This is an automated email from the ASF dual-hosted git repository.
aherbert pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-numbers.git
The following commit(s) were added to refs/heads/master by this push:
new fdcef0ec NUMBERS-204: Update DoubleSplitPerformance with the
sub-normal upscaling
fdcef0ec is described below
commit fdcef0ece359c54f8eb600f6b3d9d50e600a7116
Author: aherbert <aherb...@apache.org>
AuthorDate: Thu Aug 31 10:22:39 2023 +0100
NUMBERS-204: Update DoubleSplitPerformance with the sub-normal upscaling
---
.../numbers/examples/jmh/core/DoublePrecision.java | 162 +++++++++++++++++++++
.../examples/jmh/core/DoubleSplitPerformance.java | 11 +-
2 files changed, 171 insertions(+), 2 deletions(-)
diff --git
a/commons-numbers-examples/examples-jmh/src/main/java/org/apache/commons/numbers/examples/jmh/core/DoublePrecision.java
b/commons-numbers-examples/examples-jmh/src/main/java/org/apache/commons/numbers/examples/jmh/core/DoublePrecision.java
index c364f654..9df6c387 100644
---
a/commons-numbers-examples/examples-jmh/src/main/java/org/apache/commons/numbers/examples/jmh/core/DoublePrecision.java
+++
b/commons-numbers-examples/examples-jmh/src/main/java/org/apache/commons/numbers/examples/jmh/core/DoublePrecision.java
@@ -60,6 +60,9 @@ final class DoublePrecision {
* Assuming the multiplier is above 2^27 and the maximum exponent is 1023
then a safe
* limit is a value with an exponent of (1023 - 27) = 2^996. */
private static final double SAFE_UPPER = 0x1.0p996;
+ /** The lower limit for a product {@code x * y} below which the round-off
component may be
+ * sub-normal. This is set as 2^-1022 * 2^54. */
+ private static final double SAFE_LOWER = 0x1.0p-968;
/** The scale to use when down-scaling during a split into a high part.
* This must be smaller than the inverse of the multiplier and a power of
2 for exact scaling. */
@@ -69,6 +72,13 @@ final class DoublePrecision {
* This is the inverse of {@link #DOWN_SCALE}. */
private static final double UP_SCALE = 0x1.0p30;
+ /** The upscale factor squared. */
+ private static final double UP_SCALE2 = 0x1.0p60;
+ /** The downscale factor squared. */
+ private static final double DOWN_SCALE2 = 0x1.0p-60;
+ /** The safe upper limit so the product {@code x * y} can be upscaled by
2^60. */
+ private static final double SAFE_UPPER_S = 0x1.0p963;
+
/** The mask to zero the lower 27-bits of a long . */
private static final long ZERO_LOWER_27_BITS = 0xffff_ffff_f800_0000L;
@@ -402,6 +412,158 @@ final class DoublePrecision {
// This could be done at a higher limit (e.g. Math.abs(xy) >
Double.MAX_VALUE / 4)
// but is included here to have a single low probability branch
condition.
+ // Add the absolute inputs for a single comparison. The sum will not
be more than
+ // 3-fold higher than any component.
+ final double a = Math.abs(x);
+ final double b = Math.abs(y);
+ final double ab = Math.abs(xy);
+ if (a + b + ab >= SAFE_UPPER) {
+ // Only required to scale the largest number as x*y does not
overflow.
+ if (a > b) {
+ return productLowUnscaled(x * DOWN_SCALE, y, xy * DOWN_SCALE)
* UP_SCALE;
+ }
+ return productLowUnscaled(x, y * DOWN_SCALE, xy * DOWN_SCALE) *
UP_SCALE;
+ }
+
+ // The result is computed using a product of the low parts.
+ // To avoid underflow in the low parts we note that these are
approximately a factor
+ // of 2^27 smaller than the original inputs so their product will be
~2^54 smaller
+ // than the product xy. Ensure the product is at least 2^54 above a
sub-normal.
+ if (ab <= SAFE_LOWER) {
+ // Scaling up here is safe: the largest magnitude cannot be above
SAFE_LOWER / MIN_VALUE.
