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12_Matmul_with_diagonal_matrices_ • hybrid_diag_matmul_base

Level 1 • Task 12
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(A, B):
    """
    Performs a matrix multiplication of a diagonal matrix with another matrix.

    Args:
        A (torch.Tensor): A 1D tensor representing the diagonal of the diagonal matrix. Shape: (N,).
        B (torch.Tensor): A 2D tensor representing the second matrix. Shape: (N, M).

    Returns:
        torch.Tensor: The result of the matrix multiplication. Shape: (N, M).
    """
    return torch.diag(A) @ B


class Model(nn.Module):
    """
    Simple model that performs a matrix multiplication of a diagonal matrix with another matrix.
    C = diag(A) * B
    """

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

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


M = 4096
N = 4096


def get_inputs():
    A = torch.randn(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 matrix multiplication of a diagonal matrix with another matrix.
    C = diag(A) * B
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, A, B):
        """
        Performs the matrix multiplication.

        Args:
            A (torch.Tensor): A 1D tensor representing the diagonal of the diagonal matrix. Shape: (N,).
            B (torch.Tensor): A 2D tensor representing the second matrix. Shape: (N, M).

        Returns:
            torch.Tensor: The result of the matrix multiplication. Shape: (N, M).
        """
        return torch.diag(A) @ B

M = 4096
N = 4096

def get_inputs():
    A = torch.randn(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 12_Matmul_with_diagonal_matrices_
Level ID 1
Task ID 12
Kernel Name hybrid_diag_matmul_base
CUDA Speedup (Native) 54.400x
CUDA Speedup (Compile) 55.456x
CUDA Runtime 0.051 ms
PyTorch Runtime (Native) 2.774 ms
PyTorch Runtime (Compile) 2.828 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 1.00
View Experiment Progress Details

Related Kernels (Level 1, Task 12 • 12_Matmul_with_diagonal_matrices_)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 flat_no_atomic_diag_matmul_base 0.05 54.40 55.46
🥇 shared_mem_diag_matmul_base_base 0.05 54.40 55.46
🥇 hybrid_diag_matmul_base 0.05 54.40 55.46
🥇 adaptive_diag_matmul_base 0.05 54.40 55.46
🥇 optimized_block_size_diag_matmul_base 0.05 54.40 55.46
🥇 hybrid_diag_matmul_base 0.05 54.40 55.46
🥇 adaptive_diag_matmul_base 0.05 54.40 55.46
🥇 diag_matmul_modular_edit_1 0.05 54.40 55.46
🥇 diag_matmul_warp_sync_base 0.05 54.40 55.46
🥇 diag_matmul_shared_min_sync_edit_1 0.05 54.40 55.46
🥇 diag_matmul_readonly_base 0.05 54.40 55.46
🥇 diag_matmul_modular_base 0.05 54.40 55.46
🥇 diag_matmul_shared_min_sync_base 0.05 54.40 55.46
🥇 stride_loop_diag_matmul_base 0.05 54.40 55.46
🥇 shared_memory_optimized_diag_matmul_base 0.05 54.40 55.46
🥇 unified_diag_matmul_base 0.05 54.40 55.46
🥇 diag_matmul_modular_base 0.05 54.40 55.46
🥇 adaptive_diag_matmul_edit_1 0.05 54.40 55.46
19 coalesced_diag_matmul_base 0.05 53.35 54.39
19 diag_matmul_readonly_edit_1 0.05 53.35 54.39 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

__global__ void hybrid_diag_matmul_kernel(
    const float* __restrict__ A,
    const float* __restrict__ B,
    float* __restrict__ C,
    const int64_t N,
    const int64_t M,
    const bool use_vectorized
) {
    if (use_vectorized) {
        // Vectorized approach for large matrices where M is divisible by 4
        const int tid = threadIdx.x + blockIdx.x * blockDim.x;
        const int stride = blockDim.x * gridDim.x;
        const int total = N * M;
        const int vec_total = total / 4;
        
        const float4* B_vec = reinterpret_cast<const float4*>(B);
        float4* C_vec = reinterpret_cast<float4*>(C);
        
        for (int idx = tid; idx < vec_total; idx += stride) {
            const int base_idx = idx * 4;
            const int row = base_idx / M;
            const float a_val = A[row];
            
            float4 b_val = B_vec[idx];
            float4 c_val;
            c_val.x = a_val * b_val.x;
            c_val.y = a_val * b_val.y;
            c_val.z = a_val * b_val.z;
            c_val.w = a_val * b_val.w;
            
