GPU Acceleration and Inference Optimization

GPU acceleration transforms compute-bound tasks into memory-bound ones by leveraging massive parallelism. Effective optimization requires bypassing high-level abstractions to manage the Single Instruction, Multiple Threads (SIMT) architecture and the memory hierarchy directly.

SIMT and Warp Divergence

Modern GPUs (NVIDIA CUDA architecture) execute threads in groups of 32 called **warps**. All threads in a warp execute the same instruction. When code branches (e.g., `if-else`), and threads within a warp take different paths, the GPU serializes execution for each path, a phenomenon known as **warp divergence**.

**Concrete Impact:** A 50/50 branch split in a warp results in 50% utilization.

**Optimization:** Use predication or move branching logic outside the inner kernel loop.

Memory Hierarchy Management

The primary bottleneck is usually moving data from Global Memory (VRAM) to Registers.

1. **Registers:** Fast, private to threads.

2. **Shared Memory:** User-managed L1 cache, shared within a thread block (~48KB-164KB).

3. **Global Memory:** High capacity, high latency (~400-800 cycles).

Concrete Example: Tiled Matrix Multiplication

Standard matrix multiplication ($C = A \times B$) has $O(N^3)$ operations but $O(N^2)$ data. Without tiling, every element of $A$ and $B$ is read from Global Memory $N$ times.

**Tiling Strategy:** Load "tiles" of $A$ and $B$ into **Shared Memory** once, perform sub-matrix multiplication using fast shared memory, and write the result back.

```cuda

__global__ void MatrixMulKernel(float* A, float* B, float* C, int N) {

__shared__ float As[TILE_SIZE][TILE_SIZE];

__shared__ float Bs[TILE_SIZE][TILE_SIZE];

int tx = threadIdx.x; int ty = threadIdx.y;

int row = blockIdx.y * TILE_SIZE + ty;

int col = blockIdx.x * TILE_SIZE + tx;

float Pvalue = 0;

for (int m = 0; m < N/TILE_SIZE; ++m) {

// Load tiles into shared memory

As[ty][tx] = A[row * N + m * TILE_SIZE + tx];

Bs[ty][tx] = B[(m * TILE_SIZE + ty) * N + col];

__syncthreads(); // Barrier: Wait for all threads to finish loading

for (int k = 0; k < TILE_SIZE; ++k)

Pvalue += As[ty][k] * Bs[k][tx];

__syncthreads(); // Barrier: Ensure computation is done before next load

}

C[row * N + col] = Pvalue;

}

```

*Result:* Reduces global memory traffic by a factor of `TILE_SIZE`.

Quantization and Mixed Precision

Reducing bit-width increases throughput and reduces memory pressure.

- **FP16/BF16:** Standard for training and inference on Tensor Cores.

- **INT8:** Requires calibration to map dynamic range. Typically yields 2-4x speedup on compatible hardware (Ampere+).

- **FP8:** Available on Hopper/Blackwell, offering a balance between range and precision.

Graph Compilation (TensorRT)

Static graphs allow the compiler to perform:

1. **Vertical Fusion:** Combines Conv + Bias + ReLU into one kernel.

2. **Horizontal Fusion:** Combines independent operations (e.g., 1x1 convolutions) into a single wide kernel.

3. **Kernel Selection:** Profiles different implementations (Winograd, FFT, Direct) to find the fastest for the specific GPU and input dimensions.

Key Performance Metrics

- **Arithmetic Intensity:** Ops / Byte. High intensity is required to saturate compute units.

- **Occupancy:** Ratio of active warps to maximum warps per SM. Low occupancy suggests register pressure or high shared memory usage.

- **Memory Bandwidth Utilization:** Percentage of theoretical peak achieved.