on i386 (A64), 5 cycles on amd64 (A64), and 3 cycles on ia64). gcc
tends to generate very bad code for accessing floating point values
as bits except when the integer accesses have the same width as the
floating point values, and direct accesses to bit-fields (as is common
only for long double precision) always gives such accesses. Use the
expsign access method, which is good for 80-bit long doubles and
hopefully no worse for 128-bit long doubles. Now the generated code
is less bad. There is still unnecessary copying of the arg on amd64
and i386 and mysterious extra slowness on amd64.
pi/4 <= |x| <= 3pi/4. Use the same branch ladder as for float precision.
Remove the optimization for |x| near pi/2 and don't do it near the
multiples of pi/2 in the newly optimized range, since it requires
fairly large code to handle only relativley few cases. Ifdef out
optimization for |x| <= pi/4 since this case can't occur because it
is done in callers.
On amd64 (A64), for cos() and sin() with uniformly distributed args,
no cache misses, some parallelism in the caller, and good but not great
CC and CFLAGS, etc., this saves about 40 cycles or 38% in the newly
optimized range, or about 27% on average across the range |x| <= 2pi
(~65 cycles for most args, while the A64 hardware fcos and fsin take
~75 cycles for half the args and 125 cycles for the other half). The
speedup for tan() is much smaller, especially relatively. The speedup
on i386 (A64) is slightly smaller, especially relatively. i386 is
still much slower than amd64 here (unlike in the float case where it
is slightly faster).
saves an average of about 8 cycles or 5% on A64 (amd64 and i386 --
more in cycles but about the same percentage on i386, and more with
old versions of gcc) with good CFLAGS and some parallelism in the
caller. As usual, it takes a couple more multiplications so it will
be slower on old machines.
Convert to __FBSDID().
optimization of about 10% for cos(x), sin(x) and tan(x) on
|x| < 2**19*pi/2. We didn't do this before because __ieee754__rem_pio2()
is too large and complicated for gcc-3.3 to inline very well. We don't
do this for float precision because it interferes with optimization
of the usual (?) case (|x| < 9pi/4) which is manually inlined for float
precision only.
This has some rough edges:
- some static data is duplicated unnecessarily. There isn't much after
the recent move of large tables to k_rem_pio2.c, and some static data
is duplicated to good affect (all the data static const, so that the
compiler can evaluate expressions like 2*pio2 at compile time and
generate even more static data for the constant for this).
- extern inline is used (for the same reason as in previous inlining of
k_cosf.c etc.), but C99 apparently doesn't allow extern inline
functions with static data, and gcc will eventually warn about this.
Convert to __FBSDID().
Indent __ieee754_rem_pio2()'s declaration consistently (its style was
made inconsistent with fdlibm a while ago, so complete this).
Fix __ieee754_rem_pio2()'s return type to match its prototype. Someone
changed too many ints to int32_t's when fixing the assumption that all
ints are int32_t's.
Bruce for putting lots of effort into these; getting them right isn't
easy, and they went through many iterations.
Submitted by: Steve Kargl <sgk@apl.washington.edu> with revisions from bde
|x| or |y| and b is |y| or |x|) when mixing NaN arg(s).
hypot*() had its own foot shooting for mixing NaNs -- it swaps the
args so that |x| in bits is largest, but does this before quieting
signaling NaNs, so on amd64 (where the result of adding NaNs depends
on the order) it gets inconsistent results if setting the quiet bit
makes a difference, just like a similar ia64 and i387 hardware comparison.
The usual fix (see e_powf.c 1.13 for more details) of mixing using
(a+0.0)+-(b+0.0) doesn't work on amd64 if the args are swapped (since
the rder makes a difference with SSE). Fortunately, the original args
are unchanged and don't need to be swapped when we let the hardware
decide the mixing after quieting them, but we need to take their
absolute value.
hypotf() doesn't seem to have any real bugs masked by this non-bug.
On amd64, its maximum error in 2^32 trials on amd64 is now 0.8422 ulps,
and on i386 the maximum error is unchanged and about the same, except
with certain CFLAGS it magically drops to 0.5 (perfect rounding).
Convert to __FBSDID().
be into 12+24 bits of precision for extra-precision multiplication,
but was into 13+24 bits. On i386 with -O1 the bug was hidden by
accidental extra precision, but on amd64, in 2^32 trials the bug
caused about 200000 errors of more than 1 ulp, with a maximum error
of about 80 ulps. Now the maximum error in 2^32 trials on amd64
is 0.8573 ulps. It is still 0.8316 ulps on i386 with -O1.
The nearby decomposition of 1/ln2 and the decomposition of 2/(3ln2) in
the double precision version seem to be sub-optimal but not broken.
This uses 2 tricks to improve consistency so that more serious problems
aren't hidden in simple regression tests by noise for the NaNs:
- for a signaling NaN, adding 0.0 generates the invalid exception and
converts to a quiet NaN, and doesn't have too many effects for other
types of args (it converts -0 to +0 in some rounding modes, but that
hopefully doesn't change the result after adding the NaN arg). This
avoids some inconsistencies on i386 and ia64. On these arches, the
result of an operation on 2 NaNs is apparently the largest or the
smallest of the NaNs as bits (consistently largest or smallest for
each arch, but the opposite). I forget which way the comparison
goes and if the sign bit affects it. The quiet bit is is handled
poorly by not always setting it before the comparision or ignoring
it. Thus if one of the args was originally a signaling NaN and the
other was originally a quiet NaN, then the result depends too much
on whether the signaling NaN has been quieted at this point, which
in turn depends on optimizations and promotions. E.g., passing float
signaling NaNs to double functions must quiet them on conversion;
on i387, loading a signaling NaN of type float or double (but not
long double) into a register involves a conversion, so it quiets
signaling NaNs, so if the addition has 2 register operands than it
only sees quiet NaNs, but if the addition has a memory operand then
it sees a signaling NaN iff it is in the memory operand.
- subtraction instead of addition is used to avoid a dubious optimization
in old versions of gcc. For SSE operations, mixing of NaNs apparently
always gives the target operand. This is not as good as the i387
and ia64 behaviour. It doesn't mix NaNs at all, and makes addition
not quite commutative. Old versions of gcc sometimes rewrite x+y
to y+x and thus give different results (in bits) for NaNs. gcc-3.3.3
rewrites x+y to y+x for one of pow() and powf() but not the other,
so starting from float NaN args x and y, powf(x, y) was almost always
different from pow(x, y).
These tricks won't give consistency of 2-arg float and double functions
with long double ones on amd64, since long double ones use the i387
which has different semantics from SSE.
Convert to __FBSDID().
and trunc() to the corresponding long double functions. This is not
just an optimization for these arches. The full long double functions
have a wrong value for `huge', and the arches without full long doubles
depended on it being wrong.
This has the side effect of confusing gcc-4.2.1's optimizer into more
often doing the right thing. When it does the wrong thing here, it
seems to be mainly making too many copies of x with dependency chains.
This effect is tiny on amd64, but in some cases on i386 it is enormous.
E.g., on i386 (A64) with -O1, the current version of exp2() should
take about 50 cycles, but took 83 cycles before this change and 66
cycles after this change. exp2f() with -O1 only speeded up from 51
to 47 cycles. (exp2f() should take about 40 cycles, on an Athlon in
either i386 or amd64 mode, and now takes 42 on amd64). exp2l() with
-O1 slowed down from 155 cycles to 123 for some args; this is unimportant
since the i386 exp2l() is a fake; the wrong thing for it seems to
involve branch misprediction.
faster on all machines tested (old Celeron (P2), A64 (amd64 and i386)
and ia64) except on ia64 when compiled with -O1. It takes 2 more
multiplications, so it will be slower on old machines. The speedup
is about 8 cycles = 17% on A64 (amd64 and i386) with best CFLAGS
and some parallelism in the caller.
