Gemini New chat My stuff Gems

Chess champ Chats Dart Autograd Engine Analysis Oracle Linux File Descriptor Checks Asterisk Dialplan Optimization and ARI Livewire Break Timer Update ARI Call Hangup Race Condition Attention Free Transformer Explained AI Attendance System Pitch Refinement Calculating Agent Break Times explain this sip message bye cause: │BYE sip:alice@10.44.0.56:5060 SIP/2.0 10.44.0.56:5060 10.44.0.70:5060│Via: SIP/2.0/UDP 10.44.0.70:5060;rport;b ──────────┬───────── ──────────┬───────│nch=z9hG4bKPjb41061e9-afc3-4bca-8697-dec 15:05:11.542262 │ INVITE (SDP) │ │99b9d6e +0.001314 │ ──────────────────────────> │ │From: sip:6003@10.44.0.70;tag=214ce3fe 15:05:11.543576 │ 401 Unauthorized │ │048-45f6-9755-7aae8f8fd0e4 +0.000118 │ <────────────────────────── │ │To: sip:alice@10.44.0.56;tag=as62db7e3 15:05:11.543694 │ ACK │ │Call-ID: 33e8fef84df0c4534be1a7215b1a463 +0.000125 │ ──────────────────────────> │ │10.44.0.56:5060 15:05:11.543819 │ INVITE (SDP) │ │CSeq: 15148 BYE +0.001586 │ ──────────────────────────> │ │Reason: Q.850;cause=16 15:05:11.545405 │ 100 Trying │ │Max-Forwards: 70 +0.007404 │ <────────────────────────── │ │User-Agent: Asterisk PBX 22.0.0 15:05:11.552809 │ 200 OK (SDP) │ │Content-Length: 0 How Helium Is Made Call Center Intrusion with ARI Flutter Smartphone IoT Sensor Integration Agent Module Test Report Refinement Following Up On Promised Promotion ARI Agent State Race Condition Asterisk Race Condition in Agent Selection Asterisk IVR Dialplan Refinement Salt api monitor reconaisance mode stores users requests and response Building Quiche DLL for Windows Artisan Command for Model Stats Git Commit History Commands Explained i have this log: PS C:\www\dart\flutter-quic-test> dart pub get Resolving dependencies... Downloading packages... fe_analyzer_shared 88.0.0 (93.0.0 available) analyzer 8.1.1 (10.0.1 available) characters 1.4.0 (1.4.1 available) flutter_quic 0.1.3-beta (1.0.0 available) json_annotation 4.9.0 (4.10.0 available) lints 5.1.1 (6.0.0 available) material_color_utilities 0.11.1 (0.13.0 available) meta 1.16.0 (1.18.1 available) Got dependencies! 8 packages have newer versions incompatible with dependency constraints. Try dart pub outdated for more information. PS C:\www\dart\flutter-quic-test> dart run Building package executable... Built flutter_quic_test:flutter_quic_test. Unhandled exception: Invalid argument(s): Failed to load dynamic library 'flutter_quic.dll': The specified module could not be found. (error code: 126) #0 open (dart:ffi-patch/ffi_dynamic_library_patch.dart:11:43) #1 new DynamicLibrary.open (dart:ffi-patch/ffi_dynamic_library_patch.dart:22:12) #2 new ExternalLibrary.open (package:flutter_rust_bridge/src/platform_types/io.dart:42:47) #3 loadExternalLibraryRaw.

Settings and help Conversation with Gemini You said but my gpu runs on pytorch without any issues

Gemini said That's a very important point, and it highlights a key difference between running pre-compiled CUDA applications (like PyTorch) and compiling your own CUDA code.

The fact that PyTorch works means the essential runtime components of the NVIDIA driver are installed and functional. However, your setup is missing the development and management components.

The Difference: Runtime vs. Development Kit ⚙️ #

Think of it like a car:

Running PyTorch: This is like driving the car. You have the keys and the engine works, so you can get from place to place. The basic driver runtime is the "engine" that PyTorch needs.

Compiling with nvcc: This is like being a mechanic trying to build or modify the engine. To do this, you need the full workshop: blueprints, diagnostic tools, and specialized equipment.

The nvcc compiler is part of that workshop (the CUDA Development Toolkit). The nvidia-smi.exe tool is your main diagnostic computer.

What's Happening on Your System #

PyTorch's Needs (Runtime): PyTorch comes with its own pre-compiled CUDA kernels. To run them, it only needs to find a compatible NVIDIA driver on your system that can execute the code. Even a partial or minimal driver installation (like one Windows might install automatically) can be enough for this.

Your Needs (Development): You are trying to be a developer. The nvcc compiler needs the full driver suite and CUDA Toolkit to be correctly installed and integrated. It needs to query the driver for device information, link against specific libraries, and use other parts of the toolkit.

The Bottom Line: The fact that nvidia-smi.exe is missing is undeniable proof that your driver installation is incomplete. This incomplete installation is exactly why the nvcc compiler is crashing.

Even though you can "drive the car" with PyTorch, you can't "build the engine" until you fix the workshop. The solution remains the same: you must perform a clean reinstallation of the NVIDIA driver to install all the necessary development and management components.

You said so where can i find already compiled pytorch dll for running using dart ffi

Gemini said You can find the already-compiled PyTorch DLLs inside any standard PyTorch installation, but you cannot call them directly using Dart FFI in a practical way. The core issue is that PyTorch exposes a complex C++ API (using classes, templates, and objects), while Dart FFI requires a simple C-style API.

Instead of finding a single DLL, the correct approach is to use PyTorch's official C++ distribution, called LibTorch, and create your own C-style wrapper around it.

Why You Can't Directly Call PyTorch from Dart FFI #

C++ vs. C API: Dart FFI is designed to call simple C functions like int add(int a, int b). PyTorch's C++ API involves complex objects like torch::Tensor and classes like torch::jit::script::Module. Dart FFI doesn't know how to create, manage, or destroy these C++ objects.

Dependency Hell: PyTorch is not one DLL. It's a collection of many (torch.dll, torch_cpu.dll, c10.dll, etc.) that all depend on each other. Managing this entire ecosystem from Dart would be extremely difficult.

Think of it this way: Dart FFI knows how to use a simple light switch (a C function), but PyTorch gives you a complex electrical engineering panel (a C++ API). You need to build a simple switch that controls the complex panel.

The Correct Approach: The C Wrapper Method #

This is the standard workflow for using complex C++ libraries in other languages. You create a simple C "middleman" that Dart can talk to.

Step 1: Download LibTorch First, download the official PyTorch C++ distribution, LibTorch, from the PyTorch website. Be sure to get the version that matches your system (Windows/Linux, CPU/CUDA). This package contains all the necessary headers and compiled DLLs.

Step 2: Write a C Wrapper (wrapper.cpp) Next, create a C++ file that will act as your bridge. This file will use the C++ LibTorch API internally but will expose simple extern "C" functions that Dart can understand.

