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8338694: x86_64 intrinsic for tanh using libm #20657

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8338694: x86_64 intrinsic for tanh using libm #20657

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0328101
8338694: x86_64 intrinsic for tanh using libm
vamsi-parasa Aug 21, 2024
79766f1
Fix bug in NaN path
vamsi-parasa Aug 22, 2024
4739ad4
Add stub initialization and extra tanh tests
vamsi-parasa Aug 30, 2024
39350a3
update libm tanh reference test with code review suggestions
vamsi-parasa Sep 5, 2024
4aa52bf
c1 and template generator fixes
vamsi-parasa Sep 11, 2024
d4ddc31
quad precision tanh tests
vamsi-parasa Sep 13, 2024
ca3314c
Update test/jdk/java/lang/Math/HyperbolicTests.java
vamsi-parasa Sep 13, 2024
c151123
remove tanh tests in seprate file
vamsi-parasa Sep 16, 2024
d249508
update copyright year and remove unused random from HyperbolicTests
vamsi-parasa Sep 16, 2024
e908eb4
Update HyperbolicTests.java
vamsi-parasa Sep 16, 2024
7416537
Merge branch 'master' of https://git.openjdk.java.net/jdk into onetanh
vamsi-parasa Sep 16, 2024
3664be1
update tanh additional tests
vamsi-parasa Sep 16, 2024
b438555
Merge branch 'master' of https://git.openjdk.java.net/jdk into onetanh
vamsi-parasa Sep 16, 2024
1ee4c1f
add call to the additional tests
vamsi-parasa Sep 17, 2024
aa16389
remove -ve tests
vamsi-parasa Sep 17, 2024
5da2754
fix is_intrinsic_supported to work properly
vamsi-parasa Sep 19, 2024
4dc2e36
change ifdef from x86 to AMD64
vamsi-parasa Sep 23, 2024
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2 changes: 1 addition & 1 deletion test/jdk/java/lang/Math/HyperbolicTests.java
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Original file line number Diff line number Diff line change
Expand Up @@ -1008,7 +1008,7 @@ static int testTanhWithReferenceInRange(double min, double range) {
testCases[i] = min + range * rand.nextDouble();
}

for(int i = 0; i < testCases.length; i++) {
for (int i = 0; i < testCases.length; i++) {
double testCase = testCases[i];
failures += testTanhWithReferenceUlpDiff(testCase, StrictMath.tanh(testCase), 2.5);
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The allowable worst-case error is 2.5 ulp, although at many arguments FDLIBM has a smaller error.

For a general Math vs StrictMath test with an allowable 2.5 ulp error, without knowing how accurate FDLIBM is for that function and argument, a large error of approx. 2X the nominal error should be allowed (in case FDLIBM errors in one direction and the Math method errors in the other direction).

If the test is going to use randomness, then its jtreg tags should include

@key randomness

and it is preferable to use jdk.test.lib.RandomFactory to get and Random object since that handles printing out a key so the random sequence can be replicated if the test fails.

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If the test is going to use randomness, then its jtreg tags should include

@key randomness

and it is preferable to use jdk.test.lib.RandomFactory to get and Random object since that handles printing out a key so the random sequence can be replicated if the test fails.

Please see the test updated to use @key randomness and jdk.test.lib.RandomFactory to get and Random object.

The allowable worst-case error is 2.5 ulp, although at many arguments FDLIBM has a smaller error.
For a general Math vs StrictMath test with an allowable 2.5 ulp error, without knowing how accurate FDLIBM is for that function and argument, a large error of approx. 2X the nominal error should be allowed (in case FDLIBM errors in one direction and the Math method errors in the other direction).

So far the tests haven't failed with error of 2.5ulp. Would it be better to make it 5ulp? Please let me know.

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So far, this will be the only intrinsic implementation of tanh. Therefore, at the moment it is just checking the consistency of the intrinsic implementation with StrictMath/FDLIBM tanh. If the intrinsic has a ~1 ulp accuracy, it would be expected to often be within 2.5 ulps of FDLIBM tanh. However, as written the regression test would not necessarily pass against any allowable Math.tanh implementation, which is the usual criteria for java.lang.Math tests that aren't otherwise constrained (such as by being limited to a given subset of platforms).

If there was a correctly rounded tanh to compare against, then this style of testing would be valid.

Are there any plan to intrinsify sinh or cosh?

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I think instead of random we should generate offline additional correctly rounded fixed test points to cater to new algorithm using high precision arithmetic library and then simply extend the HyperbolicTests.java with these new fixed test points using existing ulp testing mechanism in the test.

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Thank you Sandhya(@sviswa7) for the suggestion! Will update the existing HyperbolicTests.java with new fixed point tests with quad precision reference values.

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Hi Joe (@jddarcy),

As suggested by Sandhya (@sviswa7), I added ~750 fixed point tests for tanh in TanhTests.java using the quad precision tanh implementation in libquadmath library from gcc.

Please let me know if this looks good.

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@vamsi-parasa In my thoughts the best way to do this is add the additional tests points to HyperbolicTests.java itself in the testcases array of testTanh() method. We should remove all the other changes from HyperbolicTests.java. Also no need for separate TanhTests.java file.

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Hi Sandhya(@sviswa7), please see the updated code in HyperbolicTests.java which removes the previous random based tests with the new fixed point tests. Also removed the TanhTests.java.

}
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