Move the evaluation of 2**k up a bit so that it doesn't compete too
much with the new polynomial evaluation. Unlike the previous
optimization, this rearrangement cannot change the result, so compilers
and CPU schedulers can do it, but they don't do it quite right yet.
This saves a whole 1 or 2 cycles on A64.
when the result is +-0. IEEE754 requires (in all rounding modes) that
if the result is +-0 then its sign is the same as that of the first
arg, but in round-towards-minus-infinity mode an uncorrected implementation
detail always reversed the sign. (The detail is that x-x with x's
sign positive gives -0 in this mode only, but the algorithm assumed
that x-x always has positive sign for such x.)
remquo() and remquof() seem to need the same fix, but I cannot test them
yet.
Use long doubles when mixing NaN args. This trick improves consistency
of results on at least amd64, so that more serious problems like the
above aren't hidden in simple regression tests by noise for the NaNs.
On amd64, hardware remainder should be used since it is about 10 times
faster than software remainder and is already used for remquo(), but
it involves using the i387 even for floats and doubles, and the i387
does NaN mixing which is better than but inconsistent with SSE NaN mixing.
Software remainder() would probably have been inconsistent with
software remainderl() for the same reason if the latter existed.
Signaling NaNs cause further inconsistencies on at least ia64 and i386.
Use __FBSDID().
exp2(i/TBLSIZE) * p(z) instead of only for the final multiplication
and addition. This fixes the code to match the comment that the maximum
error is 0.5010 ulps (except on machines that evaluate float expressions
in extra precision, e.g., i386's, where the evaluation was already
in extra precision).
Fix and expand the comment about use of double precision.
The relative roundoff error from evaluating p(z) in non-extra precision
was about 16 times larger than in exp2() because the interval length
is 16 times smaller. Its maximum was at least P1 * (1.0 ulps) *
max(|z|) ~= log(2) * 1.0 * 1/32 ~= 0.0217 ulps (1.0 ulps from the
addition in (1 + P1*z) with a cancelation error when z ~= -1/32). The
actual final maximum was 0.5313 ulps, of which 0.0303 ulps must have
come from the additional roundoff error in p(z). I can't explain why
the additional roundoff error was almost 3/2 times larger than the rough
estimate.
precision. The new polynomial has degree 4 instead of 10, and a maximum
error of 2**-30.04 ulps instead of 2**-33.15. This doesn't affect the
final error significantly; the maximum error was and is about 0.5015
ulps on i386 -O1, and the number of cases with an error of > 0.5 ulps
is increased from 13851 to 14407.
Note that the error is only this close to 0.5 ulps due to excessive
extra precision caused by compiler bugs on i386. The extra precision
could be obtained intentionally, and is useful for keeping the error
of the hyperbolic float functions below 1 ulp, since these functions
are implemented using expm1f. My recent change for scaling by 2**k
had the unintentional side effect of retaining extra precision for
longer, so callers of expm1f see errors of more like 0.0015 ulps than
0.5015 ulps, and for the hyperbolic functions this reduces the maximum
error from nearly about 2 ulps to about 0.75 ulps.
This is about 10% faster on i386 (A64). expm1* is still very slow,
but now the float version is actually significantly faster. The
algorithm is very sophisticated but not very good except on machines
with fast division.
exponent bits of the reduced result, construct 2**k (hopefully in
parallel with the construction of the reduced result) and multiply by
it. This tends to be much faster if the construction of 2**k is
actually in parallel, and might be faster even with no parallelism
since adjustment of the exponent requires a read-modify-wrtite at an
unfortunate time for pipelines.
In some cases involving exp2* on amd64 (A64), this change saves about
40 cycles or 30%. I think it is inherently only about 12 cycles faster
in these cases and the rest of the speedup is from partly-accidentally
avoiding compiler pessimizations (the construction of 2**k is now
manually scheduled for good results, and -O2 doesn't always mess this
up). In most cases on amd64 (A64) and i386 (A64) the speedup is about
20 cycles. The worst case that I found is expf on ia64 where this
change is a pessimization of about 10 cycles or 5%. The manual
scheduling for plain exp[f] is harder and not as tuned.
Details specific to expm1*:
- the saving is closer to 12 cycles than to 40 for expm1* on i386 (A64).
For some reason it is much larger for negative args.
- also convert to __FBSDID().
exponent bits of the reduced result, construct 2**k (hopefully in
parallel with the construction of the reduced result) and multiply by
it. This tends to be much faster if the construction of 2**k is
actually in parallel, and might be faster even with no parallelism
since adjustment of the exponent requires a read-modify-wrtite at an
unfortunate time for pipelines.
In some cases involving exp2* on amd64 (A64), this change saves about
40 cycles or 30%. I think it is inherently only about 12 cycles faster
in these cases and the rest of the speedup is from partly-accidentally
avoiding compiler pessimizations (the construction of 2**k is now
manually scheduled for good results, and -O2 doesn't always mess this
up). In most cases on amd64 (A64) and i386 (A64) the speedup is about
20 cycles. The worst case that I found is expf on ia64 where this
change is a pessimization of about 10 cycles or 5%. The manual
scheduling for plain exp[f] is harder and not as tuned.
This change ld128/s_exp2l.c has not been tested.
that is specialized for float precision. The new polynomial has degree
5 instead of 11, and a maximum error of 2**-27.74 ulps instead
of 2**-30.64. This doesn't affect the final error significantly; the
maximum error was and is about 0.9101 ulps on amd64 -01 and the number
of cases with an error of > 0.5 ulps is actually reduced by epsilon
despite the larger error in the polynomial.
This is about 15% faster on amd64 (A64), i386 (A64) and ia64. The asm
version is still used instead of this on i386 since it is faster and
more accurate.
cases which are used mainly by regression tests.
As usual, the cutoff for tiny args was not correctly translated to
float precision. It was 2**-54 but 2**-24 works. It must be about
2**-precision, since the error from approximating log(1+x) by x is
about the same as |x|. Exhaustive testing shows that 2**-24 gives
perfect rounding in round-to-nearest mode.
Similarly for the cutoff for being small, except this is not used by
so many other functions. It was 2**-29 but 2**-15 works. It must be
a bit smaller than sqrt(2**-precision), since the error from
approximating log(1+x) by x-x*x/2 is about the same as x*x. Exhaustive
testing shows that 2**-15 gives a maximum error of 0.5052 ulps in
round-to-nearest-mode. The algorithm for the general case is only good
for 0.8388 ulps, so this is sufficient (but it loses slightly on i386 --
then extra precision gives 0.5032 ulps for the general case).
While investigating this, I noticed that optimizing the usual case by
falling into a middle case involving a simple polynomial evaluation
(return x-x*x/2 instead of x here) is not such a good idea since it
gives an enormous pessimization of tinier args on machines for which
denormals are slow. Float x*x/2 is denormal when |x| ~< 2**-64 and
x*x/2 is evaluated in float precision, so it can easily be denormal
for normal x. This is even more interesting for general polynomial
evaluations. Multiplying out large powers of x is normally a good
optimization since it reduces dependencies, but it creates denormals
starting with quite large x.
forget to translate "float" to "double".
ucbtest didn't detect the bug, but exhaustive testing of the float
case relative to the double case eventually did. The bug only affects
args x with |x| ~> 2**19*(pi/2) on non-i386 (i386 is broken in a
different way for large args).
it should never have existed and it has not been used for many years
(floats are reduced faster using doubles). All relevant changes (just
the workaround for broken assignment) have been merged to the double
version.
there is a problem with non-floats (when i386 defaults to extra
precision). This essentially restores yesterday's behaviour for doubles
on i386 (since generic rint() isn't used and everywhere else assumed
working assignment), but for arches that use the generic rint() it
finishes restoring some of 1995's behaviour (don't waste time doing
unnecessary store/load).
variable hack for exp2f() only.