Here's a simple example that creates a tensor and gets its data:

wrapper.cpp

C++ #include <torch/torch.h> #include

// The extern "C" block tells the C++ compiler to create simple C-style functions // without any name mangling, so Dart can find them easily. extern "C" {

// This function creates a tensor and returns a pointer to it.
// We return it as a void* so Dart doesn't need to know what a torch::Tensor is.
__declspec(dllexport) void* create_tensor(float* data, int size) {
    try {
        // Use the C++ API to create the tensor
        auto tensor = torch::from_blob(data, {size});
        // We need to clone it because from_blob doesn't own the memory.
        auto* tensor_ptr = new torch::Tensor(tensor.clone());
        return tensor_ptr;
    } catch (const c10::Error& e) {
        std::cerr << "PyTorch Error: " << e.what() << std::endl;
        return nullptr;
    }
}

// This function retrieves the data from a tensor.
__declspec(dllexport) void get_tensor_data(void* tensor_ptr, float* out_data) {
    auto* tensor = static_cast<torch::Tensor*>(tensor_ptr);
    // Copy tensor data to the output buffer
    memcpy(out_data, tensor->data_ptr<float>(), tensor->numel() * sizeof(float));
}

// It's CRITICAL to have a function to free the memory you allocated.
__declspec(dllexport) void destroy_tensor(void* tensor_ptr) {
    if (tensor_ptr) {
        delete static_cast<torch::Tensor*>(tensor_ptr);
    }
}

} Step 3: Compile Your Wrapper into a DLL Now, you compile wrapper.cpp into a single DLL, linking it against the LibTorch libraries. This command is complex and must be run from a Developer Command Prompt for Visual Studio.

PowerShell

This command is an example and paths must be adjusted #

cl /LD /I "C:\path\to\libtorch\include" /I "C:\path\to\libtorch\include\torch\csrc\api\include" wrapper.cpp /link /LIBPATH:"C:\path\to\libtorch\lib" c10.lib torch.lib torch_cpu.lib This creates wrapper.dll. This is the only DLL your Dart code needs to know about.

Step 4: Call from Dart Finally, your Dart code loads wrapper.dll and calls the simple C functions you defined.

main.dart

Dart import 'dart:ffi'; import 'package:ffi/ffi.dart';

// Define C function signatures typedef CreateTensorNative = Pointer

// Define Dart function types typedef CreateTensor = Pointer

void main() { final dylib = DynamicLibrary.open('path/to/wrapper.dll');

final createTensor = dylib.lookupFunction<CreateTensorNative, CreateTensor>('create_tensor'); final getTensorData = dylib.lookupFunction<GetTensorDataNative, GetTensorData>('get_tensor_data'); final destroyTensor = dylib.lookupFunction<DestroyTensorNative, DestroyTensor>('destroy_tensor');

// Prepare data in Dart final data = calloc

// Call the C++ wrapper to create the tensor final tensorPtr = createTensor(data, 5); print('Created tensor at address: $tensorPtr');

// Prepare a buffer to get the data back final outData = calloc

print('Data from tensor: ${outData.asTypedList(5)}');

// IMPORTANT: Clean up the C++ memory destroyTensor(tensorPtr); print('Tensor destroyed.');

calloc.free(data); calloc.free(outData); }

Alternative: The ONNX Runtime #

For model inference, a much easier path is to use the ONNX Runtime.

Train in Python: Train your model in PyTorch as usual.

Export to ONNX: Export the trained model to the open ONNX format (.onnx).

Use ONNX Runtime C API: The ONNX Runtime has a clean, simple C API designed for exactly this purpose. You can use Dart FFI to call the ONNX Runtime library to load and run your .onnx model, which is far simpler than trying to wrap LibTorch yourself.

You said will these operations run on gpu?

Gemini said No, the specific operations in the example I provided will run on the CPU.

There are two key reasons for this, both in the code and in the compilation command.

Why It Runs on the CPU #

Default Behavior: In LibTorch (and PyTorch), when you create a tensor without specifying a device, it is created on the CPU by default. The line torch::from_blob(data, {size}) does not have any device placement instructions.

Linked Libraries: The example compilation command explicitly links against the CPU-specific PyTorch library:

PowerShell ... /link ... c10.lib torch.lib torch_cpu.lib To run on the GPU, you would need to link against the CUDA-specific libraries instead.

How to Make It Run on the GPU 🚀 #

To make these operations run on your NVIDIA GPU, you need to make two changes, assuming you have downloaded the CUDA version of LibTorch and have your drivers set up correctly.

Modify the C++ Code You need to explicitly tell LibTorch to move the tensor to the GPU. This is done by calling the .to() method on the tensor and specifying the CUDA device.

wrapper.cpp (Modified for GPU)

C++ #include <torch/torch.h>

extern "C" { __declspec(dllexport) void* create_tensor(float* data, int size) { // Create a CPUTensor from the input data first auto cpu_tensor = torch::from_blob(data, {size});

    // *** Move the tensor to the GPU ***
    // This is the key change.
    auto gpu_tensor = cpu_tensor.to(torch::kCUDA);

    // Store the new GPU tensor on the heap
    auto* tensor_ptr = new torch::Tensor(gpu_tensor);
    return tensor_ptr;
}

__declspec(dllexport) void get_tensor_data(void* tensor_ptr, float* out_data) {
    auto* gpu_tensor = static_cast<torch::Tensor*>(tensor_ptr);

    // *** Move the tensor back to the CPU to access its data ***
    auto cpu_tensor = gpu_tensor->to(torch::kCPU);

    // Now you can safely copy the data
    memcpy(out_data, cpu_tensor.data_ptr<float>(), cpu_tensor.numel() * sizeof(float));
}

__declspec(dllexport) void destroy_tensor(void* tensor_ptr) {
    if (tensor_ptr) {
        delete static_cast<torch::Tensor*>(tensor_ptr);
    }
}

} 2. Modify the Compilation Command You must link against the CUDA libraries from the LibTorch package instead of the CPU-only ones.

PowerShell

Note the change from torch_cpu.lib to torch_cuda.lib #

cl /LD /I "C:\path\to\libtorch\include" ... wrapper.cpp /link /LIBPATH:"C:\path\to\libtorch\lib" c10.lib torch.lib torch_cuda.lib c10_cuda.lib ... By making these changes, the create_tensor function will now perform a memory copy from the CPU (where your Dart data lives) to the GPU, and all subsequent operations on that tensor in your C++ code would execute on the GPU.

You said I want to write my own cuda matrix multiplication and call it in dart

Gemini said Of course. This is an excellent goal and a perfect use case for FFI. We'll go through the complete process step-by-step:

Write the CUDA code for matrix multiplication (.cu file).

Compile it into a shared library (.dll on Windows).

Write the Dart code to load the library, prepare the data, and call the function.

Here is the complete guide.

Step 1: Write the CUDA Matrix Multiplication Code First, create a file named matmul.cu. This file will contain both the GPU kernel and the C-style wrapper function that Dart will call.

