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JobHandle and dependencies
ParallelForTransform jobs
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ParallelFor jobs

When scheduling jobs, there can only be one job doing one task. In a game, it is common to want to perform the same operation on a large number of objects. There is a separate job type called IJobParallelFor to handle this.

Note: A “ParallelFor” job is a collective term in Unity for any struct that implements the IJobParallelFor interface.

A ParallelFor job uses a NativeArray of data to act on as its data source. ParallelFor jobs run across multiple cores. There is one job per core, each handling a subset of the workload. IJobParallelFor behaves like IJob, but instead of a single Execute method, it invokes the Execute method once per item in the data source. There is an integer parameter in the Execute method. This index is to access and operate on a single element of the data source within the job implementation.

An example of a ParallelFor job definition:

struct IncrementByDeltaTimeJob: IJobParallelFor
    public NativeArray<float> values;
    public float deltaTime;

    public void Execute (int index)
        float temp = values[index];
        temp += deltaTime;
        values[index] = temp;

Scheduling ParallelFor jobs

When scheduling ParallelFor jobs, you must specify the length of the NativeArray data source that you are splitting. The Unity C# Job System cannot know which NativeArray you want to use as the data source if there are several in the struct. The length also tells the C# Job System how many Execute methods to expect.

Behind the scenes, the scheduling of ParallelFor jobs is more complicated. When scheduling ParallelFor jobs, the C# Job System divides the work into batches to distribute between cores. Each batch contains a subset of Execute methods. The C# Job System then schedules up to one job in Unity’s native job system per CPU core and passes that native job some batches to complete.

A ParallelFor job dividing batches across cores
A ParallelFor job dividing batches across cores

When a native job completes its batches before others, it steals remaining batches from the other native jobs. It only steals half of a native job’s remaining batches at a time, to ensure cache locality.

To optimize the process, you need to specify a batch count. The batch count controls how many jobs you get, and how fine-grained the redistribution of work between threads is. Having a low batch count, such as 1, gives you a more even distribution of work between threads. It does come with some overhead, so sometimes it is better to increase the batch count. Starting at 1 and increasing the batch count until there are negligible performance gains is a valid strategy.

An example of scheduling a ParallelFor job

Job code:

// Job adding two floating point values together
public struct MyParallelJob : IJobParallelFor
    public NativeArray<float> a;
    public NativeArray<float> b;
    public NativeArray<float> result;

    public void Execute(int i)
        result[i] = a[i] + b[i];

Main thread code:

NativeArray<float> a = new NativeArray<float>(2, Allocator.TempJob);

NativeArray<float> b = new NativeArray<float>(2, Allocator.TempJob);

NativeArray<float> result = new NativeArray<float>(2, Allocator.TempJob);

a[0] = 1.1;
b[0] = 2.2;
a[1] = 3.3;
b[1] = 4.4;

MyParallelJob jobData = new MyParallelJob();
jobData.a = a;  
jobData.b = b;
jobData.result = result;

// Schedule the job with one Execute per index in the results array and only 1 item per processing batch
JobHandle handle = jobData.Schedule(result.Length, 1);

// Wait for the job to complete

// Free the memory allocated by the arrays

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JobHandle and dependencies
ParallelForTransform jobs