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Operating duties in parallel – The.Swift.Dev.


With the ability to run duties in parallel is good, it could possibly pace up issues for positive when you possibly can make the most of a number of CPU cores, however how can we really implement these sort of operations in Swift? πŸ€”

There are a number of methods of operating parallel operations, I had an extended article concerning the Grand Central Dispatch (GCD) framework, there I defined the variations between parallelism and concurrency. I additionally demonstrated easy methods to arrange serial and concurrent dispatch queues, however this time I might wish to focus a bit extra on duties, staff and jobs.

Think about that you’ve got an image which is 50000 pixel broad and 20000 pixel lengthy, that is precisely one billion pixels. How would you alter the colour of every pixel? Properly, we may do that by iterating by way of every pixel and let one core do the job, or we may run duties in parallel.

The Dispatch framework provides a number of methods to resolve this difficulty. The primary resolution is to make use of the concurrentPerform perform and specify some variety of staff. For the sake of simplicity, I will add up the numbers from zero to 1 billion utilizing 8 staff. πŸ’ͺ

import Dispatch

let staff: Int = 8
let numbers: [Int] = Array(repeating: 1, rely: 1_000_000_000)

var sum = 0
DispatchQueue.concurrentPerform(iterations: staff) { index in
    let begin = index * numbers.rely / staff
    let finish = (index + 1) * numbers.rely / staff
    print("Employee #(index), gadgets: (numbers[start..<end].rely)")

    sum += numbers[start..<end].scale back(0, +)
}

print("Sum: (sum)")

Cool, however nonetheless every employee has to work on numerous numbers, perhaps we should not begin all the employees directly, however use a pool and run solely a subset of them at a time. That is fairly a simple activity with operation queues, let me present you a primary instance. 😎

import Basis

let staff: Int = 8
let numbers: [Int] = Array(repeating: 1, rely: 1_000_000_000)

let operationQueue = OperationQueue()
operationQueue.maxConcurrentOperationCount = 4

var sum = 0
for index in 0..<staff {
    let operation = BlockOperation {
        let begin = index * numbers.rely / staff
        let finish = (index + 1) * numbers.rely / staff
        print("Employee #(index), gadgets: (numbers[start..<end].rely)")
        
        sum += numbers[start..<end].scale back(0, +)
    }
    operationQueue.addOperation(operation)
}

operationQueue.waitUntilAllOperationsAreFinished()

print("Sum: (sum)")

Each of the examples are above are extra ore much less good to go (if we glance by way of at potential knowledge race & synchronization), however they rely upon extra frameworks. In different phrases they’re non-native Swift options. What if we may do one thing higher utilizing structured concurrency?

let staff: Int = 8
let numbers: [Int] = Array(repeating: 1, rely: 1_000_000_000)

let sum = await withTaskGroup(of: Int.self) { group in
    for i in 0..<staff {
        group.addTask {
            let begin = i * numbers.rely / staff
            let finish = (i + 1) * numbers.rely / staff
            return numbers[start..<end].scale back(0, +)
        }
    }

    var abstract = 0
    for await outcome in group {
        abstract += outcome
    }
    return abstract
}

print("Sum: (sum)")

By utilizing activity teams you possibly can simply setup the employees and run them in parallel by including a activity to the group. Then you possibly can look ahead to the partial sum outcomes to reach and sum the whole lot up utilizing a thread-safe resolution. This method is nice, however is it potential to restrict the utmost variety of concurrent operations, similar to we did with operation queues? πŸ€·β€β™‚οΈ

func parallelTasks<T>(
    iterations: Int,
    concurrency: Int,
    block: @escaping ((Int) async throws -> T)
) async throws -> [T] {
    attempt await withThrowingTaskGroup(of: T.self) { group in
        var outcome: [T] = []

        for i in 0..<iterations {
            if i >= concurrency {
                if let res = attempt await group.subsequent() {
                    outcome.append(res)
                }
            }
            group.addTask {
                attempt await block(i)
            }
        }

        for attempt await res in group {
            outcome.append(res)
        }
        return outcome
    }
}


let staff: Int = 8
let numbers: [Int] = Array(repeating: 1, rely: 1_000_000_000)

let res = attempt await parallelTasks(
    iterations: staff,
    concurrency: 4
) { i in
    print(i)
    let begin = i * numbers.rely / staff
    let finish = (i + 1) * numbers.rely / staff
    return numbers[start..<end].scale back(0, +)
}

print("Sum: (res.scale back(0, +))")

It’s potential, I made just a little helper perform just like the concurrentPerform technique, this manner you possibly can execute quite a few duties and restrict the extent of concurrency. The principle thought is to run quite a few iterations and when the index reaches the utmost variety of concurrent gadgets you wait till a piece merchandise finishes and then you definitely add a brand new activity to the group. Earlier than you end the duty you additionally need to await all of the remaining outcomes and append these outcomes to the grouped outcome array. 😊

That is it for now, I hope this little article will allow you to to handle concurrent operations a bit higher.

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