This example will multiply matrix A (M x K) by matrix B (K x N) to produce matrix C (M x N).

matmul.cu

C++ #include

// Platform-specific macro for exporting functions from a DLL/shared library #if defined(_WIN32) || defined(_WIN64) #define DLLEXPORT __declspec(dllexport) #else #define DLLEXPORT attribute((visibility("default"))) #endif

/**

@brief CUDA kernel for matrix multiplication (C = A * B).
- @param A Pointer to matrix A on the device.
@param B Pointer to matrix B on the device.
@param C Pointer to matrix C on the device.
@param M Number of rows in A and C.
@param N Number of columns in B and C.
@param K Number of columns in A and rows in B. / global void matrixMulKernel(float A, float* B, float* C, int M, int N, int K) { // Calculate the row and column of the C matrix element to compute int row = blockIdx.y * blockDim.y + threadIdx.y; int col = blockIdx.x * blockDim.x + threadIdx.x;

// Ensure we don't go out of bounds of the C matrix if (row < M && col < N) { float C_value = 0.0; // Compute the dot product for C[row][col] for (int i = 0; i < K; ++i) { C_value += A[row * K + i] * B[i * N + col]; } C[row * M + col] = C_value; } }

/**

@brief C-style wrapper function that Dart FFI will call.
This function orchestrates the entire GPU operation. / extern "C" DLLEXPORT void matrix_multiply_cuda( float host_A, float* host_B, float* host_C, int M, int N, int K ) { // Pointers for device (GPU) memory float *dev_A, *dev_B, *dev_C;

// Calculate the size in bytes for each matrix int size_A = M * K * sizeof(float); int size_B = K * N * sizeof(float); int size_C = M * N * sizeof(float);

// 1. Allocate memory on the GPU cudaMalloc(&dev_A, size_A); cudaMalloc(&dev_B, size_B); cudaMalloc(&dev_C, size_C);

// 2. Copy matrices from host (CPU) to device (GPU) cudaMemcpy(dev_A, host_A, size_A, cudaMemcpyHostToDevice); cudaMemcpy(dev_B, host_B, size_B, cudaMemcpyHostToDevice);

// 3. Configure the kernel launch parameters // We'll use 16x16 thread blocks dim3 threadsPerBlock(16, 16); dim3 numBlocks((N + 15) / 16, (M + 15) / 16);

printf("Launching kernel...\n"); // 4. Launch the kernel on the GPU matrixMulKernel<<<numBlocks, threadsPerBlock>>>(dev_A, dev_B, dev_C, M, N, K);

// Synchronize to ensure the kernel has finished before copying back cudaDeviceSynchronize(); printf("Kernel finished.\n");

// 5. Copy the result matrix C from device (GPU) back to host (CPU) cudaMemcpy(host_C, dev_C, size_C, cudaMemcpyDeviceToHost);

// 6. Free the allocated memory on the GPU cudaFree(dev_A); cudaFree(dev_B); cudaFree(dev_C); } Step 2: Compile the CUDA Code into a DLL Now that your code is ready, compile it. Since you're on Windows, you'll use this command in your terminal. This assumes you have fixed your NVIDIA driver and CUDA Toolkit installation.

PowerShell nvcc -shared -o matmul.dll matmul.cu If successful, you will see a new file named matmul.dll in your directory. This is the compiled library that Dart will load.

Step 3: Write the Dart Code Create a file named main.dart. This script will define the matrices, load matmul.dll, call the function, and print the result.

main.dart

Dart import 'dart:ffi'; import 'package:ffi/ffi.dart'; import 'dart:io' show Platform;

// 1. Define the FFI function signatures to match the C/C++ wrapper. // This is the native C function signature. typedef MatMulFunc = Void Function( Pointer

// This is the Dart-friendly version of the function. typedef MatMul = void Function( Pointer

void main() { // Define matrix dimensions: A(M x K) * B(K x N) = C(M x N) const M = 4; const K = 3; const N = 4;

// 2. Load the compiled DLL. // Assumes matmul.dll is in the same directory as the script. final dylib = DynamicLibrary.open('matmul.dll');

// 3. Look up the function by its C name. final matMulCuda = dylib.lookupFunction<MatMulFunc, MatMul>('matrix_multiply_cuda');

// 4. Allocate memory for the matrices that C can understand. // Matrices are stored as flat, 1D arrays in row-major order. final Pointer

// 5. Populate the input matrices A and B with some data. final matrixA = hostA.asTypedList(M * K); final matrixB = hostB.asTypedList(K * N);

// Matrix A (4x3) matrixA.setAll(0, [ 1, 2, 3, // row 0 4, 5, 6, // row 1 7, 8, 9, // row 2 1, 1, 1, // row 3 ]);

// Matrix B (3x4) matrixB.setAll(0, [ 9, 8, 7, 6, // row 0 5, 4, 3, 2, // row 1 1, 2, 3, 4, // row 2 ]);

print('Matrix A:'); printMatrix(hostA, M, K); print('Matrix B:'); printMatrix(hostB, K, N);

// 6. Call the CUDA function! print('\nCalling CUDA function...'); matMulCuda(hostA, hostB, hostC, M, N, K); print('CUDA function returned.');

// 7. Print the result from matrix C. print('\nResult Matrix C:'); printMatrix(hostC, M, N);

// 8. CRITICAL: Free the allocated memory to avoid leaks. calloc.free(hostA); calloc.free(hostB); calloc.free(hostC); }

/// Helper function to print a matrix from a flat array. void printMatrix(Pointer

PowerShell dart run main.dart Expected Output:

You will see the output from both the C++ printf statements and the Dart print statements.

Matrix A: 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 1.0 1.0 1.0 Matrix B: 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 2.0 3.0 4.0

Calling CUDA function... Launching kernel... Kernel finished. CUDA function returned.

Result Matrix C: 22.0 22.0 22.0 22.0 67.0 64.0 61.0 58.0 112.0 106.0 100.0 94.0 15.0 14.0 13.0 12.0

You have now successfully written a custom CUDA kernel, compiled it, and executed it on the GPU using Dart.

You said PS C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools> & "C:\Windows\System32\DriverStore\FileRepository\nvmiui.inf_amd64_727fe9a457c47c84/nvidia-smi.exe"

Thu Sep 11 13:11:23 2025

+-----------------------------------------------------------------------------------------+

| NVIDIA-SMI 576.57 Driver Version: 576.57 CUDA Version: 12.9 |

|-----------------------------------------+------------------------+----------------------+

| GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC |

| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |

| | | MIG M. |

|=========================================+========================+======================|

| 0 NVIDIA GeForce RTX 3060 ... WDDM | 00000000:01:00.0 Off | N/A |

| N/A 46C P8 11W / 80W | 0MiB / 6144MiB | 0% Default |

| | | N/A |

+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+

| Processes: |

| GPU GI CI PID Type Process name GPU Memory |

| ID ID Usage |

|=========================================================================================|

| No running processes found |

+-----------------------------------------------------------------------------------------+

PS C:\Program Files (x86)\Microsoft Visual Studio\2022\BuildTools>

Gemini said Excellent! You found it. This output is exactly what we needed and it confirms everything.

Great News: Your Driver and Toolkit are Compatible ✅ #

This nvidia-smi output tells us two crucial things:

Your Driver Version is 576.57.

This driver supports up to CUDA Version 12.9.

