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Print all known information about the GPU on Apple-designed chips

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- bing-conversation.md
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- build-destination-screenshot.png
- - AppleGPUInfo.swift
  - CAppleGPUInfo.swift
- - main.swift
- - AppleGPUInfoTests.swift
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.spi.yml
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README.md

// repository documentation

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# Apple GPU Info

This is a mini-framework for querying parameters of an Apple-designed GPU. It also contains a command-line tool, `gpuinfo`, which reports information similarly to [clinfo](https://github.com/Oblomov/clinfo). It was co-authored with an AI.

## Features

Listed parameters:
- Name ✅
- Vendor ✅
- Core count ✅
- Clock frequency ✅
- Bandwidth ✅
- FLOPS (FP32 operations per second) ✅
- IPS (shader instructions per second) ✅
- System-level cache ✅
- Memory ✅
- Family ✅

Interfaces:
- Swift module
- [C bindings](./Sources/AppleGPUInfo/include/AppleGPUInfo/AppleGPUInfo.h)
- Command-line tool

Recognized devices:
- A7 - A17 Pro
- M1 - M2 Ultra
- M3 - M3 Max
- M4 - M4 Max (with LPDDR5X-8533 memory support)
- Future devices treated like the closest existing analog

## Usage

One way to use this library is from the command-line:

```
git clone https://github.com/philipturner/applegpuinfo
cd applegpuinfo
swift run gpuinfo list

# Sample output
GPU name: Apple M1 Max
GPU vendor: Apple
GPU core count: 32
GPU clock frequency: 1.296 GHz
GPU bandwidth: 409.6 GB/s
GPU FLOPS: 10.617 TFLOPS
GPU IPS: 5.308 TIPS
GPU system level cache: 48 MB
GPU memory: 32 GB
GPU family: Apple 7

# Sample output (M4 Max)
GPU name: Apple M4 Max
GPU vendor: Apple
GPU core count: 40
GPU clock frequency: 1.38 GHz
GPU bandwidth: 546.0 GB/s
GPU FLOPS: 14.131 TFLOPS
GPU IPS: 14.131 TIPS
GPU system level cache: 48 MB
GPU memory: 128 GB
GPU family: Apple 9
```

You can also use it directly from Swift:

```swift
// Inside package manifest
dependencies: [
  // Dependencies declare other packages that this package depends on.
  .package(url: "https://github.com/philipturner/applegpuinfo", branch: "main"),
],

// Inside source code
import AppleGPUInfo

let device = try GPUInfoDevice()
print(device.flops)
print(device.bandwidth)
```

## Methodology

Original Goal: In one hour, finish a mini-package and command-line tool for querying Apple GPU device parameters.

Results: I spent 57 minutes finishing the file that wraps the `AppleGPUDevice` structure. I asked GPT-4 to generate the tests and command-line tool. I renamed the command-line tool from `applegpuinfo` to `gpuinfo` according to the AI's suggestion. Finally, I congratulated it and asked for it to leave a comment to users on the README. That triggered a safeguard and it quit the conversation. The stop time was 1 hour, 25 minutes.

Documentation of AI contributions: [bing-conversation.md](./Documentation/bing-conversation.md)

After creating the first release of the library, I have continued experimenting with workflows accelerated by _free_ access to GPT-4. The above document details these subsequent modifications to the library.

## Testing

This framework is confirmed to work on the following devices. If anyone wishes to contribute to this list, please paste the output of `gpuinfo` into a new GitHub issue. Next, paste the last 15 lines generated by the following command. Different variations of the same chip (e.g. different cores or memory) are welcome.

```
sudo powermetrics --sample-rate 500 --samplers gpu_power
```

| Production Year | Chip | Cores | SLC | Memory | Bandwidth | TFLOPS |
| --------------- | --- | ----- | ------ | ---- | --------- | ------ |
| 2017 | A10X     | 12 |  0 MB |    4 GB |  68.2 GB/s |  0.768 |
| 2020 | A14      |  4 | 16 MB | 5.54 GB |  34.1 GB/s |  0.654 |
| 2020 | M1       |  8 |  8 MB |    8 GB |  68.3 GB/s |  2.617 |
| 2021 | A15      |  5 | 32 MB | 5.49 GB |  34.1 GB/s |  1.713 |
| 2021 | M1 Pro   | 14 | 24 MB |   16 GB | 204.8 GB/s |  4.645 |
| 2021 | M1 Pro   | 16 | 24 MB |   32 GB | 204.8 GB/s |  5.308 |
| 2021 | M1 Max   | 32 | 48 MB |   32 GB | 409.6 GB/s | 10.617 |
| 2022 | M1 Ultra | 48 | 96 MB |   64 GB | 819.2 GB/s | 15.925 |
| 2022 | M2       | 10 |  8 MB |   16 GB | 102.4 GB/s |  3.579 |
| 2023 | M2 Pro   | 19 | 24 MB |   16 GB | 204.8 GB/s |  6.800 |
| 2023 | M2 Pro   | 19 | 24 MB |   32 GB | 204.8 GB/s |  6.800 |

`gpuinfo` runs on macOS. On iOS, you need to create an Xcode project. 
- Go to File > New > Project > iOS > App. 
- Click Next, ensure the interface is SwiftUI, and give the project a name. Click Next > Create.
- Go to File > Add Package Dependencies > Search or Enter Package URL. Type https://github.com/philipturner/applegpuinfo and click Add Package.
- Select the AppleGPUInfo product of kind Library, then click Add Package.
- Replace the code in `ContentView.swift` with the demonstration below.
- Connect your iOS device to your Mac with a USB cable. Ensure it is targeted in the [build destination](./Documentation/build-destination-screenshot.png).
- Press `Cmd + R` and look for text in the Xcode console.
- Paste the console output into a [GitHub issue](https://github.com/philipturner/applegpuinfo/issues).

<details>
<summary>iOS Demonstration</summary>

```swift
import AppleGPUInfo
import SwiftUI

struct ContentView: View {
  var body: some View {
    VStack {
      Image(systemName: "globe")
        .imageScale(.large)
        .foregroundStyle(.tint)
      Text({ () -> String in
        logHardwareSpecifications()
        return "Hello, world!"
      }())
    }
    .padding()
  }
}

func logHardwareSpecifications() {
  do {
    // Create an instance of GPUInfoDevice using its initializer
    let error = setenv("GPUINFO_LOG_LEVEL", "1", 1)
    if error != 0 {
      print("'setenv' failed with error code '\(error)'.")
    }
    _ = try GPUInfoDevice()
  } catch {
    // Handle any errors that may occur
    print("Error: \(error.localizedDescription)")
  }
}
```

</details>

## Attribution

This project was made possible by [GPT-4](https://openai.com/research/gpt-4), accessed through Bing Chat.

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