Ethereum: Use multiple GPU’s with oclvanitygen

Using Multiple GPUs with OpenCL on Ethereum

As a developer working on the Ethereum network, you are probably familiar with the importance of optimizing performance for the Ethereum Virtual Machine (EVM). One effective way to achieve this is to use multiple graphics processing units (GPUs) using OpenCL. In this article, we will explore how to use multiple GPUs with OpenCL on the Ethereum blockchain.

Background

OpenCL is a programming interface that allows developers to access the computing resources of a multi-core CPU or GPU, enabling parallel processing and improving overall system performance. Ethereum’s EVM uses the WebGPU API, which supports OpenCL through the native oclan driver.

Setting Up Multiple GPUs with OpenCL

To use multiple GPUs with OpenCL on Ethereum, you will need to:

• Install a compatible graphics card

  : Make sure your GPU supports the WebGPU API and has sufficient compute capabilities. Popular options for Ethereum developers include the NVIDIA GeForce and Quadro series.

• Configure OpenCL Driver: Update your OpenCL driver to version 2.0 or later, which supports multi-GPU support. You can download the latest OpenCL driver from the official Intel website or AMD website.

• Create multiple contexts: Create multiple OpenCL contexts for each GPU using the oclan::Context class. This will allow you to run different kernels on each GPU.

• Configure kernel startup parameters: Specify additional startup parameters, such as the number of threads per block and the size of shared memory, to optimize performance.

Example Code: Using Multiple GPUs with OpenCL

Here’s an example code snippet that shows how to use multiple GPUs with OpenCL:

#include 

#include 


// Initialize the WebGPU API

Gpu::Gpu webgpus[2]; // 2 GPUs, including the one we will configure


// Create multiple contexts for each GPU

GpuContext* ctx1 = webgpus[0].create();

GpuContext* ctx2 = webgpus[1].create();


// Start the kernel on ctx1

int main() {

    // Define a simple OpenCL kernel (eg matrix multiplication)

    Kernel matmul;

    matmul.setKernelName("matmul_kernel");

    matmul.setLaunchParams(
{128, 256}, // number of threads per block, shared memory size
32, // number of threads per thread
{1.0f, 2.0f} // warp width and depth
);
// Launch kernel on ctx1
matmul.launch(ctx1);
// Wait for kernel to finish executing
matmul.wait();
return 0;
}

Tips and Considerations

• When using multiple GPUs with OpenCL, you may need to adjust launch parameters to optimize performance. You can do this by creating a LaunchParams object that defines specific launch requirements for each GPU.

• Please note that multiple GPUs may not always be available or stable (e.g. when switching between different displays). So make sure your installation is robust and resilient.

• Using multiple GPUs with OpenCL can also introduce additional complexity and cost. Be aware of resource usage and performance tradeoffs.

Conclusion

Using multiple GPUs with OpenCL on Ethereum offers a promising way to optimize system performance for EVM. By leveraging WebGPU’s multi-GPU support, you can create more efficient kernels that take advantage of the individual GPU capabilities. However, it is important to configure your settings carefully to ensure optimal performance and reliability.

Additional Resources

• Intel OpenCL documentation: [Intel OpenCL](

• AMD WebGPU API documentation: [AMD WebGPU API](

• Ethereum EVM WebGPU documentation: [Ethereum EVM WebGPU](

Thank you for reading! Do you have any questions or would you like to discuss further?