The volatile variable had a surprisingly large cost for exp2f() -- 19
cycles or 15% on i386 in the worst case observed. This is only partly
explained by there being several references to the variable, only one
of which benefited from it being volatile. Arches that have working
assignment are likely to benefit even more from not having any volatile
variable.
exp2() now has a chance of working with extra precision on i386.
exp2() has even more references to the variable, so it would have been
pessimized more by simply declaring the variable as volatile. Even
the temporary volatile variable for STRICT_ASSIGN costs 5-10% on i386,
(A64) so I will change STRICT_ASSIGN() to do an ordinary assignment
until i386 defaults to extra precision.
instead of a volatile cast hack for the float version only. The cast
hack broke with gcc-4, but this was harmless since the float version
hasn't been used for a few years. Merge from the float version so
that the double version has a chance of working on i386 with extra
precision.
See k_rem_pio2f.c rev.1.8 for the original hack.
Convert to _FBSDID().
hack for log1pf() only. The cast hack broke with gcc-4, resulting in
~1 million errors of more than 1 ulp, with a maximum error of ~1.5 ulps.
Now the maximum error for log1pf() on i386 is 0.5034 ulps again (this
depends on extra precision), and log1p() has a chance of working with
extra precision.
See s_log1pf.c 1.8 for the original hack. (It claims only 62343 large
errors).
Convert to _FBSDID(). Another thing broken with gcc-4 is the static
const hack used for rcsids.
around assignments not working for gcc on i386. Now volatile hacks
for rint() and rintf() don't needlessly pessimize so many arches
and the remaining pessimizations (for arm and powerpc) can be avoided
centrally.
This cleans up after s_rint.c 1.3 and 1.13 and s_rintf.c 1.3 and 1.9:
- s_rint.c 1.13 broke 1.3 by only using a volatile cast hack in 1 place
when it was needed in 2 places, and the volatile cast hack stopped
working with gcc-4. These bugs only affected correctness tests on
i386 since i386 normally uses asm rint() and doesn't support the
extra precision mode that would break assignments of doubles.
- s_rintf.c 1.9 improved(?) on 1.3 by using a volatile variable hack
instead of an extra-precision variable hack, but it declared 2
variables as volatile when only 1 variable needed to be volatile.
This only affected speed tests on i386 since i386 uses asm rintf().
long doubles (i386, amd64, ia64) and one for machines with 128-bit
long doubles (sparc64). Other platforms use the double version.
I've only done runtime testing on i386.
Thanks to bde@ for helpful discussions and bugfixes.
assignments and casts don't clip extra precision, if any. The
implementation is to assign to a temporary volatile variable and read
the result back to assign to the original lvalue.
lib/msun currently 2 different hard-coded hacks to avoid the problem
in just a few places and needs it in a few more places. One variant
uses volatile for the original lvalue. This works but is slower than
necessary. Another temporarily casts the lvalue to volatile. This
broke with gcc-4.2.1 or earlier (gcc now stores to the lvalue but
doesn't load from it).
my original implementation made both use the same code. Unfortunately,
this meant libm depended on a vendor header at compile time and previously-
unexposed vendor bits in libc at runtime.
Hence, I just wrote my own version of the relevant vendor routine. As it
turns out, mine has a factor of 8 fewer of lines of code, and is a bit more
readable anyway. The strtod() and *scanf() routines still use vendor code.
Reviewed by: bde
someone thought it would be a good idea to copy z_abs() to libm in 1994.
However, it's never been declared or documented anywhere, and I'm
reasonably confident that nobody uses it.
Discussed with: bde, deischen, kan
I hope that this and the i386 version of it will not be needed, but
this is currently about 16 cycles or 36% faster than the C version,
and the i386 version is about 8 cycles or 19% faster than the C
version, due to poor optimization of the C version.
adds two new directories in msun: ld80 and ld128. These are for
long double functions specific to the 80-bit long double format
used on x86-derived architectures, and the 128-bit format used on
sparc64, respectively.
hundreds of similar aliasing bugs, but all except this one seem to have
been fixed by Cygnus and/or NetBSD before the modified version of fdlibm
was imported into FreeBSD in 1994.
PR: standards/113147
Submitted by: Steve Kargl <sgk@troutmask.apl.washington.edu>
involving moving the check for x == 0). The savings in cycles are
smaller for cbrtf() than for cbrt(), and positive in all measured cases
with gcc-3.4.4, but still very machine/compiler-dependent.
Warning, after symbol versioning is enabled, going back is not easy
(use WITHOUT_SYMVER at your own risk).
Change the default thread library to libthr.
There most likely still needs to be a version bump for at least the
thread libraries. If necessary, this will happen later.
that ints have >= 31 value bits elsewhere. s/int/int32_t/ seems to
have been done too globally for all other files in msun/src before
msun/ was imported into FreeBSD.
Minor fixes in comments.
e_lgamma_r.c:
Describe special cases in more detail:
- exception for lgamma(0) and lgamma(neg.integer)
- lgamma(-Inf) = Inf. This is wrong but is required by C99 Annex F. I
hope to change this.
(1) tgamma(-Inf) returned +Inf and failed to raise any exception, but
should always have raised an exception, and should behave like
tgamma(negative integer).
(2) tgamma(negative integer) returned +Inf and raised divide-by-zero,
but should return NaN and raise "invalid" on any IEEEish system.
(3) About half of the 2**52 negative intgers between -2**53 and -2**52
were misclassified as non-integers by using floor(x + 0.5) to round
to nearest, so tgamma(x) was wrong (+-0 instead of +Inf and now NaN)
on these args. The floor() expression is hard to use since rounding
of (x + 0.5) may give x or x + 1, depending on |x| and the current
rounding mode. The fixed version uses ceil(x) to classify x before
operating on x and ends up being more efficient since ceil(x) is
needed anyway.
(4) On at least the problematic args in (3), tgamma() raised a spurious
inexact.
(5) tgamma(large positive) raised divide-by-zero but should raise overflow.
(6) tgamma(+Inf) raised divide-by-zero but should not raise any exception.
(7) Raise inexact for tiny |x| in a way that has some chance of not being
optimized away.
The fix for (5) and (6), and probably for (2), also prevents -O optimizing
away the exception.
PR: 112180 (2)
Standards: Annex F in C99 (IEC 60559 binding) requires (1), (2) and (6).
values in more detail, and change the style of this comment to be closer
to fdlibm and C99:
- tgamma(-Inf) was undocumented and is wrong (+Inf, should be NaN)
- tgamma(negative integer) is as intended (+Inf) but not best for IEEE-754
(NaN)
- tgamma(-0) was documented as being wrong (+Inf) but was correct (-Inf)
- documentation of setting of exceptions (overflow, etc.) was more
complete here than in most of libm, but was further from matching
the actual setting than in most of libm, due to various bugs here
(primarily, always evaluating +Inf one/zero and getting unwanted
divide-by-zero exceptions from this). Now the actual behaviour with
gcc -O0 is documented. Optimization still breaks setting of exceptions
all over libm, so nothing can depend on this working.
- tgamma(NaN)'s exception was documented as being wrong (invalid) but was
correct (no exception with IEEEish NaNs).
Finish (?) rev.1.5. gamma was not renamed to tgamma in one place.
Finish (?) rev.1.6. errno.h was not completely removed.
Details: As a side-effect of restoring a saved FP environment,
fesetenv() overwrites the tag word, which indicates which i387
registers are in use. Normally this isn't a problem because
the calling convention requires the register stack to be empty
on function entry and exit. However, fesetenv() is inlined, so we
need to tell gcc explicitly that the i387 registers get clobbered.
PR: 85101
In e_log.c, there was just a off-by-1 (1 ulp) error in the comment
about the threshold. The precision of the threshold is unimportant,
but the magic numbers in the code are easier to understand when the
threshold is described precisely.
