Deep Learning GPU Performance

Understanding how to maximize GPU utilization when training deep neural networks is key to reducing time‑to‑insight. Below we discuss best practices, tooling, and benchmark results across popular frameworks.

Key Factors

• Batch size selection – larger batches increase GPU throughput but may affect convergence.
• Mixed‑precision training – FP16 can double throughput on modern GPUs.
• Data pipeline efficiency – prefetching, caching, and parallel data loading.
• Kernel fusion – reducing kernel launch overhead.

Sample Benchmark (PyTorch)

#!/usr/bin/env python
import torch, time

model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=False).cuda()
input = torch.randn(64, 3, 224, 224).cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

def train_step():
    optimizer.zero_grad()
    output = model(input)
    loss = output.mean()
    loss.backward()
    optimizer.step()

# Warm‑up
for _ in range(10):
    train_step()

# Timing
start = time.time()
for _ in range(100):
    train_step()
print(f"Throughput: {64*100/(time.time()-start):.2f} images/sec")

Visualization

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