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20 changes: 10 additions & 10 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -96,16 +96,16 @@ Benchmarks comparing cuTile.jl against cuTile Python on an RTX 5080:

| Kernel | Julia | Python | Status |
|--------|-------|--------|--------|
| Vector Addition | 813 GB/s | 834 GB/s | OK (-3%) |
| Matrix Transpose | 769 GB/s | 795 GB/s | OK (-3%) |
| Matrix Multiplication | 48.3 TFLOPS | 48.6 TFLOPS | OK (=) |
| Layer Normalization | 254 GB/s | 683 GB/s | https://github.com/JuliaGPU/cuTile.jl/issues/1 (-63%) |
| Batch Matrix Multiply | 31.7 TFLOPS | 31.6 TFLOPS | OK (=) |
| FFT (3-stage Cooley-Tukey) | 508 μs | 230 μs | (-55%) |

Compute-intensive kernels (matmul, batch matmul) perform identically to Python. Memory-bound
kernels (vadd, transpose) are within ~3% of Python. The layernorm kernel is slower due to
conservative token threading in the compiler (see https://github.com/JuliaGPU/cuTile.jl/issues/1).
| Vector Addition | 840 GB/s | 844 GB/s | OK (=) |
| Matrix Transpose | 806 GB/s | 816 GB/s | OK (-1%) |
| Layer Normalization | 1074 GB/s | 761 GB/s | OK (+41%) |
| Matrix Multiplication | 36.8 TFLOPS | 50.7 TFLOPS | -27% |
| Batch Matrix Multiply | 28.3 TFLOPS | 40.0 TFLOPS | -29% |
| FFT (3-stage Cooley-Tukey) | 571 μs | 192 μs | -66% |

Memory-bound kernels (vadd, transpose, layernorm) match or beat Python. Compute-intensive
kernels (matmul, batch matmul, FFT) are slower due to conservative token threading in the
generated Tile IR, which serializes loads that could otherwise be pipelined.


## Supported Operations
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2 changes: 1 addition & 1 deletion examples/batchmatmul.py
Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@ def batchmatmul_cutile_kernel(A, B, C, tm: ct.Constant[int], tn: ct.Constant[int
# Example harness
#=============================================================================

def prepare(*, benchmark: bool = False, Batch: int = None, M: int = None, K: int = None, N: int = None, dtype=np.float16):
def prepare(*, benchmark: bool = False, Batch: int = None, M: int = None, K: int = None, N: int = None, dtype=np.float32):
"""Allocate and initialize data for batch matmul."""
if Batch is None:
Batch = 8 if benchmark else 4
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