In e_logf.c, mistranslation of the magic numbers gave an off-by-1
(1 * 16 ulps) error in the intended negative bound for the threshold
and an off-by-7 (7 * 16 ulps) error in the intended positive bound for
the threshold, and the intended bounds were not translated from the
double precision bounds so they were unnecessarily small by a factor
of about 2048.
The optimization of using the simple Taylor approximation for args
near a power of 2 is dubious since it only applies to a relatively
small proportion of args, but if it is done then doing it 2048 times
as often _may_ be more efficient. (My benchmarks show unexplained
dependencies on the data that increase with further optimizations
in this area.)
2**-28 as a side effect, by merging with the float precision version
of tanh() and the double precision version of sinh().
For tiny x, tanh(x) ~= x, and we used the expression x*(one+x) to
return this value (x) and set the inexact flag iff x != 0. This
doesn't work on ia64 since gcc -O does the dubious optimization
x*(one+x) = x+x*x so as to use fma, so the sign of -0.0 was lost.
Instead, handle tiny x in the same as sinh(), although this is imperfect:
- return x directly and set the inexact flag in a less efficient way.
- increased the threshold for non-tinyness from 2**-55 to 2**-28 so that
many more cases are optimized than are pessimized.
Updated some comments and fixed bugs in others (ranges for half-open
intervals mostly had the open end backwards, and there were nearby style
bugs).
functions are only for compatibility with obsolete standards. They
shouldn't be used, so they shouldn't be optimized. Use the generic
versions instead.
This fixes scalbf() as a side effect. The optimized asm version left
garbage on the FP stack. I fixed the corresponding bug in the optimized
asm scalb() and scalbn() in 1996. NetBSD fixed it in scalb(), scalbn()
and scalbnf() in 1999 but missed fixing it in scalbf(). Then in 2005
the bug was reimplemented in FreeBSD by importing NetBSD's scalbf().
The generic versions have slightly different error handling:
- the asm versions blindly round the second parameter to a (floating
point) integer and proceed, while the generic versions return NaN
if this rounding changes the value. POSIX permits both behaviours
(these functions are XSI extensions and the behaviour for a bogus
non-integral second parameter is unspecified). Apart from this
and the bug in scalbf(), the behaviour of the generic versions seems
to be identical. (I only exhusatively tested
generic_scalbf(1.0F, anyfloat) == asm_scalb(1.0F, anyfloat). This
covers many representative corner cases involving NaNs and Infs but
doesn't test exception flags. The brokenness of scalbf() showed up
as weird behaviour after testing just 7 integer cases sequentially.)
to scalbf(), but ldexpf() cannot be implemented in that way since the
types of the second parameter differ. ldexpf() can be implemented as
a weak or strong reference to scalbnf() (*) but that was already done
long before rev.1.10 was committed. The old implementation uses a
reference, so rev.1.10 had no effect on applications. The C files for
the scalb() family are not used for amd64 or i386, so rev.1.10 had even
less effect for these arches.
(*) scalbnf() raises the radix to the given exponent, while ldexpf()
raises 2 to the given exponent. Thus the functions are equivalent
except possibly for their error handling iff the radix is 2. Standards
more or less require identical error handling. Under FreeBSD, the
functions are equivalent except for more details being missing in
scalbnf()'s man page.
commit broke the 2**24 cases where |x| > DBL_MAX/2. There are exponent
range problems not just for denormals (underflow) but for large values
(overflow). Doubles have more than enough exponent range to avoid the
problems, but I forgot to convert enough terms to double, so there was
an x+x term which was sometimes evaluated in float precision.
Unfortunately, this is a pessimization with some combinations of systems
and compilers (it makes no difference on Athlon XP's, but on Athlon64's
it gives a 5% pessimization with gcc-3.4 but not with gcc-3.3).
Exlain the problem better in comments.
algorithm for the second step significantly to also get a perfectly
rounded result in round-to-nearest mode. The resulting optimization
is about 25% on Athlon64's and 30% on Athlon XP's (about 25 cycles
out of 100 on the former).
Using extra precision, we don't need to do anything special to avoid
large rounding errors in the third step (Newton's method), so we can
regroup terms to avoid a division, increase clarity, and increase
opportunities for parallelism. Rearrangement for parallelism loses
the increase in clarity. We end up with the same number of operations
but with a division reduced to a multiplication.
Using specifically double precision, there is enough extra precision
for the third step to give enough precision for perfect rounding to
float precision provided the previous steps are accurate to 16 bits.
(They were accurate to 12 bits, which was almost minimal for imperfect
rounding in the old version but would be more than enough for imperfect
rounding in this version (9 bits would be enough now).) I couldn't
find any significant time optimizations from optimizing the previous
steps, so I decided to optimize for accuracy instead. The second step
needed a division although a previous commit optimized it to use a
polynomial approximation for its main detail, and this division dominated
the time for the second step. Use the same Newton's method for the
second step as for the third step since this is insignificantly slower
than the division plus the polynomial (now that Newton's method only
needs 1 division), significantly more accurate, and simpler. Single
precision would be precise enough for the second step, but doesn't
have enough exponent range to handle denormals without the special
grouping of terms (as in previous versions) that requires another
division, so we use double precision for both the second and third
steps.
distributed non-large args, this saves about 14 of 134 cycles for
Athlon64s and about 5 of 199 cycles for AthlonXPs.
Moved the check for x == 0 inside the check for subnormals. With
gcc-3.4 on uniformly distributed non-large args, this saves another
5 cycles on Athlon64s and loses 1 cycle on AthlonXPs.
Use INSERT_WORDS() and not SET_HIGH_WORD() when converting the first
approximation from bits to double. With gcc-3.4 on uniformly distributed
non-large args, this saves another 4 cycles on both Athlon64s and and
AthlonXPs.
Accessing doubles as 2 words may be an optimization on old CPUs, but on
current CPUs it tends to cause extra operations and pipeline stalls,
especially for writes, even when only 1 of the words needs to be accessed.
Removed an unused variable.
function approximation for the second step. The polynomial has degree
2 for cbrtf() and 4 for cbrt(). These degrees are minimal for the final
accuracy to be essentially the same as before (slightly smaller).
Adjust the rounding between steps 2 and 3 to match. Unfortunately,
for cbrt(), this breaks the claimed accuracy slightly although incorrect
rounding doesn't. Claim less accuracy since its not worth pessimizing
the polynomial or relying on exhaustive testing to get insignificantly
more accuracy.
This saves about 30 cycles on Athlons (mainly by avoiding 2 divisions)
so it gives an overall optimization in the 10-25% range (a larger
percentage for float precision, especially in 32-bit mode, since other
overheads are more dominant for double precision, surprisingly more
in 32-bit mode).
- in preparing for the third approximation, actually make t larger in
magnitude than cbrt(x). After chopping, t must be incremented by 2
ulps to make it larger, not 1 ulp since chopping can reduce it by
almost 1 ulp and it might already be up to half a different-sized-ulp
smaller than cbrt(x). I have not found any cases where this is
essential, but the think-time error bound depends on it. The relative
smallness of the different-sized-ulp limited the bug. If there are
cases where this is essential, then the final error bound would be
5/6+epsilon instead of of 4/6+epsilon ulps (still < 1).
- in preparing for the third approximation, round more carefully (but
still sloppily to avoid branches) so that the claimed error bound of
0.667 ulps is satisfied in all cases tested for cbrt() and remains
satisfied in all cases for cbrtf(). There isn't enough spare precision
for very sloppy rounding to work:
- in cbrt(), even with the inadequate increment, the actual error was
0.6685 in some cases, and correcting the increment increased this
a little. The fix uses sloppy rounding to 25 bits instead of very
sloppy rounding to 21 bits, and starts using uint64_t instead of 2
words for bit manipulation so that rounding more bits is not much
costly.
