self.high = numpy.float32(numpy.amax([a, b, c, d])) > self.low = numpy.float32(numpy.amin([a, b, c, d])) > self.high += _ulpsize(self.high) * tol > self.low -= _ulpsize(self.low) * tol > return self
Yes, I think that's right. > > As for manual fma's, that should work. I wonder, though - a double-round > manual fma has the potential to produce more error than a single-round, and > the spec allows either method, so don't we want to evaluate the more > error-ful option? Yes and no. Both a * b + c and fma(a, b, c) have exact right answers as defined by the spec. However for a particular a * b + c that happens, the implementation is allowed to use either one. You could define it as a range, but... how do you detect the a * b + c case? Let's say I'm doing dot(x, x), which becomes a * a + b * b + c * c + d * d. An implementation is perfectly within its right to rewrite this as fma(a, a, fma(b, b, fma(c, c, d * d))) or even fma(a, a, b * b + fma(c, c, d * d)) Since this approach only considers one op at a time, I don't see an easy way to handle it, unfortunately... _______________________________________________ Piglit mailing list Piglit@lists.freedesktop.org http://lists.freedesktop.org/mailman/listinfo/piglit
