Compiling models to megakernels
a month ago
- #Inference Compiler
- #GPU Optimization
- #Megakernels
- Luminal is an inference compiler focused on maximizing GPU utilization by addressing compute and bandwidth limitations.
- Traditional kernel execution faces bottlenecks like kernel launch overhead, wave quantization, and idle time during initial weight loading.
- Megakernels fuse entire model operations into a single kernel, eliminating synchronization gaps and improving hardware utilization.
- Dynamic scheduling in megakernels uses a global instruction queue, allowing SMs to fetch tasks opportunistically, reducing idle time.
- Barrier counters manage fine-grained synchronization, ensuring data readiness without full kernel synchronization.
- Luminal transforms compute graphs into instruction queues with optimized data dependencies and barrier strides.
- Symbolic work queues represent instructions symbolically, enabling dynamic dimension adjustments without queue rebuilds.
- Megakernels represent a next-gen approach to GPU programming, minimizing unnecessary synchronizations and keeping hardware busy.
- Luminal's work is open-source, inviting contributions and collaboration in advancing inference compiler technology.