Heterogeneous computing and .NET

[meghuizen]

I just want to propose an idea on what really could benefit the .NET landscape. Especially with the AI world now exploding with all relying on the Python ecosystem, due to it's quality machine learning libraries with quality support for CUDA, ROCm, etc. Though .NET has support for this, it's limited at this point.

Also with the increase of specialized chips (GPU, NPU, TPU, APU, FPGA, etc.) and directing specific workloads to specific compute platforms/chips, I think there comes more and more a need to have good foundational support for that in your development environment.

Also with the rise of quantum computing. I expect quantum computing to not fully run all forms of computing with will also specialize on certain types of calculations and therefor have a similar role as the GPU, NPU, FPGA's etc. to which you offload certain workloads to. While Q# support in .NET does a really good job in experimenting with this, I think the role of where Q# fits in this might change a bit when quantum computing becomes more main stream (even though that might be years/decades ahead).

What is Heterogeneous computing?
Heterogeneous computing typically refers to a system that uses multiple types of computing cores, like CPUs, GPUs, ASICs, FPGAs, and NPUs. By assigning different workloads to processors that are designed for specific purposes or specialized processing, performance and energy efficiency is improved. The term “heterogenous compute” may also refer to the use of processors based on different computer architectures, a common approach when a particular architecture is better suited for a specific task due to power efficiency, compatibility, or the number of cores available.

Example Projects
SYCL: Intel has, with their GPU acceleration architecture support for the SYCL language. SYCL can also build CUDA kernels or FPGA models. You can read more on that, here: https://www.khronos.org/sycl/

How can .NET benefit from Heterogeneous computing?

• Parallel Execution: Automatically offload heavy matrix operations to GPUs, NPUs, TPUs.
• Auto-Optimized Execution: Use the best device (CPU, GPU, or specialized accelerator) for each task.
• Kernel Fusion: Reduce memory overhead by batching operations into a single compute unit.
• One .NET Codebase → Multiple Backends (CUDA, ROCm, OpenCL, SYCL, Metal, DirectML, MLX).
• Portable Execution: Deploy AI workloads across Windows, Linux, macOS, and cloud GPUs.
• Efficient Memory Management: Auto-handle data movement between RAM, VRAM, and cache.

Compile time vs Runtime
Since the specialized hardware cannot be determined on compile time, you might want to decide on runtime on which hardware is available. Though you might want to leverage JIT, AOT and Source Generation into this and therefor want to overlap into both worlds.
But I think the main focus area of this might be on runtime.

How can something like this look like?

    // The language exposes compute kernels as normal static methods.
    // The runtime JIT compiler inspects the Compute attribute and 
    // compiles the entire method as a kernel for the chosen device.
    public static partial class ComputeKernels
    {
        [Compute("GPU")]
        public static Vector ComputeMatVec(Matrix A, Vector x)
        {
            // Write your computation as normal C# code.
            var result = A * x;           // Matrix-vector multiplication
            var activated = ReLU(result); // ReLU activation
            return activated;
        }

        // Helper: ReLU activation.
        private static Vector ReLU(Vector v)
        {
            var res = new Vector(v.Length);
            for (var i = 0; i < v.Length; i++)
                res[i] = v[i] > 0 ? v[i] : 0;
            return res;
        }
    }

    // Other Option: The new language extension: a compute kernel. The "compute kernel" construct is
    // similar to a normal C# method but is marked for offloading and compiled to specialized hardware.
    // The syntax here is an extension: the "compute kernel" keyword, an explicit signature with an arrow,
    // and a body written in standard C# (using var everywhere).
    [ComputeTarget("CUDA,ROCm,MLX,FPGA,SYCL")]
    compute kernel MatVecKernel(Matrix A, Vector x) -> Vector
    {
        // Standard C# code: perform matrix-vector multiplication and then apply ReLU.
        var result = A * x;
        var activated = ReLU(result);
        return activated;
    }

// maybe even have the ability to use Q# inside a compute block
[ComputeTarget("Quantum")]
compute quantum kernel MatVecReLUKernel(Matrix A, Vector x) -> Vector
{
    // --- STEP 1: Quantum State Preparation ---
    var qubits = QuantumRuntime.PrepareQuantumState(x);

    // --- STEP 2: Matrix-Vector Multiplication via Unitary Transformation ---
    QuantumOperators.ApplyMatrixUnitary(A, qubits);

    // --- STEP 3: ReLU Activation via Measurement and Thresholding ---
    var y = new Vector(x.Length);
    for (var i = 0; i < qubits.Length; i++)
    {
        // Measure qubit i. In Q#, measurement returns either Zero or One.
        var outcome = M(qubits[i]);
        // In our simulation, we interpret 'One' as a positive (unchanged) value,
        // and 'Zero' as a negative value that gets thresholded to 0.
        y[i] = (outcome == One) ? x[i] : 0.0;
    }

    // Reset qubits to the |0> state.
    ResetAll(qubits);

    return y;
}
    
    // The main program: the developer writes standard C#.
    // The DSL JIT compiler behind the scenes compiles ComputeMatVec into
    // a device-specific kernel and automatically selects the appropriate backend.
    public static class Program
    {
        public static void Main()
        {
            // Create sample Matrix and Vector.
            var A = new Matrix(4, 4);
            var x = new Vector(4);

            // Populate A and x.
            for (var i = 0; i < 4; i++)
            {
                x[i] = i + 1;
                for (var j = 0; j < 4; j++)
                    A.Data[i, j] = (i + 1) * (j + 1);
            }

            // Developer calls ComputeMatVec normally.
            // At runtime, the DSL JIT compiler determines whether to offload to CUDA
            // or to execute on the CPU if no GPU is available.
            var result = ComputeKernels.ComputeMatVec(A, x);

            // Compute code can also be external files instead of inline compute blocks and loaded and run

            // Output the result.
            Console.WriteLine("Result:");
            for (var i = 0; i < result.Length; i++)
                Console.WriteLine(result[i]);
        }
    }