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9_Tall_skinny_matrix_multiplication_ • multi_tile_mapping_base

Level 1 • Task 9
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(A, B):
    """
    Performs a single matrix multiplication (C = A * B) where one of the matrices is tall and skinny (M >> N or N >> M).

    Args:
        A (torch.Tensor): Input matrix of shape (M, K) or (K, M) where M >> N or N >> M.
        B (torch.Tensor): Input matrix of shape (K, N) or (N, K) where M >> N or N >> M.

    Returns:
        torch.Tensor: Output matrix of shape (M, N) or (N, M)
    """
    return torch.matmul(A, B)


class Model(nn.Module):
    """
    Simple model that performs a single matrix multiplication (C = A * B) where one of the matrices is tall and skinny (M >> N or N >> M)
    """

    def __init__(self):
        super(Model, self).__init__()

    def forward(self, A, B, fn=module_fn):
        return fn(A, B)


M = 16384
N = 16


def get_inputs():
    A = torch.randn(M, N)
    B = torch.randn(N, M)
    return [A, B]


def get_init_inputs():
    return []  # No special initialization inputs needed

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a single matrix multiplication (C = A * B) where one of the matrices is tall and skinny (M >> N or N >> M)
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, A, B):
        """
        Performs the matrix multiplication.

        Args:
            A (torch.Tensor): Input matrix of shape (M, K) or (K, M) where M >> N or N >> M.
            B (torch.Tensor): Input matrix of shape (K, N) or (N, K) where M >> N or N >> M.

        Returns:
            torch.Tensor: Output matrix of shape (M, N) or (N, M)
        """
        return torch.matmul(A, B)

M = 16384
N = 16

def get_inputs():
    A = torch.randn(M, N)
    B = torch.randn(N, M)
    return [A, B]

def get_init_inputs():
    return []  # No special initialization inputs needed
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 9_Tall_skinny_matrix_multiplication_
Level ID 1
Task ID 9
Kernel Name multi_tile_mapping_base
CUDA Speedup (Native) 0.749x
CUDA Speedup (Compile) 0.565x
CUDA Runtime 0.710 ms
PyTorch Runtime (Native) 0.532 ms
PyTorch Runtime (Compile) 0.401 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 9 • 9_Tall_skinny_matrix_multiplication_)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 unrolled_loop_matmul_base 0.68 0.78 0.59
🥈 constant_mem_matmul_base_base 0.69 0.78 0.58
🥉 unrolled_matmul_kernel_base 0.69 0.77 0.58
4 balanced_workload_matmul_base_base 0.71 0.75 0.56
4 multi_tile_mapping_base 0.71 0.75 0.56
6 optimized_tiled_gemm_base 0.71 0.75 0.56
6 optimized_matmul_kernel_base 0.71 0.75 0.56
8 streamed_balanced_matmul_base 0.75 0.71 0.53
9 streamed_balanced_matmul_base 0.75 0.71 0.53
9 streamed_pipelined_matmul_base 0.75 0.71 0.53
11 predicated_tile_loading_unrolled_edit_1 1.26 0.42 0.32
11 unrolled_loop_optimization_base 1.26 0.42 0.32
11 unrolled_loop_optimization_edit_1 1.26 0.42 0.32
11 modular_device_functions_edit_1 1.26 0.42 0.32
15 uniform_flow_matmul_base 1.26 0.42 0.32
15 warp_optimized_reduction_edit_1 1.26 0.42 0.32
17 predicated_tile_loading_unrolled_base 1.26 0.42 0.32
18 modular_device_functions_base 1.26 0.42 0.32
19 warp_divergence_optimized_base_base 1.27 0.42 0.32
20 coalesced_memory_access_base_base 1.27 0.42 0.32 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

#define BLOCK_SIZE 16
#define TILE_FACTOR 2
#define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)  // Each block computes a TILE_DIM x TILE_DIM output tile

// Helper to fetch matrix elements considering transpose
__device__ inline float get_element(const float* __restrict__ matrix, int row, int col, int ld, bool transpose) {
    return transpose ? matrix[col * ld + row] : matrix[row * ld + col];
}