Since you previously confirmed your installed CUDA Toolkit is also version 12.9, we now know for sure that your driver and toolkit are a perfect match. This means version incompatibility is not the cause of the compiler error.

What This Means #

The fact that you had to find nvidia-smi.exe in that deep system folder (DriverStore\FileRepository) confirms our theory: the driver is installed, but the installation was flawed and didn't set up the normal system paths correctly.

This faulty installation was almost certainly why the nvcc compiler was crashing with an ACCESS_VIOLATION—it couldn't properly communicate with the driver.

Next Step: Try Compiling Again #

Now that we know the core components are functional and compatible, you should try compiling your matmul.cu file again. The compiler should be able to work correctly now.

Navigate back to the directory with your matmul.cu file and run the command:

PowerShell nvcc -shared -o matmul.dll matmul.cu It should now compile successfully without the ACCESS_VIOLATION error, creating your matmul.dll file. After that, you can proceed with running your Dart script.

You said i do not have matmul.cu

Gemini said My apologies, you are correct. I provided the compilation command assuming you had already created the file using the code from my earlier explanation.

Here is the complete code for the matmul.cu file again.

Code for matmul.cu #

Please create a new file named matmul.cu in your project directory and paste the following code into it.

C++ #include

/**

@brief CUDA kernel for matrix multiplication (C = A * B).
- @param A Pointer to matrix A on the device.
@param B Pointer to matrix B on the device.
@param C Pointer to matrix C on the device.
@param M Number of rows in A and C.
@param N Number of columns in B and C.
@param K Number of columns in A and rows in B. / global void matrixMulKernel(float A, float* B, float* C, int M, int N, int K) { // Calculate the row and column of the C matrix element to compute int row = blockIdx.y * blockDim.y + threadIdx.y; int col = blockIdx.x * blockDim.x + threadIdx.x;

// Ensure we don't go out of bounds of the C matrix if (row < M && col < N) { float C_value = 0.0; // Compute the dot product for C[row][col] for (int i = 0; i < K; ++i) { C_value += A[row * K + i] * B[i * N + col]; } C[row * M + col] = C_value; } }

/**

@brief C-style wrapper function that Dart FFI will call.
This function orchestrates the entire GPU operation. / extern "C" DLLEXPORT void matrix_multiply_cuda( float host_A, float* host_B, float* host_C, int M, int N, int K ) { // Pointers for device (GPU) memory float *dev_A, *dev_B, *dev_C;

// Calculate the size in bytes for each matrix int size_A = M * K * sizeof(float); int size_B = K * N * sizeof(float); int size_C = M * N * sizeof(float);

// 1. Allocate memory on the GPU cudaMalloc(&dev_A, size_A); cudaMalloc(&dev_B, size_B); cudaMalloc(&dev_C, size_C);

// 2. Copy matrices from host (CPU) to device (GPU) cudaMemcpy(dev_A, host_A, size_A, cudaMemcpyHostToDevice); cudaMemcpy(dev_B, host_B, size_B, cudaMemcpyHostToDevice);

// 3. Configure the kernel launch parameters // We'll use 16x16 thread blocks dim3 threadsPerBlock(16, 16); dim3 numBlocks((N + 15) / 16, (M + 15) / 16);

printf("Launching kernel...\n"); // 4. Launch the kernel on the GPU matrixMulKernel<<<numBlocks, threadsPerBlock>>>(dev_A, dev_B, dev_C, M, N, K);

// Synchronize to ensure the kernel has finished before copying back cudaDeviceSynchronize(); printf("Kernel finished.\n");

// 5. Copy the result matrix C from device (GPU) back to host (CPU) cudaMemcpy(host_C, dev_C, size_C, cudaMemcpyDeviceToHost);

// 6. Free the allocated memory on the GPU cudaFree(dev_A); cudaFree(dev_B); cudaFree(dev_C); }

What to Do Next #

Create the file: Make a new file named matmul.cu.

Paste the code: Copy the code block above and paste it into matmul.cu.

Save the file.

Now, compile it by running the nvcc command in your terminal:

PowerShell nvcc -shared -o matmul.dll matmul.cu You said nvcc -shared -o matmul.dll matmul.cu

nvcc warning : Support for offline compilation for architectures prior to '<compute/sm/lto>_75' will be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).

matmul.cu

nvcc error : 'cudafe++' died with status 0xC0000005 (ACCESS_VIOLATION)

PS C:\www\dart\cuda-test\cuda>

Gemini said This is the same ACCESS_VIOLATION crash as before.

We have successfully diagnosed the root cause, and this error confirms it. Let's recap what we know:

Your CUDA Toolkit (12.9) and NVIDIA Driver (576.57) are compatible. This is good.

Your driver installation is faulty. We know this because nvidia-smi.exe was not in its proper place and the system paths were not set up.

The compiler (nvcc) is crashing because it relies on a correctly installed and integrated driver to function. Even though the driver files exist on your system, they are not properly registered for development tools to use.

The Only Remaining Solution: A Clean Installation #

Finding nvidia-smi was a diagnostic step to confirm the problem. The fix is to repair the installation itself.

You must perform the clean driver reinstallation we discussed earlier. This is no longer optional; it is the necessary step to fix the compiler crash.

Here is the plan again:

Download DDU (Display Driver Uninstaller) and the latest NVIDIA driver for your RTX 3060.

Disconnect from the internet.

Reboot into Safe Mode.

Run DDU to completely remove all NVIDIA software.

Reboot into normal Windows.

Run the NVIDIA driver installer you downloaded.

Choose Custom (Advanced) and check the box for "Perform a clean installation".

Restart your PC one final time.

After you complete this process, the nvcc command will work. The clean installation will properly register all the components that the compiler needs to communicate with the GPU.

You said i want to reinstall nvcc

Gemini said You don't reinstall nvcc by itself. nvcc is the compiler that comes bundled inside the NVIDIA CUDA Toolkit. To reinstall nvcc, you must reinstall the entire toolkit.

Given all the issues you've faced, you need to perform a clean reinstallation of both the NVIDIA Driver and the CUDA Toolkit to ensure they are installed correctly and can work together.

The Complete Reinstallation Plan 📝 #

Follow these steps in this exact order to fix the installation.

Step 1: Download Fresh Installers First, download the two installers you will need.

NVIDIA Driver: Go to the NVIDIA Driver Website and get the latest driver for your GeForce RTX 3060.

CUDA Toolkit: Download the same version you were using, CUDA Toolkit 12.9, from the NVIDIA Developer website.

Step 2: Completely Uninstall Everything 🧹 You must remove the old, corrupted installations first.

Uninstall the NVIDIA Driver: Use DDU (Display Driver Uninstaller) from Safe Mode, as we discussed before. This is the most important step for fixing the driver.

Uninstall the CUDA Toolkit:

Go to Windows "Add or remove programs".

Uninstall everything related to the "NVIDIA CUDA Toolkit 12.9". This might include several items like "NVIDIA Nsight", "NVIDIA PhysX", etc. Remove them all.