+ return productLowUnscaled(x * UP_SCALE, y * UP_SCALE, xy *
UP_SCALE2) * DOWN_SCALE2;
+ }
+
+ // No scaling required
+ return productLowUnscaled(x, y, xy);
+ }
+
+
+ /**
+ * Compute the low part of the double length number {@code (z,zz)} for the
exact
+ * product of {@code x} and {@code y}. This is equivalent to computing a
{@code double}
+ * containing the magnitude of the rounding error when converting the
exact 106-bit
+ * significand of the multiplication result to a 53-bit significand.
+ *
+ * <p>The method is written to be functionally similar to using a fused
multiply add (FMA)
+ * operation to compute the low part, for example JDK 9's Math.fma
function (note the sign
+ * change in the input argument for the product):
+ * <pre>
+ * double x = ...;
+ * double y = ...;
+ * double xy = x * y;
+ * double low1 = Math.fma(x, y, -xy);
+ * double low2 = DoublePrecision.productLow(x, y, xy);
+ * </pre>
+ *
+ * <p>Special cases:
+ *
+ * <ul>
+ * <li>If {@code x * y} is sub-normal or zero then the result is 0.0.
+ * <li>If {@code x * y} is infinite or NaN then the result is NaN.
+ * </ul>
+ *
+ * @param x First factor.
+ * @param y Second factor.
+ * @param xy Product of the factors (x * y).
+ * @return the low part of the product double length number
+ * @see #highPart(double)
+ * @see #productLow(double, double, double, double, double)
+ */
+ static double productLowS(double x, double y, double xy) {
+ // Verify the input. This must be NaN safe.
+ //assert Double.compare(x * y, xy) == 0
+
+ // If the number is sub-normal, inf or nan there is no round-off.
+ if (isNotNormal(xy)) {
+ // Returns 0.0 for sub-normal xy, otherwise NaN for inf/nan:
+ return xy - xy;
+ }
+
+ // The result xy is finite and normal.
+ // Use Dekker's mul12 algorithm that splits the values into high and
low parts.
+ // Dekker's split using multiplication will overflow if the value is
within 2^27
+ // of double max value. It can also produce 26-bit approximations that
are larger
+ // than the input numbers for the high part causing overflow in hx *
hy when
+ // x * y does not overflow. So we must scale down big numbers.
+ // We only have to scale the largest number as we know the product
does not overflow
+ // (if one is too big then the other cannot be).
+ // We also scale if the product is close to overflow to avoid
intermediate overflow.
+ // This could be done at a higher limit (e.g. Math.abs(xy) >
Double.MAX_VALUE / 4)
+ // but is included here to have a single low probability branch
condition.
+
+ // Add the absolute inputs for a single comparison. The sum will not
be more than
+ // 3-fold higher than any component.
+
+ // Note: To drop a branch to check for upscaling, we use a lower
threshold than
+ // SAFE_UPPER in productLow
+ final double a = Math.abs(x);
+ final double b = Math.abs(y);
+ if (a + b + Math.abs(xy) >= SAFE_UPPER_S) {
+ // Only required to scale the largest number as x*y does not
overflow.
+ if (a > b) {
+ return productLowUnscaled(x * DOWN_SCALE, y, xy * DOWN_SCALE)
* UP_SCALE;
+ }
+ return productLowUnscaled(x, y * DOWN_SCALE, xy * DOWN_SCALE) *
UP_SCALE;
+ }
+
+ // Scaling up here is safe
+ return productLowUnscaled(x * UP_SCALE, y * UP_SCALE, xy * UP_SCALE2)
* DOWN_SCALE2;
+ }
+
+ /**
+ * Compute the low part of the double length number {@code (z,zz)} for the
exact
+ * product of {@code x} and {@code y}. This is equivalent to computing a
{@code double}
+ * containing the magnitude of the rounding error when converting the
exact 106-bit
+ * significand of the multiplication result to a 53-bit significand.