            C_vec[idx] = c_val;
        }
    } else {
        // Row-based approach for smaller matrices or when M is not divisible by 4
        int row = blockIdx.x;
        if (row < N) {
            float a_val = A[row];
            const int main_end = (M / blockDim.x) * blockDim.x;
            
            // Main loop with coalesced access
            for (int j = threadIdx.x; j < main_end; j += blockDim.x) {
                int idx = row * M + j;
                C[idx] = a_val * B[idx];
            }
            
            // Handle remaining elements
            for (int j = main_end + threadIdx.x; j < M; j += blockDim.x) {
                int idx = row * M + j;
                C[idx] = a_val * B[idx];
            }
        }
    }
}

at::Tensor forward(at::Tensor A, at::Tensor B) {
    TORCH_CHECK(A.dim() == 1, "A must be a 1D tensor");
    TORCH_CHECK(B.dim() == 2, "B must be a 2D tensor");
    TORCH_CHECK(A.size(0) == B.size(0), "Dimension mismatch");

    A = A.contiguous();
    B = B.contiguous();

    int64_t N = A.size(0);
    int64_t M = B.size(1);
    auto C = torch::empty({N, M}, B.options());

    // Choose approach based on matrix size and alignment
    bool use_vectorized = (M >= 512) && (M % 4 == 0);
    
    if (use_vectorized) {
        const int threads = 256;
        const int blocks = min(65535, (int)((N * M + threads * 4 - 1) / (threads * 4)));
        hybrid_diag_matmul_kernel<<<blocks, threads>>>(
            A.data_ptr<float>(), B.data_ptr<float>(), C.data_ptr<float>(),
            N, M, true);
    } else {
        int threads = (M > 256) ? 256 : (((M + 31) / 32) * 32);
        dim3 grid(N);
        hybrid_diag_matmul_kernel<<<grid, threads>>>(
            A.data_ptr<float>(), B.data_ptr<float>(), C.data_ptr<float>(),
            N, M, false);
    }

