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Using several GPUS with OpenNL in Ethereum
As a developer who works on the Ethereum network, you are probably familiar with the importance of optimizing the performance of the Ethereum virtual machine (EVM). An efficient way to achieve this is to leverage several graphic processing units (GPUS) using the OpenNL. In this article, we will explore how to use multiple GPUs with OpenNL in Blockchain Ethereum.
Bottom
OpenNL is a programming interface that allows developers to access the computational resources of a multi -core CPU or GPU, allowing parallel processing and improving the overall system performance. Ethereum’s EVM uses the Webgpu API, which supports OpenNL through its native Oclan 'driver.
Configuring several gpus with opencl
To use multiple GPUS with OpenNL on Ethereum, you will need:
- Install a compatible graphics card : Make sure your GPU is supported by the WebGPU API and has sufficient computing features. Popular options for Ethereum developers include the GeForce series and Nvidia board.
- Set the OpenNL : Update your OpenNL driver to version 2.0 or later, which supports multi-GPU support. You can download the latest OpenNL driver from the official Intel website or the AMD website.
- Create multiple contexts : Create several openclio contexts for each GPU using theOclan :: Context
class. This will allow you to perform different kernels on each GPU.
- Set Kernel Release Parameters : Specify additional initiation parameters, such as the number of block chains and shared memory size to optimize performance.
Example Code: Using multiple gpus with OpenNL
Here is an example of code code by demonstrating how to use multiple GPUS with OpenNL:
CPP
#include
#include
// Initialize the API Webgpu
GPU :: GPU Webgpus [2]; // 2 gpus, including what we will configure
// Create several contexts for each GPU
GPUCONTEXT* CTX1 = Webgpus [0] .Create ();
GPUCONTEXT* CTX2 = Webgpus [1] .Create ();
// launch the kernel on CTX1
int main () {
// Define a simple OpenNL kernel (eg matrix multiplication)
Kernel Matmul;
matmul.setkernelnam (“matmul_kernel”);
Matmul.setLanchparams (
{128, 256}, // number of threads per block, shared memory size
32, // number of threads per thread
{1.0f, 2.0f} // width and urgent depth
);
// launch the kernel on CTX1
Matmul.Launch (CTX1);
// Wait for the kernel to finish running
matmul.wait ();
Return 0;
}
`
Tips and Considerations
- When using multiple GPUs with OpenNL, you may need to adjust the initiation parameters to optimize performance. You can do this by creating a “Launchparams” object that defines the specific release requirements for each GPU.
- Remember that several GPUS cannot always be available or stable (for example, alternating between different displays). Therefore, make sure your configuration is robust and resilient.
- Using multiple GPUS with OpenNL can also introduce additional general complexity and general expenses. Be aware of the use of resources and performance trade-offs.
Conclusion
Using multiple GPUs with OpenNL on Ethereum offers a promising way to optimize system performance for the EVM. By leveraging webgpu multi-GPU support, you can create more efficient kernels that take advantage of GPU individual features. However, it is essential to carefully configure your setting to ensure ideal performance and reliability.
Additional resources
- Intel OpenNL documentation: [Intel OpenNL] (
- API documentation of AMD Webgpu: [AMD Webgpu API] (
- Ethereum EVM Webgpu Documentation: [Ethereum EVM Webgpu] (
Thanks for reading! Do you have any questions or would you like to discuss more?