- in cbrtf(), the 0.667 bound was already satisfied even with the
inadequate increment, but change the code to almost match cbrt()
anyway. There is not enough spare precision in the Newton
approximation to double the inadequate increment without exceeding
the 0.667 bound, and no spare precision to avoid this problem as
in cbrt(). The fix is to round using an increment of 2 smaller-ulps
before chopping so that an increment of 1 ulp is enough. In cbrt(),
we essentially do the same, but move the chop point so that the
increment of 1 is not needed.
Fixed comments to match code:
- in cbrt(), the second approximation is good to 25 bits, not quite 26 bits.
- in cbrt(), don't claim that the second approximation may be implemented
in single precision. Single precision cannot handle the full exponent
range without minor but pessimal changes to renormalize, and although
single precision is enough, 25 bit precision is now claimed and used.
Added comments about some of the magic for the error bound 4/6+epsilon.
I still don't understand why it is 4/6+ and not 6/6+ ulps.
Indent comments at the right of code more consistently.
the second step of approximating cbrt(x). It turns out to be neither
very magic not nor very good. It is just the (2,2) Pade approximation
to 1/cbrt(r) at r = 1, arranged in a strange way to use fewer operations
at a cost of replacing 4 multiplications by 1 division, which is an
especially bad tradeoff on machines where some of the multiplications
can be done in parallel. A Remez rational approximation would give
at least 2 more bits of accuracy, but the (2,2) Pade approximation
already gives 6 more bits than needed. (Changed the comment which
essentially says that it gives 3 more bits.)
Lower order Pade approximations are not quite accurate enough for
double precision but are plenty for float precision. A lower order
Remez rational approximation might be enough for double precision too.
However, rational approximations inherently require an extra division,
and polynomial approximations work well for 1/cbrt(r) at r = 1, so I
plan to switch to using the latter. There are some technical
complications that tend to cost a division in another way.
This gives an optimization of between 9 and 22% on Athlons (largest
for cbrt() on amd64 -- from 205 to 159 cycles).
We extracted the sign bit and worked with |x|, and restored the sign
bit as the last step. We avoided branches to a fault by using accesses
to FP values as bits to clear and restore the sign bit. Avoiding
branches is usually good, but the bit access macros are not so good
(especially for setting FP values), and here they always caused pipeline
stalls on Athlons. Even using branches would be faster except on args
that give perfect branch misprediction, since only mispredicted branches
cause stalls, but it possible to avoid touching the sign bit in FP
values at all (except to preserve it in conversions from bits to FP
not related to the sign bit). Do this. The results are identical
except in 2 of the 3 unsupported rounding modes, since all the
approximations use odd rational functions so they work right on strictly
negative values, and the special case of -0 doesn't use an approximation.
<cbrt(x) in bits> ~= <x in bits>/3 + BIAS.
Keep the large comments only in the double version as usual.
Fixed some style bugs (mainly grammar and spelling errors in comments).
It was because I forgot to translate the part of the double precision
algorithm that chops t so that t*t is exact. Now the maximum error
is the same as for double precision (almost exactly 2.0/3 ulps).
The maximum error was 3.56 ulps.
The bug was another translation error. The double precision version
has a comment saying "new cbrt to 23 bits, may be implemented in
precision". This means exactly what it says -- that the 23 bit second
approximation for the double precision cbrt() may be implemented in
single (i.e., float) precision. It doesn't mean what the translation
assumed -- that this approximation, when implemented in float precision,
is good enough for the the final approximation in float precision.
First, float precision needs a 24 bit approximation. The "23 bit"
approximation is actually good to 24 bits on float precision args, but
only if it is evaluated in double precision. Second, the algorithm
requires a cleanup step to ensure its error bound.
In float precision, any reasonable algorithm works for the cleanup
step. Use the same algorithm as for double precision, although this
is much more than enough and is a significant pessimization, and don't
optimize or simplify anything using double precision to implement the
float case, so that the whole double precision algorithm can be verified
in float precision. A maximum error of 0.667 ulps is claimed for cbrt()
and the max for cbrtf() using the same algorithm shouldn't be different,
but the actual max for cbrtf() on amd64 is now 0.9834 ulps. (On i386
-O1 the max is 0.5006 (down from < 0.7) due to extra precision.)
The threshold for not being tiny was too small. Use the usual 2**-12
threshold. As for sinhf, use a different method (now the same as for
sinhf) to set the inexact flag for tiny nonzero x so that the larger
threshold works, although this method is imperfect. As for sinhf,
this change is not just an optimization, since the general code that
we fell into has accuracy problems even for tiny x. On amd64, avoiding
it fixes tanhf on 2*13495596 args with errors of between 1 and 1.3
ulps and thus reduces the total number of args with errors of >= 1 ulp
from 37533748 to 5271278; the maximum error is unchanged at 2.2 ulps.
The magic number 22 is log(DBL_MAX)/2 plus slop. This is bogus for
float precision. Use 9 (log(FLT_MAX)/2 plus less slop than for
double precision). Unlike for coshf and tanhf, this is just an
optimization, and MAX isn't misspelled EPSILON in the commit log.
I started testing with nonstandard rounding modes, and verified that
the chosen thresholds work for all modes modulo problems not related
to thresholds. The best thresholds are not very dependent on the mode,
at least for tanhf.
shares its low half with pio2_hi. pio2_hi is rounded down although
rounding to nearest would be a tiny bit better, so pio4_hi must be
rounded down too. It was rounded to nearest, which happens to be
different in float precision but the same in double precision.
This fixes about 13.5 million errors of more than 1 ulp in asinf().
The largest error was 2.81 ulps on amd64 and 2.57 ulps on i386 -O1.
Now the largest error is 0.93 ulps on amd65 and 0.67 ulps on i386 -O1.
sqrt(2)/2-1. For log1p(), fixed the approximation to sqrt(2)/2-1.
The end result is to fix an error of 1.293 ulps in
log1pf(0.41421395540 (hex 0x3ed413da))
and an error of 1.783 ulps in
log1p(-0.292893409729003961761) (hex 0x12bec4 00000001)).
The former was the only error of > 1 ulp for log1pf() and the latter
is the only such error that I know of for log1p().
The approximations don't need to be very accurate, but the last 2 need
to be related to the first and be rounded up a little (even more than
1 ulp for sqrt(2)/2-1) for the following implementation-detail reason:
when the arg (x) is not between (the approximations to) sqrt(2)/2-1
and sqrt(2)-1, we commit to using a correction term, but we only
actually use it if 1+x is between sqrt(2)/2 and sqrt(2) according to
the first approximation. Thus we must ensure that
!(sqrt(2)/2-1 < x < sqrt(2)-1) implies !(sqrt(2)/2 < x+1 < sqrt(2)),
where all the sqrt(2)'s are really slightly different approximations
to sqrt(2) and some of the "<"'s are really "<="'s. This was not done.
In log1pf(), the last 2 approximations were rounded up by about 6 ulps
more than needed relative to a good approximation to sqrt(2), but the
actual approximation to sqrt(2) was off by 3 ulps. The approximation
to sqrt(2)-1 ended up being 4 ulps too small, so the algoritm was
broken in 4 cases. The result happened to be broken in 1 case. This
is fixed by using a natural approximation to sqrt(2) and derived
approximations for the others.
In logf(), all the approximations made sense, but the approximation
to sqrt(2)/2-1 was 2 ulps too small (a tiny amount, since we compare
with a granularity of 2**32 ulps), so the algorithm was broken in 2
cases. The result was broken in 1 case. This is fixed by rounding
up the approximation to sqrt(2)/2-1 by 2**32 ulps, so 2**32 cases are
now handled a little differently (still correctly according to my
assertion that the approximations don't need to be very accurate, but
this has not been checked).
on assignment.
Extra precision on i386's broke hi+lo decomposition in the usual way.
