person Tute World Team

GPU Architecture & CUDA — How GPUs Run AI Code

GPUs accelerate AI training by thousands of times compared to CPUs. Understanding why requires knowing how GPUs work — the hierarchy of threads, the memory system, and the CUDA programming model. This knowledge helps you write faster PyTorch code and debug performance bottlenecks.

⚡ Covers: GPU vs CPU · SM · Warp · Thread Block · Grid · CUDA Model · Memory Hierarchy · PyTorch CUDA API · Profiling

GPU vs CPU — Fundamentally Different

🖥️ CPU (e.g., AMD Ryzen 9)

  • 8–32 powerful cores
  • Optimised for sequential tasks
  • Large cache (32MB+), complex branch prediction
  • Low latency per operation
  • Good at control flow and general-purpose logic

🎮 GPU (e.g., NVIDIA A100)

  • 6,912 CUDA cores (A100)
  • Optimised for massively parallel tasks
  • Small cache per core, simpler execution
  • High throughput (hides latency with parallelism)
  • Perfect for matrix multiplications, convolutions

Matrix multiply of 4096×4096 matrices requires ~134M multiply-add operations. A CPU does this sequentially in seconds. A GPU does all operations in parallel in milliseconds. Every layer forward/backward pass is a matrix multiply.

GPU Thread Hierarchy

🧵 Thread Visualiser — Click a Level

GPUs organize computation in a 3-level hierarchy. Click each level to understand it.

Thread: The smallest unit of execution. Each thread runs the same kernel code with a unique thread ID. Threads in a block can share memory and synchronize.

The Thread Hierarchy Explained

🧵
Thread — The smallest execution unit. Each thread runs the kernel function independently with its unique (blockIdx, threadIdx). On A100: up to 2048 threads can be active per SM at once.
🌀
Warp — 32 threads that execute the same instruction simultaneously (SIMT — Single Instruction, Multiple Thread). If threads in a warp take different branches (if/else), the warp executes both paths serially — avoid divergence!
📦
Thread Block — A group of threads (up to 1024) that run on the same Streaming Multiprocessor (SM). Threads in a block share fast shared memory (SRAM) and can synchronize with __syncthreads(). Choose block sizes that are multiples of 32 (warp size).
🌐
Grid — All thread blocks for a kernel launch. Blocks are assigned to SMs dynamically — a GPU with 108 SMs (A100) processes 108 blocks simultaneously. Grids can be 1D, 2D, or 3D to match your data shape.

GPU Memory Hierarchy

Registers

~1 cycle latency

Private to each thread. Fastest but limited. If a kernel uses too many registers, it "spills" to slower local memory.

Shared Memory (SRAM)

~5 cycle latency · 192KB/SM

Shared within a thread block. Programmable L1 cache. Key for optimised CUDA kernels — load data once from DRAM, reuse from shared memory.

L2 Cache

~50 cycle latency · 40MB (A100)

Shared across all SMs. Caches frequently accessed global memory. PyTorch's memory manager tries to reuse allocations to benefit from L2 hits.

Global Memory (HBM)

~500 cycle latency · 80GB (A100)

Your model weights, activations, gradients live here. HBM2e provides 2 TB/s bandwidth. Most AI training is memory bandwidth-bound, not compute-bound.

PyTorch CUDA API

Python · Essential PyTorch CUDA Operations
import torch

# Device management
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(torch.cuda.device_count())           # Number of GPUs
print(torch.cuda.get_device_name(0))       # "NVIDIA A100 80GB PCIe"
print(torch.cuda.get_device_properties(0)) # Full specs

# Memory management
print(torch.cuda.memory_allocated(0))      # Bytes in use
print(torch.cuda.memory_reserved(0))       # Bytes allocated (including cache)
torch.cuda.empty_cache()                   # Release cached memory back to OS

# Multi-GPU: select specific GPU
with torch.cuda.device(1):                 # Use GPU 1
    x = torch.randn(1000, 1000, device='cuda')

# CUDA Streams — run operations concurrently
stream1 = torch.cuda.Stream()
stream2 = torch.cuda.Stream()

with torch.cuda.stream(stream1):
    out1 = model_a(data_a)   # Runs on stream1

with torch.cuda.stream(stream2):
    out2 = model_b(data_b)   # Runs on stream2, overlapping with stream1

torch.cuda.synchronize()   # Wait for all streams to finish

# Mixed precision — ~2× faster with FP16 tensors
from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()
with autocast():            # Forward pass in FP16
    output = model(inputs)
    loss = criterion(output, targets)

scaler.scale(loss).backward()   # Scale to avoid FP16 underflow
scaler.step(optimizer)
scaler.update()

Profiling with PyTorch Profiler

Python · Profile GPU Kernel Performance
from torch.profiler import profile, record_function, ProfilerActivity

with profile(
    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
    record_shapes=True,
    profile_memory=True,
) as prof:
    with record_function("model_forward"):
        output = model(inputs)
    with record_function("model_backward"):
        loss.backward()

# Print top 20 most expensive operations
print(prof.key_averages().table(
    sort_by="cuda_time_total",
    row_limit=20
))

# Export for TensorBoard visualization
prof.export_chrome_trace("trace.json")
# Open in chrome://tracing or Perfetto UI

Frequently Asked Questions

Why is my GPU utilization low even though my code runs on CUDA?

Low GPU utilization usually means CPU-GPU data transfer bottleneck or data loading is too slow. Check: use num_workers>0 in DataLoader, enable pin_memory=True, prefetch data, make sure your batch size is large enough to saturate GPU compute. Also check with nvidia-smi that memory is actually allocated. Use PyTorch Profiler to identify the bottleneck.

What is CUDA out of memory (OOM) and how do I fix it?

OOM happens when your model + activations + gradients exceed available VRAM. Fix strategies: reduce batch size, use gradient checkpointing (trade compute for memory), use mixed precision (FP16 halves activation memory), accumulate gradients over multiple small batches, use model sharding. For debugging, call torch.cuda.memory_summary() to see what's consuming memory.

What is the difference between CUDA cores and Tensor Cores?

CUDA cores perform general FP32 operations (one multiply-add per clock). Tensor Cores (introduced in Volta/V100) perform 4×4 matrix multiply in FP16 in a single instruction — 8× the throughput. PyTorch's torch.mm and nn.Linear automatically use Tensor Cores when inputs are FP16 or BF16. This is why mixed precision training is so much faster on modern GPUs.

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