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/**
* @test
* @bug 8160425
* @summary Test vectorization with a signalling NaN.
* @run main/othervm -XX:+IgnoreUnrecognizedVMOptions -XX:-OptimizeFill
* compiler.vectorization.TestNaNVector
* @run main/othervm -XX:+IgnoreUnrecognizedVMOptions -XX:-OptimizeFill
* -XX:MaxVectorSize=4 compiler.vectorization.TestNaNVector
*/
package compiler.vectorization;
public class TestNaNVector {
private char[] array;
private static final int LEN = 1024;
public static void main(String args[]) {
TestNaNVector test = new TestNaNVector();
// Check double precision NaN
for (int i = 0; i < 10_000; ++i) {
test.vectorizeNaNDP();
}
System.out.println("Checking double precision Nan");
test.checkResult(0xfff7);
// Check single precision NaN
for (int i = 0; i < 10_000; ++i) {
test.vectorizeNaNSP();
}
System.out.println("Checking single precision Nan");
test.checkResult(0xff80);
}
public TestNaNVector() {
array = new char[LEN];
}
public void vectorizeNaNDP() {
// This loop will be vectorized and the array store will be replaced by
// a 64-bit vector store to four subsequent array elements. The vector
// should look like this '0xfff7fff7fff7fff7' and is read from the constant
// table. However, in floating point arithmetic this is a signalling NaN
// which may be converted to a quiet NaN when processed by the x87 FPU.
// If the signalling bit is set, the vector ends up in the constant table
// as '0xfffffff7fff7fff7' which leads to an incorrect result.
for (int i = 0; i < LEN; ++i) {
array[i] = 0xfff7;
}
}
public void vectorizeNaNSP() {
// Same as above but with single precision
for (int i = 0; i < LEN; ++i) {
array[i] = 0xff80;
}
}
public void checkResult(int expected) {
for (int i = 0; i < LEN; ++i) {
if (array[i] != expected) {
throw new RuntimeException("Invalid result: array[" + i + "] = " + (int)array[i] + " != " + expected);
}
}
}
}