+ *
+ * <p>The method is written to be functionally similar to using a fused
multiply add (FMA)
+ * operation to compute the low part, for example JDK 9's Math.fma
function (note the sign
+ * change in the input argument for the product):
+ * <pre>
+ * double x = ...;
+ * double y = ...;
+ * double xy = x * y;
+ * double low1 = Math.fma(x, y, -xy);
+ * double low2 = DoublePrecision.productLow(x, y, xy);
+ * </pre>
+ *
+ * <p>Special cases:
+ *
+ * <ul>
+ * <li>If {@code x * y} is sub-normal or zero then the result is 0.0.
+ * <li>If {@code x * y} is infinite or NaN then the result is NaN.
+ * </ul>
+ *
+ * @param x First factor.
+ * @param y Second factor.
+ * @param xy Product of the factors (x * y).
+ * @return the low part of the product double length number
+ * @see #highPart(double)
+ * @see #productLow(double, double, double, double, double)
+ */
+ static double productLow0(double x, double y, double xy) {
+ // Verify the input. This must be NaN safe.
+ //assert Double.compare(x * y, xy) == 0
+
+ // If the number is sub-normal, inf or nan there is no round-off.
+ if (isNotNormal(xy)) {
+ // Returns 0.0 for sub-normal xy, otherwise NaN for inf/nan:
+ return xy - xy;
+ }
+
+ // The result xy is finite and normal.
+ // Use Dekker's mul12 algorithm that splits the values into high and
low parts.
+ // Dekker's split using multiplication will overflow if the value is
within 2^27
+ // of double max value. It can also produce 26-bit approximations that
are larger
+ // than the input numbers for the high part causing overflow in hx *
hy when
+ // x * y does not overflow. So we must scale down big numbers.
+ // We only have to scale the largest number as we know the product
does not overflow
+ // (if one is too big then the other cannot be).
+ // We also scale if the product is close to overflow to avoid
intermediate overflow.
+ // This could be done at a higher limit (e.g. Math.abs(xy) >
Double.MAX_VALUE / 4)
+ // but is included here to have a single low probability branch
condition.
+
// Add the absolute inputs for a single comparison. The sum will not
be more than
// 3-fold higher than any component.
final double a = Math.abs(x);
diff --git
a/commons-numbers-examples/examples-jmh/src/main/java/org/apache/commons/numbers/examples/jmh/core/DoubleSplitPerformance.java
b/commons-numbers-examples/examples-jmh/src/main/java/org/apache/commons/numbers/examples/jmh/core/DoubleSplitPerformance.java
index 0cefa627..8fa46f29 100644
---
a/commons-numbers-examples/examples-jmh/src/main/java/org/apache/commons/numbers/examples/jmh/core/DoubleSplitPerformance.java
+++
b/commons-numbers-examples/examples-jmh/src/main/java/org/apache/commons/numbers/examples/jmh/core/DoubleSplitPerformance.java
@@ -355,8 +355,9 @@ public class DoubleSplitPerformance {
* The name of the method.
*/
@Param({NONE, "multiply", "multiplyUnscaled",
- "productLow", "productLow1", "productLow2", "productLow3",
"productLowSplit",
- "productLowUnscaled"})
+ "productLow", "productLowS",
+ "productLow0", "productLow1", "productLow2", "productLow3",
"productLowSplit",
+ "productLowUnscaled", "fma"})
private String name;
/** The function. */
@@ -388,6 +389,10 @@ public class DoubleSplitPerformance {
};
} else if ("productLow".equals(name)) {
fun = (x, y) -> DoublePrecision.productLow(x, y, x * y);
+ } else if ("productLowS".equals(name)) {
+ fun = (x, y) -> DoublePrecision.productLowS(x, y, x * y);
+ } else if ("productLow0".equals(name)) {
+ fun = (x, y) -> DoublePrecision.productLow0(x, y, x * y);
} else if ("productLow1".equals(name)) {
fun = (x, y) -> DoublePrecision.productLow1(x, y, x * y);
} else if ("productLow2".equals(name)) {
@@ -404,6 +409,8 @@ public class DoubleSplitPerformance {
};
} else if ("productLowUnscaled".equals(name)) {
fun = (x, y) -> DoublePrecision.productLowUnscaled(x, y, x *
y);
+ } else if ("fma".equals(name)) {
+ fun = (x, y) -> Math.fma(x, y, -x * y);
} else {
throw new IllegalStateException("Unknown round-off method: " +
name);
}
