Core Java

JDK 21: Image Performance Improvements

Introduction

In a previous article, I talked about how serialization and file I/O performance was improved in JDK 21 thanks to the use of VarHandleconstructs. The method employed there has now also been applied to Java’s image-handling library making it faster. Here is what happened:

Background

When packing/unpacking primitive values (such as int and long primitives) into/from a byte array, conversion was previously made using explicit bit shifting as shown in the ImageInputStreamImpl::readInt method below:

public int readInt() throws IOException {
    if (read(byteBuf, 0, 4) !=  4) {
        throw new EOFException();
    }

    if (byteOrder == ByteOrder.BIG_ENDIAN) {
        return
            (((byteBuf[0] & 0xff) << 24) | ((byteBuf[1] & 0xff) << 16) |      // (1)
             ((byteBuf[2] & 0xff) <<  8) | ((byteBuf[3] & 0xff) <<  0));
    } else {
        return
            (((byteBuf[3] & 0xff) << 24) | ((byteBuf[2] & 0xff) << 16) |      // (2)
             ((byteBuf[1] & 0xff) <<  8) | ((byteBuf[0] & 0xff) <<  0));
    }
}
  1. Big-endian unpacking via bit shifting
  2. Little-endian unpacking via bit shifting

The scheme used here is similar to what is described in my previous article so, I will not dive into the details again. In short, this method is complex and challenging for Java to fully optimize. Also, it is hard to read for us humans.

Improvements in JDK 21

In Java 21, conversions are instead made with VarHandle constructs via the new jdk.internal.util.ByteArray class. Here is what parts of the internal ByteArray class look like:

private static final VarHandle INT =
        MethodHandles.byteArrayViewVarHandle(int[], ByteOrder.BIG_ENDIAN);


static int getInt(byte[] b, int off) {
    return (int) INT.get(b, off);
}

Using VarHandles means Java is able to optimize methods better compared to explicit bit shifting. Again, you can read more about how VarHandles work in my previous article.

The class above handles big-endian. As images need to be able to handle also little-endian a new class called ByteArrayLittleEndianwas added. This means the readInt() method can be simplified and improved like this:

public int readInt() throws IOException {
    if (read(byteBuf, 0, 4) !=  4) {
        throw new EOFException();
    }

    return (byteOrder == ByteOrder.BIG_ENDIAN)
            ? ByteArray.getInt(byteBuf, 0)
            : ByteArrayLittleEndian.getInt(byteBuf, 0);
}

Nice! It looks much cleaner now.

Affected Classes and Impact

The following classes were directly improved:

  • ImageInputStreamImpl
  • ImageOutputStreamImpl

The good news is that these classes provide the foundation for a large number of other image-handling classes in the javax.imageio.stream package and perhaps elsewhere (after all, the above classes are in the public API).

This means, in many cases, image handling becomes faster and all third-party libraries relying on any of the classes above (directly or indirectly) will also run faster with no change in your application code.

Benchmarks

In the benchmarks below, I have used Java 17 as a baseline meaning that other Java 21 performance improvements will also contribute to higher performance. I have run the benchmarks using my Mac M1 aarch64 but the benchmarks are available here for anyone to run.

Graph 1 shows the performance of the ImageInputStreamImpl::readInt method for Java 17 and Java 21.

So, the throughput of the benchmarked method has improved from about 579,800,000 bytes/s to around 639,000,000 bytes/s on my machine which is more than a 10% improvement! Not too bad!

Actual Application Performance Increase

How much faster will your image applications run under Java 21 in reality? There is only one way to find out: Run your own code on JDK 21 today by downloading a JDK 21 Early-Access Build.

Published on Java Code Geeks with permission by Per Minborg, partner at our JCG program. See the original article here: JDK 21: Image Performance Improvements

Opinions expressed by Java Code Geeks contributors are their own.

Per Minborg

I am a guy living in Palo Alto, California, but I am originally from Sweden. I am working as CTO on Speedment with Java and database application acceleration. Check it out on www.speedment.com
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