Step 3: Reinstall in the Correct Order After restarting your PC from the DDU cleanup, install the software in this order:

Install the NVIDIA Driver First:

Run the driver installer you downloaded.

Choose Custom (Advanced) installation.

Check the box for "Perform a clean installation".

Finish the installation and restart your PC.

Install the CUDA Toolkit Second:

Now, run the CUDA Toolkit 12.9 installer.

Use the "Express" installation. It will automatically detect your newly installed driver and set up all the components, including nvcc, correctly.

Step 4: Verify the Installation #

Once everything is finished and you have restarted your PC, open a new PowerShell or CMD terminal and run these two commands:

PowerShell nvidia-smi PowerShell nvcc --version Both commands should now work correctly without needing to specify a full path and without crashing. After this, your original command to compile matmul.cu will finally work.

You said maybe nvcc does not know the path of my driver

Gemini said You're exactly right. That is the core of the problem.

The nvcc compiler needs to communicate with the NVIDIA driver to get information about the GPU and to function correctly. When your driver installation is faulty, it isn't properly registered with the Windows operating system.

The "Unlisted Phone Number" Analogy 📞 #

Think of it this way:

A correctly installed driver is like having your name and number listed in the public phone book. When nvcc needs to "call" the driver, it looks it up in the public directory, finds it, and connects successfully.

Your current situation is like having a working phone, but your number is unlisted. Programs that already have your direct number (like PyTorch, which uses the basic runtime) can still call you. But nvcc is a tool that relies on the public phone book. It tries to look up the driver, can't find it, and crashes (ACCESS_VIOLATION).

The issue isn't just a simple PATH variable; it's a deeper system integration problem.

How the Reinstallation Fixes This #

The "Complete Reinstallation Plan" I provided is designed specifically to fix this.

Following those steps (using DDU and then a clean install) will force the driver to be installed correctly. It will properly "list its number in the public phone book," making it visible to the CUDA Toolkit and the nvcc compiler.

So yes, your intuition is spot on. Now, following the reinstallation plan is the necessary step to fix it.

You said let me just fix the path for the driver. i do not want to fix whats broken

Gemini said I understand. However, I must be very clear: fixing the PATH will not fix the nvcc compiler crash.

Fixing the PATH is only a convenience. It lets you type nvidia-smi instead of the full file path. It does not solve the deeper system registration issue that is causing nvcc to fail.

Using our analogy, adding the driver to the PATH is like creating a personal shortcut on your phone's home screen. It helps you find the number faster, but it still doesn't list the number in the public phone book for others (like nvcc) to find.

If you still wish to proceed, here is how you can add the driver's location to your system PATH.

How to Add the Driver to Your PATH #

This will let you run nvidia-smi from any terminal. It will not fix the nvcc compiler.

Copy the Path: The path you found for the driver is C:\Windows\System32\DriverStore\FileRepository\nvmiui.inf_amd64_727fe9a457c47c84. Copy this full string.

Edit Environment Variables:

Press the Windows Key, type env, and select "Edit the system environment variables".

In the window that opens, click the "Environment Variables..." button.

Update the Path:

In the bottom section ("System variables"), find and double-click on Path.

Click "New" and paste the driver path you copied.

Click "OK" on all windows to save.

Restart Your Terminal: Close and reopen PowerShell or CMD. Now, when you type nvidia-smi, it should work.

The Bottom Line #

After doing this, the nvidia-smi command will be convenient to use, but when you try to compile your code again with nvcc -shared -o matmul.dll matmul.cu, it will still crash with the ACCESS_VIOLATION error.

The only way to fix the compiler is to fix the broken installation with the clean reinstallation plan.

You said try something else

Gemini said Of course. Let's try a completely different approach.

Instead of trying to fix the nvcc compiler, we can use a different method that bypasses the need to compile your own CUDA code entirely. This is a standard industry practice for running models in production.

We'll use a workflow that leverages your working Python environment.

Alternative Plan: Use the ONNX Runtime #

The idea is to use Python/PyTorch (which you said works fine) to define your GPU operations, save them in a universal format called ONNX, and then use a pre-compiled, highly optimized library called the ONNX Runtime to run it from Dart.

The key advantage: You don't need nvcc or the CUDA Toolkit to be working correctly on your Dart machine. You only need the NVIDIA driver.

The New Workflow (Python → ONNX → Dart) #

Step 1: Create Your Operation in Python In Python, create a simple script to define your matrix multiplication and export it.

Make sure you have onnx installed: pip install onnx

Create a file named export_model.py:

export_model.py

Python import torch

Define a simple "model" that just does matrix multiplication #

class MatMulModel(torch.nn.Module): def forward(self, a, b): return torch.matmul(a, b)

--- Configuration --- #

The device to use for exporting (your GPU) #

device = 'cuda'

Define the dimensions for sample inputs: A(M x K) * B(K x N) #

M, K, N = 4, 3, 4

--- Create Model and Dummy Inputs --- #

model = MatMulModel().to(device) model.eval() # Set the model to inference mode

Create some dummy input tensors on the GPU to define the input shapes #

dummy_a = torch.randn(M, K, device=device) dummy_b = torch.randn(K, N, device=device)

--- Export to ONNX --- #

print("Exporting model to ONNX format...") torch.onnx.export( model, (dummy_a, dummy_b), "matmul.onnx", # The output file name input_names=['A', 'B'], output_names=['C'], opset_version=14 ) print("Export complete. File saved as matmul.onnx")

Run this script: python export_model.py. It will create a file named matmul.onnx. This file contains your GPU-accelerated matrix multiplication operation.

Step 2: Download the Pre-Compiled ONNX Runtime You don't compile anything yourself. You just download the ready-to-use library.

Go to the ONNX Runtime GitHub Releases page.

Find the latest release.

In the "Assets" section, download the file for Windows x64 with CUDA support. It will be a zip file with a name like onnxruntime-win-x64-gpu-1.xx.x.zip.

Inside this zip file, you will find the onnxruntime.dll and other necessary files. This is the library you will call from Dart.

Step 3: Call the ONNX Runtime from Dart FFI Now, your Dart code will not call your own matmul.dll. Instead, it will call onnxruntime.dll to load and run the matmul.onnx file.

The ONNX Runtime has a stable C API that is perfect for FFI. The Dart code is more involved than our simple example, but the logic is as follows:

Load onnxruntime.dll using DynamicLibrary.open().

Create an ONNX session: Call the C function CreateSession, passing the path to matmul.onnx.

Prepare input tensors: Create Dart Pointer objects for your A and B matrices, just like before.

Run inference: Call the C function Run, which takes your session and input pointers and executes the model on the GPU.

Get the result: The Run function gives you a pointer to the output tensor C, which you can then read in Dart.

This approach completely sidesteps your broken compiler. It separates the "development" part (Python) from the "deployment" part (Dart) and uses a professional, pre-built inference engine to run your operations.

You said how do i access directx imgaign from dart

Gemini said You can't access DirectX APIs directly from Dart. Instead, you must use Dart's FFI (Foreign Function Interface) to call a native C++ library (a .dll file) that you create. This C++ library will handle all the DirectX imaging operations.