// Kernel: each thread computes a 2x2 block of output, reducing the total thread count and aligning work
__global__ void matmul_kernel_multitile(const float* __restrict__ A,
                                         const float* __restrict__ B,
                                         float* __restrict__ C,
                                         int M, int N, int K,
                                         int lda, int ldb, int ldc,
                                         bool transA, bool transB) {
    // Determine the starting row and column for the block
    int blockRow = blockIdx.y * TILE_DIM;
    int blockCol = blockIdx.x * TILE_DIM;

    // Each thread computes a 2x2 tile within the block
    int threadRow = threadIdx.y;
    int threadCol = threadIdx.x;
    int row0 = blockRow + threadRow * TILE_FACTOR;  // starting row for this thread's tile
    int col0 = blockCol + threadCol * TILE_FACTOR;  // starting col for this thread's tile

    // Accumulators for a 2x2 output computed in registers
    float acc[2][2] = { {0.0f, 0.0f}, {0.0f, 0.0f} };

    // Shared memory tiles
    __shared__ float As[TILE_DIM][BLOCK_SIZE];
    __shared__ float Bs[BLOCK_SIZE][TILE_DIM];

    // Loop over tiles on the K dimension
    for (int t = 0; t < (K + BLOCK_SIZE - 1) / BLOCK_SIZE; ++t) {
        int tiledK = t * BLOCK_SIZE;
        
        // Load a tile of matrix A into shared memory
        // Each thread loads TILE_FACTOR elements from A
        for (int i = 0; i < TILE_FACTOR; ++i) {
            int globalRow = row0 + i;
            int globalCol = tiledK + threadIdx.x;
            if (globalRow < M && globalCol < K)
                As[threadIdx.y * TILE_FACTOR + i][threadIdx.x] = get_element(A, globalRow, globalCol, lda, transA);
            else
                As[threadIdx.y * TILE_FACTOR + i][threadIdx.x] = 0.0f;
        }

        // Load a tile of matrix B into shared memory
        // Each thread loads TILE_FACTOR elements from B
        for (int i = 0; i < TILE_FACTOR; ++i) {
            int globalRow = tiledK + threadIdx.y;
            int globalCol = col0 + i;
            if (globalRow < K && globalCol < N)
                Bs[threadIdx.y][threadIdx.x * TILE_FACTOR + i] = get_element(B, globalRow, globalCol, ldb, transB);
            else
                Bs[threadIdx.y][threadIdx.x * TILE_FACTOR + i] = 0.0f;
        }
        
        __syncthreads();
        
        // Multiply the loaded tiles
        for (int k = 0; k < BLOCK_SIZE; ++k) {
            float a0 = As[threadIdx.y * TILE_FACTOR + 0][k];
            float a1 = As[threadIdx.y * TILE_FACTOR + 1][k];
            float b0 = Bs[k][threadIdx.x * TILE_FACTOR + 0];
            float b1 = Bs[k][threadIdx.x * TILE_FACTOR + 1];
            acc[0][0] += a0 * b0;
            acc[0][1] += a0 * b1;
            acc[1][0] += a1 * b0;
            acc[1][1] += a1 * b1;
        }
        
        __syncthreads();
    }

    // Write back the computed 2x2 tile to global memory
    for (int i = 0; i < TILE_FACTOR; ++i) {
        for (int j = 0; j < TILE_FACTOR; ++j) {
            int globalRow = row0 + i;
            int globalCol = col0 + j;
            if (globalRow < M && globalCol < N)
                C[globalRow * ldc + globalCol] = acc[i][j];
        }
    }
}


torch::Tensor matmul_cuda(torch::Tensor A, torch::Tensor B) {
    if (!A.is_cuda() || !B.is_cuda()) {
        throw std::invalid_argument("Input tensors must be on CUDA devices");
    }
    if (A.dim() != 2 || B.dim() != 2) {
        throw std::invalid_argument("Input tensors must be 2D matrices");
    }

