I laugh about people confusing "using JIRA" for "using Agile." One is a tool, the other is a methodology. Instead of confusing the tool for the process, they... confuse the tool for the process. I guess it is the same sort of thing.
just did it, worked perfectly from the guides on my MSI motherboard. Only scary moment was the actual bios flashing, because it took a small while and you shouldn't touch anything around you that risks blowing a fuse.
This made me finally try to migrate an old system of mine. Migrate what you ask? Well, ReBAR driver stuff requires GPT-based disks, and on that machine it's still running on MBR...
Subnormal numbers have a different, basically fixed-point, representation. They exist in order to bridge the large (relatively speaking; indeed "infinite" in a sense) gap between the least positive normal number, zero, and the greatest negative normal number, caused by the usual significand-exponent representation.
Most "mundane" uses of floating point have no need for subnormal numbers, and results that underflow could just be flushed to zero. But they’re sometimes important in scientific computing to ensure sufficient smoothness around zero, avoiding precision issues.
I can think of one useful property of subnormal numbers off the top of my head. If subnormal processing is enabled, then for all finite values of `a` and `b`, `a != b` if and only if `a - b != 0`. But if subnormals are flushed to zero, then two tiny normal distinct values `a` and `b` would have a subnormal difference that is flushed to zero.
Isn't that just a scale issue that exists with or without subnormals? If a and b are closer to zero than the smallest representable number, a and b compare as the same. With subnormals your smallest possible number is smaller than without, but it's still the same issue.
But that same statement applies to normal values too, right? With normal numbers you might get the oddity of a-b -> a even if b is nonzero, but you don't get the oddity of a-b -> 0 unless the same number is represented, IIUC. A and B might not be bit identical, but they represent the same number if the difference is 0.
One nice thing that subnormals get you is the property that if x-y == 0 then x == y. If you want to guard against division by 0, and your denominator is a difference of two terms, it’s nice to be able to check equality of those terms and know that if they are not equal, then their difference will not be 0.
On early computers, any underflow generated an exception that would crash the program if not handled.
This was very good, because underflows completely break the assumptions about floating-point arithmetic on which numeric algorithms are based, so the errors in the final results become unpredictable.
Subnormal numbers have been introduced as a means to avoid handling every underflow exception, because typically the use of subnormals eliminates the errors that would otherwise be caused by underflows.
The flush-to-zero and denormals-of-zero options must be strictly forbidden for any general-purpose applications. They should be permitted only in applications where there is no doubt that regardless how big the errors will be they will not have any really harmful effect, which is true for games and perhaps for AI, but for little else.
This is another great misfeature promoted by Intel, in order to win meaningless benchmarks. It would have been much better if these standard-breaking features would not have existed, because they are much more often used when they should not be used, than when they are harmless.
I don’t know how useful they are in scientific computing either, really. They are less precise than normalized numbers… if flushing them makes a difference I think it is a bad algorithm smell.
Scientific computing can be done only in 2 ways, either with subnormals or by enabling the underflow exception and writing a suitable exception handler for it.
If the use of subnormals is disabled with FTZ/DAZ that is guaranteed to generate big errors and it is completely unpredictable how big the errors will be.
If a computational algorithm generates underflows at some place, there is no way to modify the algorithm so that flushing-to-zero will not make any difference (i.e. no errors).
What is possible, is to modify the algorithm so that underflows will never happen.
This was the traditional way of writing numeric algorithms. Because on early computers underflows would crash the program, the same as overflows, one had to improve the algorithm in order to avoid both underflows and overflows.
Subnormals and infinities have been introduced in the standard precisely for lazier programmers, so that they would be able to avoid the rewriting of algorithms without the risks that underflows and overflows would generate major errors.
Unfortunately, it seems that for some programmers this is still not enough, because they want simultaneously to not be bothered with rewriting the algorithms and to have the program run as fast as with an optimized algorithm.
For this, the solution is very simple and it is not enabling FTZ/DAZ, which unless is done for a game might cause unpredictable financial losses for an unsuspecting customer, who expects that a computer must provide correct results.
The right solution is to not buy Intel CPUs or any other kind of processors whose vendor believes that the correctness of computations does not matter. It should be noted however, that the Intel server CPUs use CPU cores that are obsolete in desktop and laptop CPUs, i.e. the tested Intel CPUs use cores like those in Meteor Lake and Raptor Lake CPUs. I do not know if the more recent Intel CPU cores, from Panther Lake/Arrow Lake S/Arrow Lake H/Lunar Lake, have retained this Intel misfeature, which has characterized the Intel CPUs for much more than a decade.
If someone says that they have enabled FTZ/DAZ and they did not see any significant difference in the results of a program, that is complete B*S*T, because it is impossible to test exhaustively any program that does floating-point computations and the errors are expected to happen only for certain values, which are unlikely to be encountered during testing, but you cannot predict that those values will not be encountered in production.
I don’t know if any bugs contribute to this but this in the intel case but it has been very common historically for subnormal performance to be lower on many processors, and things like the Alpha required you to handle them in software if the COU fired a trap.
I don't know who is down voting you. AFAIK IEEE 754:2008 does require support for subnormals. You can optionally have modes that flush them to zero, but you must support subnormals.
I haven't done any work on this stuff since 2019, so my memory may be hazy.
It probably means Apple spent the silicon to handle subnormals at full speed in hardware, rather than triggering a slow microcode handler for such numbers.
All the tested CPUs implement the standard and they implement it in the right way, except for Intel, who has chosen to save some bucks even if this decision might cause unpredictable financial losses for naive customers, who might choose to use the dangerous FTZ/DAZ options to avoid the Intel slowdown, which in turn may cause unpredictable computation errors, with even more unpredictable consequences.
Some poster has linked a Mastodon thread, where Fabian Giesen explains that handling in hardware the subnormals is cheap in floating-point adders and in fused-multiply-add (FMA) execution units. Many processors do the multiplications in the FMA execution units, so there is no penalty for them to do the subnormal handling in the right way.
On the other hand, some CPUs, including the Intel big cores, have some floating-point multipliers that are separate from the FMA units. The reason is that those separate multipliers can have lower latencies, typically by 1 or 2 clock cycles, which may help those CPUs to win some benchmarks, especially when running unoptimized legacy programs (in optimized programs, most multiplications are combined with additions into FMA operations).
The separate multipliers are simplified in comparison with those included in the FMA units, and handling subnormals in them would be expensive, because then they would become so complex that there would be no advantage for them to be separate multipliers. Which is why Intel does not handle subnormal multiplication in hardware, but a microprogram is invoked for this.
counterpoint: it doesn't matter, since you need to learn relevant boiling/freezing temperatures on both scales anyway.
As a european: F is the only american system that is easy to use and reason with (considering its 0-100 range is spanning the "doable" temp range of too-cold to too-hot), unlike other imperial systems for lengths and whatnot.
reply