Think of the C++ code as a translator that Dart can talk to, and the C++ code then talks to DirectX on Dart's behalf.

Here's the complete workflow.

Step 1: Write the C++ DirectX Code 🖥️ #

This is where you'll use the Windows and DirectX APIs to capture an image. A common use case is capturing the screen using the Desktop Duplication API.

You'll create a C++ file (e.g., dx_capture.cpp) that contains your DirectX logic and, most importantly, a simple extern "C" function that Dart can call.

Here’s a simplified conceptual example. Note that real DirectX code is much more verbose with extensive error handling and setup.

dx_capture.cpp

C++ #include <d3d11.h> #include <dxgi1_2.h> #include

// Link against necessary DirectX libraries #pragma comment(lib, "d3d11.lib") #pragma comment(lib, "dxgi.lib")

// This is the C-style function that Dart FFI will call. // __declspec(dllexport) makes it visible outside the DLL. extern "C" __declspec(dllexport) bool capture_screen( unsigned char** out_buffer, int* out_width, int* out_height ) { // --- Simplified DirectX 11 Desktop Duplication --- // In a real application, this involves: // 1. Creating a D3D11 Device and Context. // 2. Finding the DXGI Adapter and Output. // 3. Initializing the Desktop Duplication interface (IDXGIOutputDuplication). // 4. Calling AcquireNextFrame() to get a frame. // 5. Creating a "staging" texture with CPU access. // 6. Copying the GPU texture to the staging texture. // 7. Mapping the staging texture to get a pointer to the raw pixel data.

// For this example, let's pretend we did all that and got pixel data.
*out_width = 1920;
*out_height = 1080;
int buffer_size = *out_width * *out_height * 4; // BGRA format (4 bytes per pixel)

// Allocate memory for the buffer that will be sent back to Dart.
// Dart will be responsible for freeing this memory.
*out_buffer = (unsigned char*)malloc(buffer_size);
if (!*out_buffer) { return false; }

// TODO: In a real implementation, you would copy the actual
// pixel data from the mapped DirectX texture into *out_buffer here.
// For now, we'll just fill it with a test color (e.g., magenta).
for (int i = 0; i < buffer_size; i += 4) {
    (*out_buffer)[i] = 255;     // Blue
    (*out_buffer)[i + 1] = 0;   // Green
    (*out_buffer)[i + 2] = 255; // Red
    (*out_buffer)[i + 3] = 255; // Alpha
}

// Unmap the texture and release the frame in a real app.

return true;

}

// Dart FFI needs a function to free the memory we allocated in C++. extern "C" __declspec(dllexport) void free_buffer(unsigned char* buffer) { free(buffer); }

Step 2: Compile the C++ into a DLL ⚙️ #

You'll need Visual Studio's C++ build tools installed. You can compile the code from a "Developer Command Prompt for VS".

PowerShell cl.exe /LD dx_capture.cpp /o dx_capture.dll This command compiles your C++ file into a Dynamic Link Library (DLL) named dx_capture.dll. The /LD flag tells the compiler to create a DLL.

Step 3: Call the DLL from Dart using FFI 🎯 #

Now, you can write a Dart script to load dx_capture.dll and call your capture_screen function.

main.dart

Dart import 'dart:ffi'; import 'package:ffi/ffi.dart'; import 'dart:typed_data';

// FFI signature for the C++ capture function typedef CaptureScreenNative = Bool Function( Pointer<Pointer

// Dart-friendly signature typedef CaptureScreen = bool Function( Pointer<Pointer

// FFI signature for the C++ free function typedef FreeBufferNative = Void Function(Pointer

void main() { final dylib = DynamicLibrary.open('dx_capture.dll');

final captureScreen = dylib.lookupFunction<CaptureScreenNative, CaptureScreen>('capture_screen');

final freeBuffer = dylib.lookupFunction<FreeBufferNative, FreeBuffer>('free_buffer');

// Allocate pointers in Dart memory to receive the output values from C++ final pBuffer = calloc<Pointer

print('Calling native DirectX capture function...'); final success = captureScreen(pBuffer, pWidth, pHeight);

if (success) { final imageBufferPtr = pBuffer.value; final width = pWidth.value; final height = pHeight.value;

print('Capture successful!');
print('Dimensions: $width x $height');
print('Image buffer address: ${imageBufferPtr.address}');

// Access the image data as a Uint8List
final imageBytes = imageBufferPtr.asTypedList(width * height * 4);

// Now you can use the 'imageBytes'
// For example, check the color of the first pixel (BGRA)
print('First pixel (BGRA): ${imageBytes.sublist(0, 4)}');

// VERY IMPORTANT: Free the memory that was allocated in C++
freeBuffer(imageBufferPtr);
print('C++ buffer freed.');

} else { print('Capture failed.'); }

// Free the pointers allocated by Dart calloc.free(pBuffer); calloc.free(pWidth); calloc.free(pHeight); }

Handling the Image Data in Dart #

Once you have the imageBytes (Uint8List), you have the raw pixel data. If you're using Flutter, you can easily convert this into a displayable image:

Dart // Flutter-specific code import 'dart:ui' as ui;

// ... inside an async function where you have the 'imageBytes', 'width', and 'height' // Note: DirectX provides BGRA, but ui.decodeImageFromPixels often expects RGBA. // You might need to swizzle the bytes (swap the R and B channels). ui.decodeImageFromPixels( imageBytes, width, height, ui.PixelFormat.bgra8888, // Use bgra8888 since that's what we have (ui.Image image) { // Now you can use this 'image' object in a CustomPaint widget // to display it on the screen. }, ); You said let me try wsl

Gemini said That is an excellent idea. Trying WSL (Windows Subsystem for Linux) is the best possible next step.

It will give you a clean, standard Linux development environment, which completely sidesteps all the complex Windows-specific driver integration and compiler issues we've been fighting. Your experience will be much smoother.

The New Workflow in WSL #

Here's the plan to get your project working inside WSL.

Step 1: Set Up WSL and CUDA 🐧 First, you need to install WSL and get CUDA working inside it.

Install WSL: If you haven't already, open PowerShell as an Administrator and run:

PowerShell wsl --install This will install WSL and the default Ubuntu distribution. Restart your PC when prompted.

Install the NVIDIA Driver on Windows: You've already done this. WSL uses your existing Windows NVIDIA driver, so you're all set here.

Install the CUDA Toolkit inside WSL: This is the most important part. Do not install the regular Linux toolkit. NVIDIA provides a specific toolkit for WSL. Follow their official guide carefully.

Official Guide: NVIDIA's CUDA on WSL User Guide

In your Ubuntu terminal inside WSL, you'll run commands like wget and sudo apt-get install to add NVIDIA's repository and install the cuda-toolkit-12-x package.

After this is done, you should be able to run nvcc --version and nvidia-smi inside your WSL/Ubuntu terminal without any issues.

Step 2: Compile Your CUDA Code (in WSL) Copy your matmul.cu file into your WSL filesystem (e.g., into your WSL home directory ~).

Your C++ code is almost perfect, but we need to use the Linux equivalent of __declspec(dllexport). The platform-specific macro we wrote earlier will handle this automatically. The code I provided previously should work without changes.

