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9_Tall_skinny_matrix_multiplication_ • optimized_tiled_gemm_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
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Kernel Information

Operation Name 9_Tall_skinny_matrix_multiplication_
Level ID 1
Task ID 9
Kernel Name optimized_tiled_gemm_base
CUDA Speedup (Native) 0.748x
CUDA Speedup (Compile) 0.564x
CUDA Runtime 0.711 ms
PyTorch Runtime (Native) 0.532 ms
PyTorch Runtime (Compile) 0.401 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model azure-gpt-4o-2024-08-06
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 ELEMENTS_PER_THREAD 4
#define TILE_FACTOR 2
#define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)

// 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];
}

__global__ void optimized_tiled_gemm(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;

    int threadRow = threadIdx.y;
    int threadCol = threadIdx.x;
    int row0 = blockRow + threadRow * TILE_FACTOR;
    int col0 = blockCol + threadCol * TILE_FACTOR;

    float acc[TILE_FACTOR][TILE_FACTOR] = { {0.0f, 0.0f}, {0.0f, 0.0f} };

    __shared__ float As[TILE_DIM][BLOCK_SIZE];
    __shared__ float Bs[BLOCK_SIZE][TILE_DIM];

    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
        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
        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();
    }

    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_optimized(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 M = A.size(0);
    int64_t K = A.size(1);
    int64_t N = B.size(1);

    bool transA = false;
    bool transB = false;
    int lda = A.stride(0), ldb = B.stride(0), 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);

    optimized_tiled_gemm<<<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_optimized, "Optimized matrix multiplication combining multiple techniques (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 68.534 % 0.001 5
Issued Ipc Active 2.740 inst/cycle 0.000 5
SM Busy 68.534 % 0.001 5
Memory Throughput 1290470026822.698 byte/second 95548564925960736.000 5
Mem Busy 97.206 % 0.001 5
Max Bandwidth 66.200 % 0.000 5
L1/TEX Hit Rate 49.644 % 0.000 5
L2 Hit Rate 99.460 % 0.002 5
Mem Pipes Busy 46.560 % 0.000 5
Warp Cycles Per Issued Instruction 16.262 cycle 0.000 5
Warp Cycles Per Executed Instruction 16.264 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 70.024 % 0.000 5
Achieved Active Warps Per SM 44.816 warp 0.000 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (35.1%) 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 416543.78 μs
Device Time 79.90 μs
Self CPU Time 36.93 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 416506.85 μs
Device Time 79.90 μs
Self CPU Time 100.82 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 415921.30 μs
Device Time 0.00 μs
Self CPU Time 89.12 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 414148.12 μs
Device Time 0.00 μs
Self CPU Time 414148.12 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
cudaDeviceSynchronize
CPU Time 3376795.52 μs
Device Time 392.54 μs
Self CPU Time 3376795.52 μs
Self Device Time 392.54 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
optimized_tiled_gemm(float const*, float const*, float*, int, int, int, int, int, int, bool, bool)
CPU Time 0.00 μs
Device Time 3078052.36 μs
Self CPU Time 0.00 μs
Self Device Time 3078052.36 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 12723.07 μs
Device Time 64917.64 μs
Self CPU Time 12723.07 μs
Self Device Time 64917.64 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 45619.81 μs
Device Time 342855.22 μs
Self CPU Time 8910.57 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 36713.41 μs
Device Time 342855.22 μs
Self CPU Time 11533.90 μs
Self Device Time 342855.22 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 342855.22 μs
Self CPU Time 0.00 μs
Self Device Time 342855.22 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 
45294 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/b8_s3_optimized_tiled_gemm/base/base.cu:18:53 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/b8_s3_optimized_tiled_gemm/base/base.cu:18:57: 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/b8_s3_optimized_tiled_gemm/base/base.cu:19:43: 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/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:25:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | int threadRow = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:26:21: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
26 | int threadCol = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:41:29: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
41 | int globalCol = tiledK + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:50:29: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
50 | int globalRow = tiledK + threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:85:51: 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]
85 | torch::Tensor matmul_cuda_optimized(torch::Tensor A, torch::Tensor B) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:85:68: 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]
85 | torch::Tensor matmul_cuda_optimized(torch::Tensor A, torch::Tensor B) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:99:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | int lda = A.stride(0), ldb = B.stride(0), ldc = N;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:99:34: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | int lda = A.stride(0), ldb = B.stride(0), ldc = N;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:99:53: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
99 | int lda = A.stride(0), ldb = B.stride(0), ldc = N;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:104:23: 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]
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:23: note: make conversion explicit to silence this warning
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:23: note: perform multiplication in a wider type
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:39: 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]
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:39: note: make conversion explicit to silence this warning
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:39: note: perform multiplication in a wider type
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:54: 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]
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:54: note: make conversion explicit to silence this warning
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:54: note: perform multiplication in a wider type
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:70: 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]
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:70: note: make conversion explicit to silence this warning
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:104:70: note: perform multiplication in a wider type
104 | 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/b8_s3_optimized_tiled_gemm/base/base.cu:8:19: note: expanded from macro 'TILE_DIM'
8 | #define TILE_DIM (BLOCK_SIZE * TILE_FACTOR)
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/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/b8_s3_optimized_tiled_gemm/base/base.cu:110:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | M, N, K,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:110:12: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | M, N, K,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_9/b8_s3_optimized_tiled_gemm/base/base.cu:110:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
110 | M, N, K,
| ^