Why Does Custom BigInteger Implementation Run Slower Than JDK's Version?

Why is my custom implementation of BigInteger slower than the JDK's version even when using the same algorithm?

//import java.math.BigInteger;

public class MultiplyTest {
    public static void main(String[] args) {
        Random r = new Random(1);
        long tm = 0, count = 0, result = 0;
        for (int i = 0; i < 400000; i++) {
            int s1 = 400, s2 = 400;
            BigInteger a = new BigInteger(s1 * 8, r), b = new BigInteger(s2 * 8, r);
            long tm1 = System.nanoTime();
            BigInteger c = a.multiply(b);
            if (i > 100000) {
                tm += System.nanoTime() - tm1;
                count++;
            }
            result += c.bitLength();
        }
        System.out.println((tm / count) + "nsec/mul");
        System.out.println(result);
    }
}

The performance discrepancy between your custom implementation of BigInteger and the JDK's version can be attributed to several key optimizations that the HotSpot JVM (Java Virtual Machine) employs during runtime. This includes Just-In-Time (JIT) compilation optimizations that your code may not be leveraging to the same extent as the optimized JDK version.

// Example to enable optimizations in JVM
java -Xmx1G -Xms1G -XX:+UseG1GC -XX:+UseStringDeduplication -XX:+PrintCompilation MultiplyTest

Causes

• JIT Compilation: The JDK's version of BigInteger likely benefits from extensive optimizations during JIT compilation, such as inlining critical methods, escape analysis, and dead code elimination, which your custom version might not utilize effectively.
• HotSpot Optimizations: The HotSpot JVM applies specific optimizations for standard library classes (like BigInteger) that your version lacks, such as adaptive tuning based on runtime performance, which can lead to significant differences in execution speed.
• Class Loader Behavior: The JDK classes may be pre-loaded and optimized by the JVM in ways that are not applicable to your version loaded as a custom class, reducing warm-up time and enhancing performance.

Solutions

• Profile Your Code: Use profiling tools like Java VisualVM or JProfiler to analyze where your custom implementation might be spending excessive time compared to the JDK's implementation.
• Revisit JIT Compiler Flags: Ensure your Java command includes optimizations such as -XX:+AggressiveOpts or -XX:+UseSuperWord, which could allow the JIT compiler to apply more aggressive optimizations to your own classes as well.
• Consider Using Standard Libraries: For critical performance contexts, it is generally advisable to use standard JDK classes directly to take advantage of the optimizations and maintenance they receive.

Mistake: Ignoring performance implications of JVM optimizations.

Solution: Optimize your code by using built-in libraries and analyze performance differences through profiling.

Mistake: Not allowing for warm-up time in timers for benchmarking.

Solution: Run benchmarks with sufficient iterations and consider discarding initial iterations to account for JIT warm-up.