    int64_t A_rows = A.size(0);
    int64_t A_cols = A.size(1);
    int64_t B_rows = B.size(0);
    int64_t B_cols = B.size(1);

    bool transA = false;
    bool transB = false;
    int64_t M, N, K;
    int lda, ldb, ldc;

    if (A_rows >= A_cols && B_rows == A_cols) {
        // A is M x K, B is K x N
        M = A_rows;
        K = A_cols;
        N = B_cols;
        lda = A.stride(0);
        ldb = B.stride(0);
    } else if (A_cols > A_rows && B_rows == A_rows) {
        // A is transposed: K x M, so treat it as M x K with transA=true
        transA = true;
        M = A_cols;
        K = A_rows;
        N = B_cols;
        lda = A.stride(1);
        ldb = B.stride(0);
    } else if (A_rows >= A_cols && B_cols == A_cols) {
        // B is transposed: N x K, so treat it as K x N with transB=true
        transB = true;
        M = A_rows;
        K = A_cols;
        N = B_rows;
        lda = A.stride(0);
        ldb = B.stride(1);
    } else if (A_cols > A_rows && B_cols == A_rows) {
        // Both A and B are transposed
        transA = true;
        transB = true;
        M = A_cols;
        K = A_rows;
        N = B_rows;
        lda = A.stride(1);
        ldb = B.stride(1);
    } else {
        throw std::invalid_argument("Incompatible matrix dimensions for multiplication");
    }

    ldc = N;
    auto C = torch::empty({M, N}, A.options());

    dim3 blockDim(BLOCK_SIZE, BLOCK_SIZE);
    dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);

