Bare Metal GPUs

import torch
import torch.distributed as dist

# Initialize distributed training
dist.init_process_group(backend='nccl')

# Create model and move to device
model = YourModel().to(device)
model = torch.nn.parallel.DistributedDataParallel(model)

# Multi-GPU training loop
for epoch in range(num_epochs):
    for batch in dataloader:
        inputs, targets = batch
        inputs, targets = inputs.to(device), targets.to(device)

        outputs = model(inputs)
        loss = criterion(outputs, targets)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

// Example: GPU-accelerated Black-Scholes model
__global__ void blackScholesGPU(float *callPrices, float *spotPrices,
                                float *strikePrices, float *volatilities,
                                float *timesToMaturity, float riskFreeRate,
                                int numOptions) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < numOptions) {
        float S = spotPrices[i];
        float K = strikePrices[i];
        float sigma = volatilities[i];
        float T = timesToMaturity[i];
        float r = riskFreeRate;

        float d1 = (logf(S/K) + (r + 0.5f*sigma*sigma)*T) / (sigma*sqrtf(T));
        float d2 = d1 - sigma*sqrtf(T);

        callPrices[i] = S * cndf(d1) - K * expf(-r*T) * cndf(d2);
    }
}

import bpy
import GPUBatch

# Configure GPU rendering in Blender
bpy.context.scene.render.engine = 'CYCLES'
bpy.context.preferences.addons['cycles'].preferences.compute_device_type = 'CUDA'

# Enable all available GPUs for rendering
for device in bpy.context.preferences.addons['cycles'].preferences.devices:
    device.use = True

bpy.context.scene.cycles.device = 'GPU'

bpy.context.scene.render.resolution_x = 7680  # 8K resolution
bpy.context.scene.render.resolution_y = 4320
bpy.context.scene.cycles.samples = 2048

bpy.ops.render.render(write_still=True)