Parallel Reduce and Scan on the GPU
7 days ago
- #GPU Computing
- #Parallel Algorithms
- #Vulkan
- GPUs are powerful parallel machines capable of running thousands of threads simultaneously, but require specific APIs like Vulkan, CUDA, or OpenCL for interaction.
- Two fundamental algorithms discussed are reduce (summing elements) and scan (prefix sum), which are building blocks for more complex computations.
- Vulkan 1.1 introduces subgroup operations, allowing efficient communication within SIMD groups without relying on shared or global memory.
- Reduce operation in Vulkan uses subgroupAdd to sum elements within a subgroup, with additional steps for larger datasets via shared memory and multiple passes.
- Scan operation (prefix sum) uses subgroupInclusiveAdd for partial sums, combining results from subgroups to handle datasets larger than subgroup size.
- Performance benchmarks show scan operations with subgroups significantly outperform CPU implementations, while reduce shows modest improvements.
- The implementation leverages Vulkan's subgroup features for cross-platform compatibility (NVidia, AMD, Intel, Mali) and ease of use compared to CUDA.
- Shared memory and multiple passes are used to overcome limitations in workgroup sizes for both reduce and scan operations.
- Code examples and benchmarks are available on GitHub, utilizing a custom Vulkan engine (Vortex2D) for fluid simulation applications.