Compile it into a shared object (.so) file, which is the Linux version of a .dll. Open your WSL/Ubuntu terminal and run:

Bash nvcc -shared -fPIC -o libmatmul.so matmul.cu This will create libmatmul.so, your compiled library for Linux.

Step 3: Run Your Dart Code (in WSL) Install the Dart SDK inside WSL: You'll need to install Dart within your Ubuntu environment. Follow the official instructions for installing Dart on Linux.

Bash sudo apt-get update sudo apt-get install apt-transport-https wget -qO- https://dl-ssl.google.com/linux/linux_signing_key.pub | sudo gpg --dearmor -o /usr/share/keyrings/dart.gpg echo 'deb [signed-by=/usr/share/keyrings/dart.gpg arch=amd64] https://storage.googleapis.com/download.dartlang.org/linux/debian stable main' | sudo tee /etc/apt/sources.list.d/dart_stable.list sudo apt-get update sudo apt-get install dart Modify Your Dart Code: Make one small change to main.dart to load the .so file instead of the .dll file.

Dart // Change this line final dylib = DynamicLibrary.open('matmul.dll');

// To this final dylib = DynamicLibrary.open('./libmatmul.so'); // Use the Linux library name Run it: From your WSL terminal, in the same directory as your files, run your Dart code:

Bash dart run main.dart

Summary of Advantages #

No More Driver Headaches: WSL's integration with the NVIDIA driver is designed to work out-of-the-box.

Standard Tooling: You'll be using the standard, well-tested Linux compilation tools and commands.

Clean Environment: It provides a fresh start, free from any of the corrupted installation issues on Windows.

This is a much more stable and straightforward path. You should have a much better experience.

You said now give me the matmul code that can run wsl -ubuntu

Gemini said Of course. The great news is that the code I provided earlier is already designed to be cross-platform. No changes are needed for it to work in WSL-Ubuntu.

It uses a preprocessor macro that automatically detects the operating system and applies the correct syntax for exporting the function. When you compile it in WSL, it will use the Linux-specific attribute syntax.

CUDA Code for WSL-Ubuntu #

Here is the complete code again. You can create a file named matmul.cu inside your WSL environment and paste this directly into it.

matmul.cu

C++ #include

// This macro automatically chooses the correct syntax. // In WSL (Linux), it will use the attribute... part. #if defined(_WIN32) || defined(_WIN64) #define DLLEXPORT __declspec(dllexport) #else #define DLLEXPORT attribute((visibility("default"))) #endif

/**

@brief CUDA kernel for matrix multiplication (C = A * B). / global void matrixMulKernel(float A, float* B, float* C, int M, int N, int K) { int row = blockIdx.y * blockDim.y + threadIdx.y; int col = blockIdx.x * blockDim.x + threadIdx.x;

if (row < M && col < N) { float C_value = 0.0; for (int i = 0; i < K; ++i) { C_value += A[row * K + i] * B[i * N + col]; } C[row * M + col] = C_value; } }

/**

@brief C-style wrapper function that Dart FFI will call. / extern "C" DLLEXPORT void matrix_multiply_cuda( float host_A, float* host_B, float* host_C, int M, int N, int K ) { float *dev_A, *dev_B, *dev_C;

int size_A = M * K * sizeof(float); int size_B = K * N * sizeof(float); int size_C = M * N * sizeof(float);

// 1. Allocate GPU memory cudaMalloc(&dev_A, size_A); cudaMalloc(&dev_B, size_B); cudaMalloc(&dev_C, size_C);

// 2. Copy data from CPU to GPU cudaMemcpy(dev_A, host_A, size_A, cudaMemcpyHostToDevice); cudaMemcpy(dev_B, host_B, size_B, cudaMemcpyHostToDevice);

// 3. Configure kernel launch dim3 threadsPerBlock(16, 16); dim3 numBlocks((N + 15) / 16, (M + 15) / 16);

printf("Launching kernel...\n"); // 4. Launch kernel matrixMulKernel<<<numBlocks, threadsPerBlock>>>(dev_A, dev_B, dev_C, M, N, K);

cudaDeviceSynchronize(); printf("Kernel finished.\n");

// 5. Copy result from GPU to CPU cudaMemcpy(host_C, dev_C, size_C, cudaMemcpyDeviceToHost);

// 6. Free GPU memory cudaFree(dev_A); cudaFree(dev_B); cudaFree(dev_C); }

Next Step: Compile It #

After saving this file in your WSL environment, you can compile it using the Linux command:

Bash nvcc -shared -fPIC -o libmatmul.so matmul.cu You said nvcc -shared -fPIC -o libmatmul.so matmul.cu

nvcc fatal : Unknown option '-fPIC'

kkinyama@KKINYAMA-ISD-MA:/mnt/c/Users/kkinyama/cuda_test$

Gemini said That is a very surprising error, but it has a clear cause.

The -fPIC (Position-Independent Code) flag is a standard and essential option for creating shared libraries (.so files) on Linux. The error Unknown option '-fPIC' almost always means that nvcc cannot find or properly use the host C++ compiler (g++) that should be installed on your system.

The Explanation: How nvcc Works #

nvcc is a "compiler driver." When you give it a .cu file, it does two things:

It compiles the GPU-specific code (global functions).

It takes the normal C++ host code (your matrix_multiply_cuda wrapper function) and passes it to a standard C++ compiler like g++ to be compiled.

The -fPIC flag is meant for g++, not for nvcc itself. The error indicates that nvcc is not set up correctly to pass this flag to g++, most likely because the C++ toolchain is missing.

The Solution: Install the C++ Build Tools #

You need to install the core C++ development tools, including g++, inside your WSL-Ubuntu environment. The easiest way to do this is by installing the build-essential package.

Install build-essential: Open your WSL terminal and run the following command:

Bash sudo apt-get update sudo apt-get install build-essential This will download and install g++, gcc, make, and other essential tools that nvcc depends on.

Verify the Installation (Optional): After it's finished, you can verify that g++ is installed by running:

Bash g++ --version You should see output describing the g++ version.

Retry the Compilation: Now, run your original nvcc command again. It should be able to find and use g++ correctly, and it will now understand what to do with the -fPIC flag.

Bash nvcc -shared -fPIC -o libmatmul.so matmul.cu

Pro Tip: Work in the Linux Filesystem #

I notice your current path is /mnt/c/.... You are working from a mounted Windows directory. While this often works, it can sometimes lead to strange permission or performance issues.

It's generally safer and faster to work directly within the WSL/Linux filesystem.

Navigate to your Linux home directory:

Bash cd ~ Create a project folder and copy your files there:

Bash mkdir cuda_project cd cuda_project cp /mnt/c/Users/kkinyama/cuda_test/matmul.cu . Then run the nvcc command from there.

For now, just installing build-essential should solve your immediate problem.

You said again: nvcc -shared -fPIC -o libmatmul.so matmul.cu

nvcc fatal : Unknown option '-fPIC'

kkinyama@KKINYAMA-ISD-MA:/mnt/c/Users/kkinyama/cuda_test$

Gemini said Okay, since installing build-essential didn't solve the problem, it means the issue is with how the CUDA Toolkit itself is configured or installed within WSL. nvcc is still not correctly processing flags for the host compiler.

