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[CK_TILE] Add CShuffleLds microbenchmark suite
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## Summary
Microbenchmarks isolating LDS store/load operations in CShuffleEpilogue
for bank conflict analysis.
## Motivation
CShuffleEpilogue performs LDS store (MFMA registers → LDS) and load (LDS
→ registers for coalesced global writes). This suite isolates each
operation to:
- Identify which operation causes bank conflicts
- Measure pure LDS bandwidth per access pattern
- Validate access patterns across MFMA tile sizes and wave layouts
## Components
- **Microkernels** (`tile_load_store_microkernels.hpp`):
`StoreTile<Setup>`, `LoadTile<Setup>`
- **Setup Adapters** (`benchmark_cshuffle_lds.hpp`): Wire
CShuffleEpilogue to microkernels
- **Template** (`benchmark_template.cpp.in`): Generated benchmarks with
timing
## Build
```bash
cmake -G Ninja -B build -S . \
-DGPU_TARGETS=gfx950 \
-DBUILD_CK_EXAMPLES=ON \
-DBUILD_CK_TILE_CSHUFFLE_LDS_BENCHMARKS=ON
ninja -C build bench_lds_fp8_16x16x128_2x2_fp8
```
## New CMake Options
| Option | Default | Description |
|--------|---------|-------------|
| `BUILD_CK_TILE_CSHUFFLE_LDS_BENCHMARKS` | OFF | LDS microbenchmarks |
| `BUILD_CK_TILE_FMHA_TESTS` | ON | FMHA tests |
| `BUILD_CK_TILE_ENGINE` | ON | Tile engine |
| `BUILD_CK_TILE_ENGINE_TESTS` | ON | Tile engine tests |
| `BUILD_CK_EXAMPLES` | ON | Examples |
| `BUILD_CK_TUTORIALS` | ON | Tutorials |
| `BUILD_CK_DEVICE_INSTANCES` | ON | Device instances |
| `BUILD_CK_PROFILER` | ON | Profiler |
Setting guards to OFF reduces cmake configure from ~150s to ~5s.
CK Tile Example Suite
This directory contains a comprehensive suite of examples demonstrating the CK Tile programming model for high-performance GPU kernels. Each example illustrates a key deep learning or HPC operation, implemented using tile-based parallelism, modular pipelines, and data movement policy.
What is CK Tile?
CK Tile is a composable GPU programming API that expresses kernels as a composition of "tiles"—rectangular blocks of computation and data movement. The pipeline & policy orchestrates data movement (global <-> LDS <-> registers), computation, and synchronization, enabling high efficiency and flexibility.
Example Index
| Example | Operation | Description |
|---|---|---|
| 01_fmha | Fused Multi-Head Attention | Tile-based FMHA with masking, quantization, and epilogue fusion |
| 02_layernorm2d | LayerNorm2D | Blockwise layer normalization with fusion and quantization |
| 03_gemm | GEMM | Matrix multiplication with tilewise parallelism |
| 04_img2col | im2col | Image-to-column transformation for GEMM-based convolution |
| 05_reduce | Reduction | Tilewise sum, max, mean reductions |
| 06_permute | Permute | Generic tensor permutation (up to rank-8) |
| 09_topk_softmax | TopK-Softmax | Rowwise softmax and top-k selection for MoE gating |
| 10_rmsnorm2d | RMSNorm2D | Root mean square normalization for LLMs |
| 11_add_rmsnorm2d_rdquant | Add + RMSNorm2D + RDQuant | Fused add, RMSNorm, and rowwise dynamic quantization |
| 12_smoothquant | SmoothQuant | Per-channel scaling and quantization for int8 inference |
| 13_moe_sorting | MoE Sorting | Token-to-expert rearrangement for MoE dispatch |
| 14_moe_smoothquant | MoE-SmoothQuant | Expert-dependent quantization fused with top-k selection |
| 15_fused_moe | Fused MoE | End-to-end fused MoE block: sorting, group-GEMM, activation, weighting |
| 16_batched_gemm | Batched GEMM | Parallel computation of multiple GEMMs |
| 17_grouped_gemm | Grouped GEMM | Multiple independent GEMMs with different shapes |
| 18_flatmm | FLATMM | Flattened matrix multiplication for packed layouts |
| 19_gemm_multi_d | Multi-D GEMM | GEMM with multiple side inputs (bias, residual, etc.) |
| 35_batched_transpose | Batched Transpose | NCHW <-> NHWC and other layout conversions |
| 36_copy | Copy | Minimal example for tile-based memory movement |
| 37_transpose | Block Transpose | High-performance tiled transpose for large tensors |
Technical Highlights
- Tile Distribution: See
include/ck_tile/tile_program/tile_distribution/for mapping tiles to thread blocks. - Block Tile Pipelines: See
include/ck_tile/tile_program/block_tile_pipeline/for memory/computation pipelines. - Policies and Utilities: Many examples use custom policies for tile/block size and memory access.
How to Build & Run
mkdir build && cd build
sh ../script/cmake-ck-dev.sh ../ <arch>
make -j
Each example produces its own executable in build/bin/.
Learning and Extending
- Start Simple: Try 03_gemm or 36_copy to learn tile basics.
- Explore Fusion: See 11_add_rmsnorm2d_rdquant, 15_fused_moe, or 14_moe_smoothquant for advanced fusion.
- Experiment: Modify tile sizes, layouts, or pipelines to explore performance and flexibility.