    matmul_kernel_multitile<<<gridDim, blockDim>>>(
        A.data_ptr<float>(),
        B.data_ptr<float>(),
        C.data_ptr<float>(),
        M, N, K,
        lda, ldb, ldc,
        transA, transB);
    cudaDeviceSynchronize();
    return C;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &matmul_cuda, "Matrix multiplication with optimized thread/block indexing (CUDA)");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 2.740 inst/cycle 0.000 5
Executed Ipc Elapsed 2.730 inst/cycle 0.000 5
Issue Slots Busy 68.562 % 0.001 5
Issued Ipc Active 2.740 inst/cycle 0.000 5
SM Busy 68.562 % 0.001 5
Memory Throughput 1290273961637.944 byte/second 454540160070862400.000 5
Mem Busy 97.278 % 0.000 5
Max Bandwidth 66.236 % 0.000 5
L1/TEX Hit Rate 49.620 % 0.000 5
L2 Hit Rate 99.318 % 0.003 5
Mem Pipes Busy 46.584 % 0.000 5
Warp Cycles Per Issued Instruction 16.258 cycle 0.000 5
Warp Cycles Per Executed Instruction 16.258 cycle 0.000 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 31.570 0.000 5
Max Active Clusters 0.000 cluster 0.000 5
Max Cluster Size 8.000 block 0.000 5
Overall GPU Occupancy 0.000 % 0.000 5
Cluster Occupancy 0.000 % 0.000 5
Block Limit SM 32.000 block 0.000 5
Block Limit Registers 6.000 block 0.000 5
Block Limit Shared Mem 12.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 48.000 warp 0.000 5
Theoretical Occupancy 75.000 % 0.000 5
Achieved Occupancy 70.032 % 0.000 5
Achieved Active Warps Per SM 44.820 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (35.0%) based on active cycles, taking into account the rates of its different instructions. It executes integer and logic operations. It is well-utilized, but should not be a bottleneck.
INF CPIStall Check the Warp Stall Sampling (All Cycles) table for the top stall locations in your source based on sampling data. The Kernel Profiling Guide (https://docs.nvidia.com/nsight-compute/ProfilingGuide/index.html#metrics-reference) provides more details on each stall reason.
WRN Occupancy This kernel's theoretical occupancy (75.0%) is limited by the number of required registers. See the CUDA Best Practices Guide (https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#occupancy) for more details on optimizing occupancy.
Operation / Metric Value Unit
aten::to
CPU Time 406699.58 μs
Device Time 80.77 μs
Self CPU Time 45.14 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_to_copy
CPU Time 406654.44 μs
Device Time 80.77 μs
Self CPU Time 106.55 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::empty_strided
CPU Time 406100.21 μs
Device Time 0.00 μs
Self CPU Time 129.29 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceGetStreamPriorityRange
CPU Time 405779.99 μs
Device Time 0.00 μs
Self CPU Time 405779.99 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
matmul_kernel_multitile(float const*, float const*, float*, int, int, int, int, int, int, bool, bool)
CPU Time 0.00 μs
Device Time 3094679.54 μs
Self CPU Time 0.00 μs
Self Device Time 3094679.54 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaDeviceSynchronize
CPU Time 3399460.87 μs
Device Time 470.97 μs
Self CPU Time 3399460.87 μs
Self Device Time 470.97 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaEventRecord
CPU Time 12989.66 μs
Device Time 65645.60 μs
Self CPU Time 12989.66 μs
Self Device Time 65645.60 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 44856.62 μs
Device Time 346677.24 μs
Self CPU Time 8333.07 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::fill_
CPU Time 36532.14 μs
Device Time 346677.24 μs
Self CPU Time 11235.68 μs
Self Device Time 346677.24 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<int>, at::detail::Array<char*, 1> >(int, at::native::FillFunctor<int>, at::detail::Array<char*, 1>)
CPU Time 0.00 μs
Device Time 346677.24 μs
Self CPU Time 0.00 μs
Self Device Time 346677.24 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45300 warnings generated when compiling for host.
Suppressed 45322 warnings (45275 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:18:56 bugprone-easily-swappable-parameters
18 | int M, int N, int K,
| ^~~~~~
19 | int lda, int ldb, int ldc,
| ~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:18:60: note: the first parameter in the range is 'K'
18 | int M, int N, int K,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:19:46: note: the last parameter in the range is 'lda'
19 | int lda, int ldb, int ldc,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:22:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
22 | int blockRow = blockIdx.y * TILE_DIM;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:23:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | int blockCol = blockIdx.x * TILE_DIM;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:26:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int threadRow = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:27:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | int threadCol = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:46:29: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
46 | int globalCol = tiledK + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:56:29: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
56 | int globalRow = tiledK + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:93:41: warning: the parameter 'A' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
93 | torch::Tensor matmul_cuda(torch::Tensor A, torch::Tensor B) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:93:58: warning: the parameter 'B' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
93 | torch::Tensor matmul_cuda(torch::Tensor A, torch::Tensor B) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:116:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
116 | lda = A.stride(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:117:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | ldb = B.stride(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:124:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | lda = A.stride(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:125:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
125 | ldb = B.stride(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:132:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
132 | lda = A.stride(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:133:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
133 | ldb = B.stride(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:141:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
141 | lda = A.stride(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:142:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
142 | ldb = B.stride(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:147:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
147 | ldc = N;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:23: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:23: note: make conversion explicit to silence this warning
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:23: note: perform multiplication in a wider type
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:39: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:39: note: make conversion explicit to silence this warning
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:39: note: perform multiplication in a wider type
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:54: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:54: note: make conversion explicit to silence this warning
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:54: note: perform multiplication in a wider type
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:70: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:70: note: make conversion explicit to silence this warning
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:151:70: note: perform multiplication in a wider type
151 | dim3 gridDim((N + TILE_DIM - 1) / TILE_DIM, (M + TILE_DIM - 1) / TILE_DIM);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:7:19: note: expanded from macro 'TILE_DIM'
7 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR) // Each block computes a TILE_DIM x TILE_DIM output tile
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:5:20: note: expanded from macro 'BLOCK_SIZE'
5 | #define BLOCK_SIZE 16
| ^~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:157:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
157 | M, N, K,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:157:12: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
157 | M, N, K,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b7_s2_multi_tile_mapping/base/base.cu:157:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
157 | M, N, K,
| ^