Kernel Fusion

Each GPU kernel launch incurs significant fixed latency for driver scheduling and hardware setup. Kernel fusion eliminates this overhead by executing multiple computational stages within a single kernel. For example, a fused renderer might combine ray generation, sampling, and shading into one launch, reducing latency from milliseconds to microseconds. This is essential for achieving the 90+ FPS required for smooth VR experiences.

The primary bottleneck in GPU computing is often memory bandwidth, not raw compute. Separate kernels must write intermediate results to global memory (VRAM) before the next kernel can read them. Fusion keeps these intermediate values in fast on-chip registers or shared memory, drastically reducing costly off-chip memory accesses. This can improve effective bandwidth utilization by 2-5x for memory-bound rendering pipelines like those using large neural feature grids.

Fused kernels exhibit superior temporal and spatial locality. Data loaded for one operation is immediately reused by the next operation within the same thread or warp, maximizing the utility of the GPU's L1/L2 cache hierarchy. This is particularly beneficial for neural rendering workloads like evaluating a Multi-Layer Perceptron (MLP) across sampled points, where weights and activations can be kept cache-resident throughout the forward pass.

A fused kernel provides the compiler a unified view of the entire workload, enabling aggressive optimizations impossible across separate kernels:

• Common subexpression elimination across previously separate stages.
• Aggressive loop fusion and unrolling of adjacent operations.
• Improved instruction-level parallelism (ILP) by mixing compute and memory operations from different stages. This allows compilers like LLVM or NVCC to generate far more efficient machine code for the combined task.

Launching many small kernels forces the CPU to constantly manage the GPU command queue, leading to synchronization stalls. A fused kernel represents a larger, coarser-grained work unit, allowing the CPU to issue work less frequently. This reduces driver overhead and frees the CPU for other tasks like game logic or pose prediction, which is critical for maintaining low motion-to-photon latency in spatial computing applications.

Frameworks like Instant Neural Graphics Primitives (Instant NGP) rely on kernel fusion to achieve interactive training and inference. The pipeline—hashing coordinates, interpolating multi-resolution features, evaluating a tiny MLP, and compositing samples via ray marching—is implemented as a few highly optimized fused kernels. Without fusion, the overhead of launching dozens of micro-kernels per ray would make real-time NeRF rendering impossible on consumer hardware.

A compact, learnable data structure used to encode spatial features at multiple scales. It is the explicit data structure commonly fused with the small MLP in frameworks like Instant NGP. Kernel fusion is critical here to combine the hash table lookups, interpolation, and neural network evaluation into a single kernel, minimizing latency between these tightly coupled operations.

• Core Component of Instant Neural Graphics Primitives (Instant NGP).
• Enables high-fidelity, real-time rendering of neural radiance fields.
• Its efficiency is unlocked by fusing the feature fetch and decode steps with the subsequent neural network pass.

A scene representation paradigm that combines an explicit data structure (like a grid, hash table, or voxel) with a small neural network. This architecture is the primary target for kernel fusion optimizations. The fusion merges the sampling from the explicit structure with the inference pass of the neural network, eliminating the costly round-trip to global memory that would occur if they were separate kernels.

• Balances rendering speed and visual quality.
• Examples include Instant NGP (hash grid + MLP) and Plenoxels (voxel grid + spherical harmonics).
• Fusion turns the hybrid into a cohesive, high-performance computational unit.

The core volumetric rendering algorithm for neural radiance fields, where a ray is incrementally stepped through a 3D scene. Each step involves sampling the neural field at a 3D point. Kernel fusion is applied to the inner loop of ray marching, combining the coordinate transformation, feature lookup (from a hash grid, for example), MLP inference, and volume rendering equation (alpha compositing) into one monolithic kernel.

• Without fusion, each sample would require multiple separate kernel launches, creating massive overhead.
• Fusion allows the entire computation for hundreds of samples along a ray to reside in fast on-chip memory (registers/shared memory).

A two-stage graphics pipeline where a G-buffer (Geometry Buffer) containing rasterized surface attributes (position, normal, etc.) is first generated. A neural network then processes this G-buffer to produce the final image. Kernel fusion is used within the neural shading pass, combining layers of the post-processing network or fusing texture sampling operations with neural inference to apply complex materials and lighting.

• Decouples geometric complexity from shading complexity.
• The neural shading stage is a prime candidate for kernel fusion to achieve real-time performance.

A model compression technique that converts weights and activations from 32-bit floating-point (FP32) to 8-bit integers. This reduces memory bandwidth and accelerates inference. Kernel fusion is synergistic with quantization: after the model is quantized, the fused kernel operates on lower-precision data, which further increases arithmetic intensity and reduces memory traffic within the fused operation.

• Drastically reduces the model's memory footprint.
• Enables faster execution on hardware with dedicated INT8 pipelines (e.g., NVIDIA Tensor Cores).
• A fused, quantized kernel maximizes throughput per memory byte fetched.

A GPU hardware feature that allows different regions of an image to be shaded at different rates (e.g., 1x1, 2x2 pixels per shader invocation). While a hardware feature, it shares the philosophical goal of kernel fusion: optimizing compute resource allocation. In a neural renderer, a fused kernel could be designed to work in concert with VRS, applying more complex shading (via the neural network) only to high-detail regions identified by the VRS mask.

• A perceptual optimization for performance.
• Complements neural rendering by allowing the expensive fused kernel to be invoked less frequently in peripheral/low-detail regions.