Java 16 introduced a new feature called the Vector API, which provides a set of low-level vector operations for performing mathematical calculations on large sets of data. The Vector API is designed to take advantage of the hardware capabilities of modern CPUs, such as SIMD (Single Instruction Multiple Data) instructions, which can perform the same operation on multiple values in parallel.

In this blog article, we will explore what the Vector API is, how it works, and what its current status is.

What is the Vector API?

The Vector API is a set of low-level vector operations that provide a way to perform mathematical calculations on large sets of data. These operations are designed to take advantage of the hardware capabilities of modern CPUs, such as SIMD instructions, which can perform the same operation on multiple values in parallel.

The Vector API is implemented as a set of interfaces and classes in the `java.util.vector`

package. These interfaces and classes provide a way to create and manipulate vectors, which are arrays of primitive data types that can be processed in parallel using the vector operations.

How the Vector API Works

The Vector API provides a set of low-level vector operations that can be used to perform mathematical calculations on large sets of data. These operations are designed to take advantage of the hardware capabilities of modern CPUs, such as SIMD instructions, which can perform the same operation on multiple values in parallel.

To use the Vector API, you first need to create a vector. This is done using one of the `VectorSpecies`

classes, which represent different vector sizes and element types. For example, to create a vector of 256-bit floats, you would use the `FloatVector.SPECIES_256`

constant.

Once you have a vector, you can use the vector operations to perform mathematical calculations on the data. These operations include basic arithmetic operations such as addition, subtraction, multiplication, and division, as well as more complex operations such as vector dot products, vector cross products, and trigonometric functions.

Here’s an example of how the Vector API might be used to perform a vector dot product:

```
FloatVector a = FloatVector.SPECIES_256.create(...);
FloatVector b = FloatVector.SPECIES_256.create(...);
float dotProduct = a.dot(b);
```

In this example, two vectors `a`

and `b`

are created using the `FloatVector.SPECIES_256`

constant, which represents a vector of 256-bit floats. The `dot`

method is then used to perform a dot product on the two vectors, which returns a single float value representing the result of the dot product.

What are the VectorSpecies?

Here is a list of the VectorSpecies available in the Vector API along with their corresponding data type and vector size:

VectorSpecies<Byte> BYTE_128

- Data Type: byte
- Vector Size: 16

VectorSpecies<Short> SHORT_128

- Data Type: short
- Vector Size: 8

VectorSpecies<Integer> INT_128

- Data Type: int
- Vector Size: 4

VectorSpecies<Long> LONG_128

- Data Type: long
- Vector Size: 2

VectorSpecies<Float> FLOAT_128

- Data Type: float
- Vector Size: 4

VectorSpecies<Double> DOUBLE_128

- Data Type: double
- Vector Size: 2

Each VectorSpecies corresponds to a specific data type and vector size, and provides a set of low-level vector operations for performing mathematical calculations on large sets of data. These operations include basic arithmetic operations such as addition, subtraction, multiplication, and division, as well as more advanced operations such as sine, cosine, and tangent functions.

Using the Vector API and the VectorSpecies available, developers can perform mathematical calculations on large sets of data in a highly parallelized manner, leading to significant performance improvements over traditional scalar calculations.

In addition to the pre-defined VectorSpecies, the Vector API also provides a number of factory methods for creating custom VectorSpecies with arbitrary data types and vector sizes. This allows developers to create custom VectorSpecies to suit their specific needs and optimize their code for specific data types and vector sizes.

What are the available operations?

Here are some of the types of operations that VectorSpecies are able to do:

- Basic Arithmetic Operations: The VectorSpecies provide basic arithmetic operations such as addition, subtraction, multiplication, and division, allowing you to perform calculations on large sets of data with a single instruction.

For example, you could use the VectorSpecies<Float> to perform an element-wise addition of two arrays of floating-point numbers with the following code:

```
float[] a = {1.0f, 2.0f, 3.0f, 4.0f};
float[] b = {5.0f, 6.0f, 7.0f, 8.0f};
VectorSpecies<Float> species = FloatVector.SPECIES_128;
FloatVector aVec = species.create(a, 0);
FloatVector bVec = species.create(b, 0);
FloatVector result = aVec.add(bVec);
```

- Advanced Mathematical Functions: The VectorSpecies also provide advanced mathematical functions such as sine, cosine, tangent, logarithm, and exponentiation. These operations can be used to perform complex calculations on large sets of data in a highly parallelized manner.

For example, you could use the VectorSpecies<Double> to calculate the exponential function for each element of an array of doubles with the following code:

```
double[] a = {1.0, 2.0, 3.0, 4.0};
VectorSpecies<Double> species = DoubleVector.SPECIES_128;
DoubleVector aVec = species.create(a, 0);
DoubleVector result = aVec.exp();
```

- Comparison and Selection Operations: The VectorSpecies also provide operations for comparing vectors and selecting elements based on a certain condition. These operations can be used to filter or transform large sets of data based on specific criteria.

For example, you could use the VectorSpecies<Integer> to select only the positive elements of an array of integers with the following code:

```
int[] a = {1, -2, 3, -4};
VectorSpecies<Integer> species = IntVector.SPECIES_128;
IntVector aVec = species.create(a, 0);
IntVector mask = aVec.compare(VectorOperators.greaterThan(0));
IntVector result = aVec.blend(mask, species.zero());
```

Current Status of the Vector API

The Vector API was introduced in Java 16 as an incubating feature, which means that it was not considered to be a stable feature at the time of its introduction. However, the API has been further developed and improved in subsequent releases of Java.

As of Java 17, the Vector API is still considered to be an incubating feature, which means that it is subject to change in future releases of Java. However, the API has been included in the standard Java runtime since Java 16, which means that it is available for use in production applications.

In addition to being included in the standard Java runtime, the Vector API is also supported by third-party libraries and frameworks, such as the Apache Arrow project, which provides a vectorized data processing framework for big data applications.

Conclusion

The Vector API is a new feature introduced in Java 16 that provides a set of low-level vector operations for performing mathematical calculations on large sets of data. The Vector API is designed to take advantage of the hardware capabilities of modern CPUs, such as SIMD instructions, which can perform the same operation on multiple values in parallel.

While the Vector API is still considered to be an incubating feature in Java 17, it has already shown promise in improving the performance of certain types of applications, such as data processing and scientific computing.

One of the main benefits of using the Vector API is that it can help to improve the efficiency of your code by allowing you to perform calculations on large sets of data in parallel. This can lead to significant performance improvements, particularly when dealing with large datasets.

However, it’s worth noting that the Vector API is not suitable for all types of applications. In some cases, the overhead of using the Vector API may outweigh the benefits, particularly for small datasets or for operations that cannot be easily parallelized.

In addition to the Vector API itself, there are also a number of tools and libraries available for working with vectorized data in Java. These include libraries such as Apache Arrow and Apache Arrow Flight, which provide a framework for working with vectorized data, as well as tools such as the JMH (Java Microbenchmark Harness), which can be used to benchmark and compare the performance of different implementations of vectorized code.

In conclusion, the Vector API is a powerful new feature introduced in Java 16 that provides a set of low-level vector operations for performing mathematical calculations on large sets of data. While still considered to be an incubating feature in Java 17, the Vector API has already shown promise in improving the performance of certain types of applications, particularly those dealing with large datasets. If you’re working with vectorized data in Java, the Vector API is definitely worth exploring further.