Let's try one more command. This version is more explicit and can often fix this exact problem.

The More Explicit Command #

We will use the -Xcompiler flag. This flag tells nvcc "don't try to understand the next option yourself, just pass it directly to the host compiler (g++)".

Try compiling with this command instead:

Bash nvcc -shared -Xcompiler -fPIC -o libmatmul.so matmul.cu There's a very good chance this will work. It forces nvcc to hand off the -fPIC flag to g++, which knows what to do with it.

If That Still Fails: Reinstall the CUDA Toolkit #

If the -Xcompiler command also fails with the same error, then your CUDA Toolkit installation inside WSL is fundamentally broken. The only definitive fix is to perform a clean reinstallation inside WSL.

Completely Purge the Old Toolkit: Run these commands to remove all traces of the previous CUDA installation from Ubuntu.

Bash sudo apt-get --purge remove 'cuda*' 'nsight*' 'nvidia*' sudo apt-get autoremove sudo apt-get clean sudo rm -rf /usr/local/cuda* Carefully Reinstall: Follow the official NVIDIA guide for WSL step-by-step. Do not use any other guide.

Official Guide: https://docs.nvidia.com/cuda/wsl-user-guide/index.html

This involves setting up the repository correctly and then running the sudo apt-get install cuda-toolkit-12-x command (or whichever version the guide specifies).

A clean installation following the official guide will resolve these toolchain issues. But first, try the command with the -Xcompiler flag.

You said thanks. it has worked! kkinyama@KKINYAMA-ISD-MA:/mnt/c/Users/kkinyama/cuda_test$ nvcc -shared -Xcompiler -fPIC -o libmatmul.so matmul.cu

kkinyama@KKINYAMA-ISD-MA:/mnt/c/Users/kkinyama/cuda_test$ ll

total 816

drwxrwxrwx 1 root root 4096 Sep 11 14:20 ./

drwxrwxrwx 1 root root 4096 Sep 11 14:15 ../

-rwxrwxrwx 1 root root 827864 Sep 11 14:20 libmatmul.so*

-rwxrwxrwx 1 root root 1943 Sep 11 14:15 matmul.cu*

kkinyama@KKINYAMA-ISD-MA:/mnt/c/Users/kkinyama/cuda_test$

Gemini said Excellent! That's fantastic news. I'm very glad to hear it worked.

You've successfully compiled your custom CUDA code into a shared library (libmatmul.so) inside the WSL environment. The -Xcompiler flag was the key to forcing nvcc to work with your system's C++ compiler correctly.

The output from ll confirms that you have your libmatmul.so file ready to go.

Final Step: Run it with Dart #

You are now at the finish line. The last step is to run the Dart code that calls this new library.

Install Dart in WSL (if you haven't yet):

Bash sudo apt-get update sudo apt-get install dart Create/Update Your Dart File: Make sure you have a main.dart file in that same directory. The code needs to load the correct library name (libmatmul.so). Here is the complete Dart code for convenience.

main.dart

Dart import 'dart:ffi'; import 'package:ffi/ffi.dart';

// C function signature typedef MatMulFunc = Void Function( Pointer

// Dart function signature typedef MatMul = void Function( Pointer

void main() { // Define matrix dimensions: A(M x K) * B(K x N) = C(M x N) const M = 4; const K = 3; const N = 4;

// --- IMPORTANT CHANGE --- // Load the Linux shared object (.so) file final dylib = DynamicLibrary.open('./libmatmul.so');

// Look up the function by its C name final matMulCuda = dylib.lookupFunction<MatMulFunc, MatMul>('matrix_multiply_cuda');

// Allocate memory for the matrices final Pointer

// Populate the input matrices final matrixA = hostA.asTypedList(M * K); final matrixB = hostB.asTypedList(K * N);

matrixA.setAll(0, [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 1, 1]); matrixB.setAll(0, [9, 8, 7, 6, 5, 4, 3, 2, 1, 2, 3, 4]);

print('Matrix A:'); printMatrix(hostA, M, K); print('Matrix B:'); printMatrix(hostB, K, N);

// Call the CUDA function print('\nCalling CUDA function...'); matMulCuda(hostA, hostB, hostC, M, N, K); print('CUDA function returned.');

// Print the result print('\nResult Matrix C:'); printMatrix(hostC, M, N);

// Free the memory calloc.free(hostA); calloc.free(hostB); calloc.free(hostC); }

/// Helper function to print a matrix void printMatrix(Pointer

Bash dart run main.dart You should now see the output from your CUDA code, executed on your GPU, right in your WSL terminal. Congratulations on getting it all working!

You said $ sudo apt-get install dart

Reading package lists... Done

Building dependency tree... Done

Reading state information... Done

E: Unable to locate package dart

kkinyama@KKINYAMA-ISD-MA:/mnt/c/Users/kkinyama/cuda_test$

Gemini said That error means your system's package manager (apt) doesn't know where to find the Dart SDK. This is normal because Dart isn't included in Ubuntu's default software sources.

You need to first tell your system where to find Google's official Dart repository.

The Solution: Add the Dart Repository #

Here are the commands to set up the repository and then install Dart. You can run them one by one in your WSL terminal.

Update your package list and install prerequisites:

Bash sudo apt-get update sudo apt-get install apt-transport-https Get Google's signing key to verify the packages:

Bash wget -qO- https://dl-ssl.google.com/linux/linux_signing_key.pub | sudo gpg --dearmor -o /usr/share/keyrings/dart.gpg Add the Dart repository to your system's sources:

Bash echo 'deb [signed-by=/usr/share/keyrings/dart.gpg arch=amd64] https://storage.googleapis.com/download.dartlang.org/linux/debian stable main' | sudo tee /etc/apt/sources.list.d/dart_stable.list Update your package list again to include the new Dart packages:

Bash sudo apt-get update Now, install the Dart SDK: This command will now work.

Bash sudo apt-get install dart After completing these steps, the Dart SDK will be installed correctly. You can verify it by running dart --version, and then you'll be able to run your project with dart run main.dart.

You said now give me the code for dart to call the .so library and do some matmul

Gemini said Of course. Here is the complete Dart code to load your libmatmul.so library, perform the matrix multiplication on the GPU, and print the result.

Dart Code for main.dart #

Create a file named main.dart in the same directory as your libmatmul.so file inside your WSL environment. Copy and paste the following code into it.

Dart import 'dart:ffi'; import 'package:ffi/ffi.dart';

// 1. Define the FFI function signatures to match the C/C++ wrapper.

// This is the native C function signature. typedef MatMulFunc = Void Function( Pointer

// This is the Dart-friendly version of the function. typedef MatMul = void Function( Pointer

void main() { // Define matrix dimensions: A(M x K) * B(K x N) = C(M x N) const M = 4; const K = 3; const N = 4;

// 2. Load the compiled .so library from the current directory. final dylib = DynamicLibrary.open('./libmatmul.so');