It caused all except 1 of the 62343 errors of more than 1 ulp for
log1pf() on i386's with gcc -O [-fno-float-store].
according to the highest nonzero bit in a denormal was missing.
fdlibm ilogbf() and ilogb() have always had the adjustment, but only
use a small part of their method for handling denormals; use the
normalization method in log[f]() for the main part.
It was lost in rev.1.9. The log message for rev.1.9 says that the
special case of +-0 is handled twice, but it was only handled once,
so it became unhandled, and this happened to break half of the cases
that return +-0:
- round-towards-minus-infinity: 0 < x < 1: result was -0 not 0
- round-to-nearest: -0.5 <= x < 0: result was 0 not -0
- round-towards-plus-infinity: -1 < x < 0: result was 0 not -0
- round-towards-zero: -1 < x < 0: result was 0 not -0
TWO52[sx] to trick gcc into correctly converting TWO52[sx]+x to double
on assignment to "double w", force a correct assignment by assigning
to *(double *)&w. This is cleaner and avoids the double rounding
problem on machines that evaluate double expressions in double
precision. It is not necessary to convert w-TWO52[sx] to double
precision on return as implied in the comment in rev.1.3, since
the difference is exact.
(1) In round-to-nearest mode, on all machines, fdlibm rint() never
worked for |x| = n+0.75 where n is an even integer between 262144
and 524286 inclusive (2*131072 cases). To avoid double rounding
on some machines, we begin by adjusting x to a value with the 0.25
bit not set, essentially by moving the 0.25 bit to a lower bit
where it works well enough as a guard, but we botched the adjustment
when log2(|x|) == 18 (2*2**52 cases) and ended up just clearing
the 0.25 bit then. Most subcases still worked accidentally since
another lower bit serves as a guard. The case of odd n worked
accidentally because the rounding goes the right way then. However,
for even n, after mangling n+0.75 to 0.5, rounding gives n but the
correct result is n+1.
(2) In round-towards-minus-infinity mode, on all machines, fdlibm rint()
never for x = n+0.25 where n is any integer between -524287 and
-262144 inclusive (262144 cases). In these cases, after mangling
n+0.25 to n, rounding gives n but the correct result is n-1.
(3) In round-towards-plus-infinity mode, on all machines, fdlibm rint()
never for x = n+0.25 where n is any integer between 262144 and
524287 inclusive (262144 cases). In these cases, after mangling
n+0.25 to n, rounding gives n but the correct result is n+1.
A variant of this bug was fixed for the float case in rev.1.9 of s_rintf.c,
but the analysis there is incomplete (it only mentions (1)) and the fix
is buggy.
Example of the problem with double rounding: rint(1.375) on a machine
which evaluates double expressions with just 1 bit of extra precision
and is in round-to-nearest mode. We evaluate the result using
(double)(2**52 + 1.375) - 2**52. Evaluating 2**52 + 1.375 in (53+1) bit
prcision gives 2**52 + 1.5 (first rounding). (Second) rounding of this
to double gives 2**52 + 2.0. Subtracting 2**52 from this gives 2.0 but
we want 1.0. Evaluating 2**52 + 1.375 in double precision would have
given the desired intermediate result of 2**52 + 1.0.
The double rounding problem is relatively rare, so the botched adjustment
can be fixed for most machines by removing the entire adjustment. This
would be a wrong fix (using it is 1 of the bugs in rev.1.9 of s_rintf.c)
since fdlibm is supposed to be generic, but it works in the following cases:
- on all machines that evaluate double expressions in double precision,
provided either long double has the same precision as double (alpha,
and i386's with precision forced to double) or my earlier fix to use
a long double 2**52 is modified to avoid using long double precision.
- on all machines that evaluate double expressions in many more than 11
bits of extra precision. The 1 bit of extra precision in the example
is the worst case. With N bits of extra precision, it sufices to
adjust the bit N bits below the 0.5 bit. For N >= about 52 there is
no such bit so the adjustment is both impossible and unnecessary. The
fix in rev.1.9 of s_rintf.c apparently depends on corresponding magic
in float precision: on all supported machines N is either 0 or >= 24,
so double rounding doesn't occur in practice.
- on all machines that don't use fdlibm rint*() (i386's).
So under FreeBSD, the double rounding problem only affects amd64 now, but
should only affect i386 in future (when double expressions are evaluated
in long double precision).
- in round-towards-minus-infinity mode, on all machines, roundf(x) never
worked for 0 < |x| < 0.5 (2*0x3effffff cases in all, or almost half of
float space). It was -0 for 0 < x < 0.5 and 0 for -0.5 < x < 0, but
should be 0 and -0, respectively. This is because t = ceilf(|x|) = 1
for these args, and when we adjust t from 1 to 0 by subtracting 1, we
get -0 in this rounding mode, but we want and expected to get 0.
- in round-towards-minus-infinity, round towards zero and round-to-nearest
modes, on machines that evaluate float expressions in float precision
(most machines except i386's), roundf(x) never worked for |x| =
<float value immediately below 0.5> (2 cases in all). It was +-1 but
should have been +-0. This is because t = ceilf(|x|) = 1 for these
args, and when we try to classify |x| by subtracting it from 1 we
get an unexpected rounding error -- the result is 0.5 after rounding
to float in all 3 rounding modes, so we we have forgotten the
difference between |x| and 0.5 and end up returning the same value
as for +-0.5.
The fix is to use floorf() instead of ceilf() and to add 1 instead of
-1 in the adjustment. With floorf() all the expressions used are
always evaluated exactly so there are no rounding problems, and with
adjustments of +1 we don't go near -0 when adjusting.
Attempted to fix round() and roundl() by cloning the fix for roundf().
This has only been tested for round(), only on args representable as
floats. Double expressions are evaluated in double precision even on
i386's, so round(0.5-epsilon) was broken even on i386's. roundl()
must be completely broken on i386's since long double precision is not
really supported. There seem to be no other dependencies on the
precision.
k_tanf.c but with different details.
The polynomial is odd with degree 13 for tanf() and odd with degree
9 for sinf(), so the details are not very different for sinf() -- the
term with the x**11 and x**13 coefficients goes awaym and (mysteriously)
it helps to do the evaluation of w = z*z early although moving it later
was a key optimization for tanf(). The details are different but simpler
for cosf() because the polynomial is even and of lower degree.
On Athlons, for uniformly distributed args in [-2pi, 2pi], this gives
an optimization of about 4 cycles (10%) in most cases (13% for sinf()
on AXP, but 0% for cosf() with gcc-3.3 -O1 on AXP). The best case
(sinf() with gcc-3.4 -O1 -fcaller-saves on A64) now takes 33-39 cycles
(was 37-45 cycles). Hardware sinf takes 74-129 cycles. Despite
being fine tuned for Athlons, the optimization is even larger on
some other arches (about 15% on ia64 (pluto2) and 20% on alpha (beast)
with gcc -O2 -fomit-frame-pointer).
cosf(x) is supposed to return something like x when x is a NaN, and
we actually fairly consistently return x-x which is normally very like
x (on i386 and and it is x if x is a quiet NaN and x with the quiet bit
set if x is a signaling NaN. Rev.1.10 broke this by normalising x to
fabsf(x). It's not clear if fabsf(x) is should preserve x if x is a NaN,
but it actually clears the sign bit, and other parts of the code depended
on this.
The bugs can be fixed by saving x before normalizing it, and using the
saved x only for NaNs, and using uint32_t instead of int32_t for ix
so that negative NaNs are not misclassified even if fabsf() doesn't
clear their sign bit, but gcc pessimizes the saving very well, especially
on Athlon XPs (it generates extra loads and stores, and mixes use of
the SSE and i387, and this somehow messes up pipelines). Normalizing
x is not a very good optimization anyway, so stop doing it. (It adds
latency to the FPU pipelines, but in previous versions it helped except
for |x| <= 3pi/4 by simplifying the integer pipelines.) Use the same
organization as in s_sinf.c and s_tanf.c with some branches reordered.
