8373722: [TESTBUG] compiler/vectorapi/TestVectorOperationsWithPartialSize.java fails intermittently

[XiaohongGong]

The test fails intermittently with the following error:

Caused by: java.lang.RuntimeException: assertEqualsWithTolerance: expected 0.0 but was 1.1754945E-38 (tolerance: 1.4E-44, diff: 1.1754945E-38)
at compiler.vectorapi.TestVectorOperationsWithPartialSize.verifyAddReductionFloat(TestVectorOperationsWithPartialSize.java:231)
at compiler.vectorapi.TestVectorOperationsWithPartialSize.testAddReductionFloat(TestVectorOperationsWithPartialSize.java:260)

The root cause is that the Vector API reduceLanes() does not guarantee a specific calculation order for floating-point reduction operations [1]. When the array contains extreme values, this can produce results outside the tolerance range compared to sequential scalar addition.

For example, given array elements:

[0.0f, Float.MIN_NORMAL, Float.MAX_VALUE, -Float.MAX_VALUE]

Sequential scalar addition produces:

0.0f + Float.MIN_NORMAL + Float.MAX_VALUE - Float.MAX_VALUE = 0.0f

However, reduceLanes() might compute:

(0.0f + Float.MIN_NORMAL) + (Float.MAX_VALUE - Float.MAX_VALUE) = Float.MIN_NORMAL

The difference of the two times of calculation is Float.MIN_NORMAL (1.1754945E-38), which exceeds the tolerance of Math.ulp(0.0f) * 10.0f = 1.4E-44. Even with a 10x rounding error factor, the tolerance is insufficient for such edge cases.

Since reduceLanes() does not require a specific calculation order, differences from scalar results can be significantly larger when special or extreme maximum/minimum values are present. Using a fixed tolerance is inappropriate for such corner cases.

This patch fixes the issue by initializing the float array in test with random normal values within a specified range, ensuring the result gap stays within the defined tolerance.

Tested locally on my AArch64 and X86_64 machines 500 times, and I didn't observe the failure again.

[1] https://docs.oracle.com/en/java/javase/25/docs/api/jdk.incubator.vector/jdk/incubator/vector/FloatVector.html#reduceLanes(jdk.incubator.vector.VectorOperators.Associative)

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• JDK-8373722: [TESTBUG] compiler/vectorapi/TestVectorOperationsWithPartialSize.java fails intermittently (Bug - P4)

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[DamonFool]

Shall we change the calculation of tolerance?

e.g.

max_abs = max(abs(arr[0]), abs(arr[1]), ...)
tolerance = Math.ulp(max_abs) * vlen

[XiaohongGong]

Shall we change the calculation of tolerance?

e.g.

max_abs = max(abs(arr[0]), abs(arr[1]), ...)
tolerance = Math.ulp(max_abs) * vlen

Thanks for looking at this issue. It's really a good question about the definition of a more reasonable tolerance, which is fundamentally a numerical analysis problem per my understanding.

For a floating‑point reduction test, the goal is to check that the API is implemented correctly. And what we really care about is how close the reduction result is to the mathematically expected value. In other words, the tolerance should be derived from the expected sum itself. For example, if the float array is [1.0f, -1.0f, 2.0f, -2.0f], we genuinely expect a result very close to 0.0f, not something near 2.0f.

Using max_abs to set the tolerance risks inflating the admissible error range (since max_abs here would be 2.0f), which I'm afraid might make the test much less effective. WDYT?

[DamonFool]

And what we really care about is how close the reduction result is to the mathematically expected value.

Understood.

However, (1.0, 5.0) the test range is really too small.
If we expand the range, the current tolerance may still become not big enough.

[DamonFool]

For example, if the float array is [1.0f, -1.0f, 2.0f, -2.0f], we genuinely expect a result very close to 0.0f, not something near 2.0f.

Using max_abs to set the tolerance risks inflating the admissible error range (since max_abs here would be 2.0f), which I'm afraid might make the test much less effective.

