minjang

#blocked = #triton_gpu.blocked<{sizePerThread = [1, 1], threadsPerWarp = [1, 1], warpsPerCTA = [1, 1], order = [1, 0]}>
#blocked1 = #triton_gpu.blocked<{sizePerThread = [1, 16], threadsPerWarp = [1, 1], warpsPerCTA = [1, 1], order = [1, 0]}>
#loc = loc("/data/users/minjang/triton-oss/triton-cpu/python/tutorials/03-matrix-multiplication-cpu.py":166:0)
module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 1 : i32, triton_gpu.target = "cpu", "triton_gpu.threads-per-warp" = 1 : i32} {
  tt.func public @matmul_kernel(%arg0: !tt.ptr<f32> {tt.divisibility = 8192 : i32} loc("/data/users/minjang/triton-oss/triton-cpu/python/tutorials/03-matrix-multiplication-cpu.py":166:0), %arg1: !tt.ptr<f32> {tt.divisibility = 8192 : i32} loc("/data/users/minjang/triton-oss/triton-cpu/python/tutorials/03-matrix-multiplication-cpu.py":166:0), %arg2: !tt.ptr<f32> {tt.divisibility = 8192 : i32} loc("/data/users/minjang/triton-oss/triton-cpu/python/tutorials/03-matrix-multiplication-cpu.py":166:0), %arg3: i32 {tt.

minjang

	.text
	.file	"LLVMDialectModule"
	.section	.rodata,"a",@progbits
	.p2align	6, 0x0                          # -- Begin function matmul_kernel
.LCPI0_0:
	.zero	4
	.long	1                               # 0x1
	.long	2                               # 0x2
	.long	3                               # 0x3
	.long	4                               # 0x4

minjang

@triton.jit
def matmul_kernel(
        # Pointers to matrices
        a_ptr, b_ptr, c_ptr,
        # Matrix dimensions
        M, N, K,
        # The stride variables represent how much to increase the ptr by when moving by 1
        # element in a particular dimension. E.g. `stride_am` is how much to increase `a_ptr`
        # by to get the element one row down (A has M rows).
        stride_am, stride_ak,  #