These changes combined recover most of the performance of the unfixed
version on A64 but still lose 10% on AXP with gcc-3.4 -O1 but not with
gcc-3.3 -O1.
that was used doesn't work normally here, since we want to be able to
multiply `hi' by the exponent of x _exactly_, and the exponent of x has
more than 7 significant bits for most denormal x's, so the multiplication
was not always exact despite a cloned comment claiming that it was. (The
comment is correct in the double precision case -- with the normal 33+53
bit decomposition the exponent can have 20 significant bits and the extra
bit for denormals is only the 11th.)
Fixing this had little or no effect for denormals (I think because
more precision is inherently lost for denormals than is lost by roundoff
errors in the multiplication).
The fix is to reduce the precision of the decomposition to 16+24 bits.
Due to 2 bugs in the old deomposition and numerical accidents, reducing
the precision actually increased the precision of hi+lo. The old hi+lo
had about 39 bits instead of at least 41 like it should have had.
There were off-by-1-bit errors in each of hi and lo, apparently due
to mistranslation from the double precision hi and lo. The correct
16 bit hi happens to give about 19 bits of precision, so the correct
hi+lo gives about 43 bits instead of at least 40. The end result is
that expf() is now perfectly rounded (to nearest) except in 52561 cases
instead of except in 67027 cases, and the maximum error is 0.5013 ulps
instead of 0.5023 ulps.
Instead of echoing the code in a comment, try to describe why we split
up the evaluation in a special way.
The new optimization is mostly to move the evaluation of w = z*z later
so that everything else (except z = x*x) doesn't have to wait for w.
On Athlons, FP multiplication has a latency of 4 cycles so this
optimization saves 4 cycles per call provided no new dependencies are
introduced. Tweaking the other terms in to reduce dependencies saves
a couple more cycles in some cases (more on AXP than on A64; up to 8
cycles out of 56 altogether in some cases). The previous version had
a similar optimization for s = z*x. Special optimizations like these
probably have a larger effect than the simple 2-way vectorization
permitted (but not activated by gcc) in the old version, since 2-way
vectorization is not enough and the polynomial's degree is so small
in the float case that non-vectorizable dependencies dominate.
On an AXP, tanf() on uniformly distributed args in [-2pi, 2pi] now
takes 34-55 cycles (was 39-59 cycles).
of between 1.0 and 1.8509 ulps for lgammaf(x) with x between -2**-21 and
-2**-70.
As usual, the cutoff for tiny args was not correctly translated to
float precision. It was 2**-70 but 2**-21 works. Not as usual, having
a too-small threshold was worse than a pessimization. It was just a
pessimization for (positive) args between 2**-70 and 2**-21, but for
the first ~50 million (negative) args below -2**-70, the general code
overflowed and gave a result of infinity instead of correct (finite)
results near 70*log(2). For the remaining ~361 million negative args
above -2**21, the general code gave almost-acceptable errors (lgamma[f]()
is not very accurate in general) but the pessimization was larger than
for misclassified tiny positive args.
Now the max error for lgammaf(x) with |x| < 2**-21 is 0.7885 ulps, and
speed and accuracy are almost the same for positive and negative args
in this range. The maximum error overall is still infinity ulps.
A cutoff of 2**-70 is probably wastefully small for the double precision
case. Smaller cutoffs can be used to reduce the max error to nearly
0.5 ulps for tiny args, but this is useless since the general algrorithm
for nearly-tiny args is not nearly that accurate -- it has a max error of
about 1 ulp.
gives a tiny but hopefully always free optimization in the 2 quadrants
to which it applies. On Athlons, it reduces maximum latency by 4 cycles
in these quadrants but has usually has a smaller effect on total time
(typically ~2 cycles (~5%), but sometimes 8 cycles when the compiler
generates poor code).
of the function name.
Added my (non-)copyright.
In k_tanf.c, added the first set of redundant parentheses to control
grouping which was claimed to be added in the previous commit.
returning float). The functions are renamed from __kernel_{cos,sin}f()
to __kernel_{cos,sin}df() so that misuses of them will cause link errors
and not crashes.
This version is an almost-routine translation with no special optimizations
for accuracy or efficiency. The not-quite-routine part is that in
__kernel_cosf(), regenerating the minimax polynomial with double
precision coefficients gives a coefficient for the x**2 term that is
not quite -0.5, so the literal 0.5 in the code and the related `hz'
variable need to be modified; also, the special code for reducing the
error in 1.0-x**2*0.5 is no longer needed, so it is convenient to
adjust all the logic for the x**2 term a little. Note that without
extra precision, it would be very bad to use a coefficient of other
than -0.5 for the x**2 term -- the old version depends on multiplication
by -0.5 being infinitely precise so as not to need even more special
code for reducing the error in 1-x**2*0.5.
This gives an unimportant increase in accuracy, from ~0.8 to ~0.501
ulps. Almost all of the error is from the final rounding step, since
the choice of the minimax polynomials so that their contribution to the
error is a bit less than 0.5 ulps just happens to give contributions that
are significantly less (~.001 ulps).
An Athlons, for uniformly distributed args in [-2pi, 2pi], this gives
overall speed increases in the 10-20% range, despite giving a speed
decrease of typically 19% (from 31 cycles up to 37) for sinf() on args
in [-pi/4, pi/4].
- Remove dead code that I forgot to remove in the previous commit.
- Calculate the sum of the lower terms of the polynomial (divided by
x**5) in a single expression (sum of odd terms) + (sum of even terms)
with parentheses to control grouping. This is clearer and happens to
give better instruction scheduling for a tiny optimization (an
average of about ~0.5 cycles/call on Athlons).
- Calculate the final sum in a single expression with parentheses to
control grouping too. Change the grouping from
first_term + (second_term + sum_of_lower_terms) to
(first_term + second_term) + sum_of_lower_terms. Normally the first
grouping must be used for accuracy, but extra precision makes any
grouping give a correct result so we can group for efficiency. This
is a larger optimization (average 3-4 cycles/call or 5%).
- Use parentheses to indicate that the C order of left to right evaluation
is what is wanted (for efficiency) in a multiplication too.
The old fdlibm code has several optimizations related to these. 2
involve doing an extra operation that can be done almost in parallel
on some superscalar machines but are pessimizations on sequential
machines. Others involve statement ordering or expression grouping.
All of these except the ordering for the combining the sums of the odd
and even terms seem to be ideal for Athlons, but parallelism is still
limited so all of these optimizations combined together with the ones
in this commit save only ~6-8 cycles (~10%).
On an AXP, tanf() on uniformly distributed args in [-2pi, 2pi] now
takes 39-59 cycles. I don't know of any more optimizations for tanf()
short of writing it all in asm with very MD instruction scheduling.
Hardware fsin takes 122-138 cycles. Most of the optimizations for
tanf() don't work very well for tan[l](). fdlibm tan() now takes
145-365 cycles.
A single polynomial approximation for tan(x) works in infinite precision
up to |x| < pi/2, but in finite precision, to restrict the accumulated
roundoff error to < 1 ulp, |x| must be restricted to less than about
sqrt(0.5/((1.5+1.5)/3)) ~= 0.707. We restricted it a bit more to
give a safety margin including some slop for optimizations. Now that
we use double precision for the calculations, the accumulated roundoff
error is in double-precision ulps so it can easily be made almost 2**29
times smaller than a single-precision ulp. Near x = pi/4 its maximum
is about 0.5+(1.5+1.5)*x**2/3 ~= 1.117 double-precision ulps.
The minimax polynomial needs to be different to work for the larger
interval. I didn't increase its degree the old degree is just large
enough to keep the final error less than 1 ulp and increasing the
degree would be a pessimization. The maximum error is now ~0.80
ulps instead of ~0.53 ulps.