FYI: the current sum based tolerance may be also bigger than max_abs based.
For example, if the float array is [1.0f, 1.0f, 2.0f, 2.0f]. The sum would be 6.0f, the max_abs would be 2.0.
What do you think?

[XiaohongGong]

For example, if the float array is [1.0f, -1.0f, 2.0f, -2.0f], we genuinely expect a result very close to 0.0f, not something near 2.0f.
Using max_abs to set the tolerance risks inflating the admissible error range (since max_abs here would be 2.0f), which I'm afraid might make the test much less effective.

FYI: the current sum based tolerance may be also bigger than max_abs based. For example, if the float array is [1.0f, 1.0f, 2.0f, 2.0f]. The sum would be 6.0f, the max_abs would be 2.0. What do you think?

We used the Math.ulp(sum) * 10 to calculate the tolerance, which means the difference of expected and actual value is inside of 10 ULP around the value of sum. Note that Math.ulp(f) is the positive distance between f and the next representable float value larger in magnitude (1 ulp around value f). Hence, it is more reasonable based on the expected value?

[XiaohongGong]

And what we really care about is how close the reduction result is to the mathematically expected value.

Understood.

However, (1.0, 5.0) the test range is really too small. If we expand the range, the current tolerance may still become not big enough.

What I really want to avoid in this case is generating values that are near the extreme maximum or minimum of float. Expanding the value range would probably still be acceptable with the current tolerance, which is derived not only from the cross‑lane sum but also includes a rounding‑error factor of 10.

BTW, consider that there are already enough API tests under test/jdk/jdk/incubator/vector, this test deliberately uses a narrower range, because its primary goal is to verify the expected IR generated on SVE, rather than to stress all numerical edge cases.

[DamonFool]

We used the Math.ulp(sum) * 10 to calculate the tolerance, which means the difference of expected and actual value is inside of 10 ULP around the value of sum.

The question here is what do you mean by expected value?
Do you mean the sequential scalar floating point add with rounding errors will produce the expected value?
Or do you mean the golden value in math?

Just consider the following case

1000.0f + Float.MAX_VALUE - Float.MAX_VALUE

The sequential scalar floating add will result 0.0f.
However, the golden value in math should be 1000.0f.

[XiaohongGong]

We used the Math.ulp(sum) * 10 to calculate the tolerance, which means the difference of expected and actual value is inside of 10 ULP around the value of sum.

The question here is what do you mean by expected value? Do you mean the sequential scalar floating point add with rounding errors will produce the expected value? Or do you mean the golden value in math?

Just consider the following case

1000.0f + Float.MAX_VALUE - Float.MAX_VALUE

The sequential scalar floating add will result 0.0f. However, the golden value in math should be 1000.0f.

I think we should refer to the java ref spec, that calculates the values in sequential order, right?

[DamonFool]

I think we should refer to the java ref spec, that calculates the values in sequential order, right?

I'm not sure. Did you find related description in the spec?

If that is true, the current sum-based Math.ulp(0.0f) * 10 is far from 1000.0f, which seems unreasonable.

[jatin-bhateja]

Ideally our tolerance window should be narrow, and increasing the tolerance range to accomodate outliers as you mentioned in your issue description may defeat the purpose.

Unlike auto-vectorization which adhears strict ordering JLS semantics, vectorAPI relaxes the reduction order to give backends leeway to use parallel reduction not strictly following the sequential order.

There are multiple considerations involed, fallback implimentation performs reduction sequentially, inline expander always relaxes the strict ordering, intrinsification of Add/Mul reductions are only supported by Aarch64, X86 and riscv.

Computing expected value using parallel reduction can be other alternative but then we may face similar problems on targets which does not intrinsify unordered reductions.

Tolerance modeling is a complex topic and involves relative and absolute error, current 10ULP absolute limit is not generic enough to handle entier spectrum of values, what you have enforced now is a range based tolerance did you try widening the input value range and confirm if 10ULP tolerance limit is sufficient ?