kernel launch configuration

**Kernel launch configuration** is the **selection of grid and block dimensions that determines how a GPU kernel maps work to hardware** - it strongly influences occupancy, memory behavior, and overall kernel throughput. **What Is Kernel launch configuration?** - **Definition**: GridDim and BlockDim parameters that define total threads and per-block parallel structure. - **Resource Coupling**: Launch shape interacts with register and shared-memory usage to set active residency. - **Work Partitioning**: Configuration determines indexing pattern, boundary handling, and thread utilization. - **Asynchronous Nature**: Kernel launches are typically non-blocking to the host until explicit synchronization. **Why Kernel launch configuration Matters** - **Throughput**: Poor launch geometry can leave hardware underutilized despite correct algorithm logic. - **Memory Efficiency**: Thread layout affects coalescing and cache reuse quality. - **Latency Hiding**: Appropriate block size improves active warp availability for scheduler. - **Scalability**: Well-chosen launch config maintains performance across diverse input sizes. - **Debuggability**: Deterministic launch patterns simplify correctness validation and profiling. **How It Is Used in Practice** - **Baseline Choice**: Start from architecture-recommended block sizes and adjust based on kernel profile. - **Occupancy Check**: Use occupancy calculators and profiler outputs to validate resource balance. - **Parameter Sweep**: Benchmark multiple launch combinations on real workloads before finalizing defaults. Kernel launch configuration is **a primary tuning lever in CUDA performance engineering** - correct thread mapping can produce large gains with no algorithmic change.