The speedup from this optimization for uniformly distributed args in
[-2pi, 2pi] is 28-43% on athlons, depending on how badly gcc selected
and scheduled the instructions in the old version. The old version
has some int-to-float conversions that are apparently difficult to schedule
well, but gcc-3.3 somehow did everything ~10 cycles or ~10% faster than
gcc-3.4, with the difference especially large on AXPs. On A64s, the
problem seems to be related to documented penalties for moving single
precision data to undead xmm registers. With this version, the speed
is cycles is almost independent of the athlon and gcc version despite
the large differences in instruction selection to use the FPU on AXPs
and SSE on A64s.
This is a minor interface change. The function is renamed from
__kernel_tanf() to __kernel_tandf() so that misues of it will cause
link errors and not crashes.
This version is a routine translation with no special optimizations
for accuracy or efficiency. It gives an unimportant increase in
accuracy, from ~0.9 ulps to 0.5285 ulps. Almost all of the error is
from the minimax polynomial (~0.03 ulps and the final rounding step
(< 0.5 ulps). It gives strange differences in efficiency in the -5
to +10% range, with -O1 fairly consistently becoming faster and -O2
slower on AXP and A64 with gcc-3.3 and gcc-3.4.
arg to __kernel_rem_pio2() gives 53-bit (double) precision, not single
precision and/or the array dimension like I thought. prec == 2 is
used in e_rem_pio2.c for double precision although it is documented
to be for 64-bit (extended) precision, and I just reduced it by 1
thinking that this would give the value suitable for 24-bit (float)
precision. Reducing it 1 more to the documented value for float
precision doesn't actually work (it gives errors of ~0.75 ulps in the
reduced arg, but errors of much less than 0.5 ulps are needed; the bug
seems to be in kernel_rem_pio2.c). Keep using a value 1 larger than
the documented value but supply an array large enough hold the extra
unused result from this.
The bug can also be fixed quickly by increasing init_jk[0] in
k_rem_pio2.c from 2 to 3. This gives behaviour identical to using
prec == 1 except it doesn't create the extra result. It isn't clear
how the precision bug affects higher precisions. 113-bit (quad) is
the largest precision, so there is no way to use a large precision
to fix it.
they can be #included in other .c files to give inline functions, and
use them to inline the functions in most callers (not in e_lgammaf_r.c).
__kernel_tanf() is too large and complicated for gcc to inline very well.
An athlons, this gives a speed increase under favourable pipeline
conditions of about 10% overall (larger for AXP, smaller for A64).
E.g., on AXP, sinf() on uniformly distributed args in [-2Pi, 2Pi]
now takes 30-56 cycles; it used to take 45-61 cycles; hardware fsin
takes 65-129.
On athlons, this gives a speedup of 10-20% for tanf() on uniformly
distributed args in [-2Pi, 2Pi]. (It only directly applies for 43%
of the args and gives a 16-20% speedup for these (more for AXP than
A64) and this gives an overall speedup of 10-12% which is all that it
should; however, it gives an overall speedup of 17-20% with gcc-3.3
on AXP-A64 by mysteriously effected cases where it isn't executed.)
I originally intended to use double precision for all internals of
float trig functions and will probably still do this, but benchmarking
showed that converting to double precision and back is a pessimization
in cases where a simple float precision calculation works, so it may
be optimal to switch precisions only when using extra precision is
much simpler.
__ieee754_rem_pio2f() to its 3 callers and manually inline them.
On Athlons, with favourable compiler flags and optimizations and
favourable pipeline conditions, this gives a speedup of 30-40 cycles
for cosf(), sinf() and tanf() on the range pi/4 < |x| <= 9pi/4, so
thes functions are now signifcantly faster than the hardware trig
functions in many cases. E.g., in a benchmark with uniformly distributed
x in [-2pi, 2pi], A64 hardware fcos took 72-129 cycles and cosf() took
37-55 cycles. Out-of-order execution is needed to get both of these
times. The optimizations in this commit apparently work more by
removing 1 serialization point than by reducing latency.
s_cosf.c and s_sinf.c:
Use a non-bogus magic constant for the threshold of pi/4. It was 2 ulps
smaller than pi/4 rounded down, but its value is not critical so it should
be the result of natural rounding.
s_cosf.c and s_tanf.c:
Use a literal 0.0 instead of an unnecessary variable initialized to
[(float)]0.0. Let the function prototype convert to 0.0F.
Improved wording in some comments.
Attempted to improve indentation of comments.
number of branches.
Use a non-bogus magic constant for the threshold of pi/4. It was 2 ulps
smaller than pi/4 rounded down, but its value is not critical so it should
be the result of natural rounding. Use "<=" comparisons with rounded-
down thresholds for all small multiples of pi/4.
Cleaned up previous commit:
- use static const variables instead of expressions for multiples of pi/2
to ensure that they are evaluated at compile time. gcc currently
evaluates them at compile time but C99 compilers are not required
to do so. We want compile time evaluation for optimization and don't
care about side effects.
- use M_PI_2 instead of a magic constant for pi/2. We need magic constants
related to pi/2 elsewhere but not here since we just want pi/2 rounded
to double and even prefer it to be rounded in the default rounding mode.
We can depend on the cmpiler being C99ish enough to round M_PI_2 correctly
just as much as we depended on it handling hex constants correctly. This
also fixes a harmless rounding error in the hex constant.
- keep using expressions n*<value for pi/2> in the initializers for the
static const variables. 2*M_PI_2 and 4*M_PI_2 are obviously rounded in
the same way as the corresponding infinite precision expressions for
multiples of pi/2, and 3*M_PI_2 happens to be rounded like this, so we
don't need magic constants for the multiples.
- fixed and/or updated some comments.
The threshold for not being tiny was too small. Use the usual 2**-12
threshold. This change is not just an optimization, since the general
code that we fell into has accuracy problems even for tiny x. Avoiding
it fixes 2*1366 args with errors of more than 1 ulp, with a maximum
error of 1.167 ulps.
The magic number 22 is log(DBL_EPSILON)/2 plus slop. This is bogus
for float precision. Use 9 (~log(FLT_EPSILON)/2 plus less slop than
for double precision). The code for handling the interval
[2**-28, 9_was_22] has accuracy problems even for [9, 22], so this
change happens to fix errors of more than 1 ulp in about 2*17000
cases. It leaves such errors in about 2*1074000 cases, with a max
error of 1.242 ulps.
The threshold for switching from returning exp(x)/2 to returning
exp(x/2)^2/2 was a little smaller than necessary. As for coshf(),
This was not quite harmless since the exp(x/2)^2/2 case is inaccurate,
and fixing it avoids accuracy problems in 2*6 cases, leaving problems
in 2*19997 cases.
Fixed naming errors in pseudo-code in comments.
The threshold for not being tiny was confusing and too small. Use the
usual 2**-12 threshold and simplify the algorithm slightly so that
this threshold works (now use the threshold for sinhf() instead of one
for 1+expm1()). This is just a small optimization.
The magic number 22 is log(DBL_EPSILON)/2 plus slop. This is bogus
for float precision. Use 9 (~log(FLT_EPSILON)/2 plus less slop than
for double precision).
The threshold for switching from returning exp(x)/2 to returning
exp(x/2)^2/2 was a little smaller than necessary. This was not quite
harmless since the exp(x/2)^2/2 case is inaccurate. Fixing it happens
to avoid accuracy problems for 2*6 of the 2*151 args that were handled
by the exp(x)/2 case. This leaves accuracy problems for about 2*19997
args near the overflow threshold (~89); the maximum error there is
2.5029 ulps.
There are also accuracy probles for args in +-[0.5*ln2, 9] -- 2*188885
args with errors of more than 1 ulp, with a maximum error of 1.384 ulps.
Fixed a syntax error and naming errors in pseudo-code in comments.