    return C;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Hybrid diagonal matrix multiplication");
}
Performance Metrics
Metric Value Unit Variance Samples
Executed Ipc Active 1.630 inst/cycle 0.000 5
Executed Ipc Elapsed 1.498 inst/cycle 0.000 5
Issue Slots Busy 40.846 % 0.029 5
Issued Ipc Active 1.630 inst/cycle 0.000 5
SM Busy 40.846 % 0.029 5
Memory Throughput 2670907328523.448 byte/second 165924245620803633152.000 5
Mem Busy 46.770 % 0.058 5
Max Bandwidth 79.728 % 0.143 5
L1/TEX Hit Rate 2.698 % 0.000 5
L2 Hit Rate 49.912 % 0.009 5
Mem Pipes Busy 26.574 % 0.015 5
Warp Cycles Per Issued Instruction 31.908 cycle 0.263 5
Warp Cycles Per Executed Instruction 32.024 cycle 0.268 5
Avg. Active Threads Per Warp 32.000 0.000 5
Avg. Not Predicated Off Threads Per Warp 28.240 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 8.000 block 0.000 5
Block Limit Shared Mem 32.000 block 0.000 5
Block Limit Warps 8.000 block 0.000 5
Theoretical Active Warps per SM 64.000 warp 0.000 5
Theoretical Occupancy 100.000 % 0.000 5
Achieved Occupancy 80.928 % 0.046 5
Achieved Active Warps Per SM 51.792 warp 0.019 5
Analysis Rules
Rule Description
INF HighPipeUtilization ALU is the highest-utilized pipeline (23.9%) 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 is not impacted by any block limit. The difference between calculated theoretical (100.0%) and measured achieved occupancy (80.5%) can be the result of warp scheduling overheads or workload imbalances during the kernel execution. Load imbalances can occur between warps within a block as well as across blocks of the same kernel. 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 3487758.23 μs
Device Time 7117.72 μs
Self CPU Time 57.11 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 3487701.12 μs
Device Time 7117.72 μs
Self CPU Time 153.97 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 3480111.21 μs
Device Time 0.00 μs
Self CPU Time 170.57 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
cudaDeviceGetStreamPriorityRange
CPU Time 3410133.21 μs
Device Time 0.00 μs
Self CPU Time 3410133.21 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
cudaLaunchKernel
CPU Time 124359.25 μs
Device Time 860.70 μs
Self CPU Time 124359.25 μs
Self Device Time 860.70 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
hybrid_diag_matmul_kernel(float const*, float const*, float*, long, long, bool)
CPU Time 0.00 μs
Device Time 23141.37 μs
Self CPU Time 0.00 μs
Self Device Time 23141.37 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 112366.26 μs
Device Time 37868.82 μs
Self CPU Time 704.42 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 111662.71 μs
Device Time 37868.82 μs
Self CPU Time 929.91 μs
Self Device Time 37868.82 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 37868.82 μs
Self CPU Time 0.00 μs
Self Device Time 37868.82 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Failed 
45256 warnings and 1 error generated when compiling for host.
Error while processing /home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu.
Suppressed 45287 warnings (45240 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.
Found compiler error(s).
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:6:5 bugprone-easily-swappable-parameters
6 | const float* __restrict__ A,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~
7 | const float* __restrict__ B,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:6:31: note: the first parameter in the range is 'A'
6 | const float* __restrict__ A,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:7:31: note: the last parameter in the range is 'B'
7 | const float* __restrict__ B,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:15:25: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
15 | const int tid = threadIdx.x + blockIdx.x * blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:16:28: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
16 | const int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:17:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
17 | const int total = N * M;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:25:29: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
25 | const int row = base_idx / M;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:39:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
39 | int row = blockIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:42:34: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
42 | const int main_end = (M / blockDim.x) * blockDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:45:26: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
45 | for (int j = threadIdx.x; j < main_end; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:45:58: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
45 | for (int j = threadIdx.x; j < main_end; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:46:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
46 | int idx = row * M + j;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:51:26: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
51 | for (int j = main_end + threadIdx.x; j < M; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:51:62: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
51 | for (int j = main_end + threadIdx.x; j < M; j += blockDim.x) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:52:27: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
52 | int idx = row * M + j;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:76:28: error: no matching function for call to 'min' [clang-diagnostic-error]
76 | const int blocks = min(65535, (int)((N * M + threads * 4 - 1) / (threads * 4)));
| ^~~
/home/common_modules/clang-tidy/20.0.0git/lib/clang/20/include/__clang_cuda_math.h:201:16: note: candidate function not viable: call to __device__ function from __host__ function
201 | __DEVICE__ int min(int __a, int __b) { return __nv_min(__a, __b); }
| ^
/usr/local/cuda/include/crt/math_functions.hpp:868:38: note: candidate function not viable: call to __device__ function from __host__ function
868 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:873:38: note: candidate function not viable: call to __device__ function from __host__ function
873 | __MATH_FUNCTIONS_DECL__ unsigned int min(const int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:878:38: note: candidate function not viable: call to __device__ function from __host__ function
878 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:883:34: note: candidate function not viable: call to __device__ function from __host__ function
883 | __MATH_FUNCTIONS_DECL__ long int min(const long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:902:43: note: candidate function not viable: call to __device__ function from __host__ function
902 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:919:43: note: candidate function not viable: call to __device__ function from __host__ function
919 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:936:43: note: candidate function not viable: call to __device__ function from __host__ function
936 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:953:39: note: candidate function not viable: call to __device__ function from __host__ function
953 | __MATH_FUNCTIONS_DECL__ long long int min(const long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:958:48: note: candidate function not viable: call to __device__ function from __host__ function
958 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:963:48: note: candidate function not viable: call to __device__ function from __host__ function
963 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:968:48: note: candidate function not viable: call to __device__ function from __host__ function
968 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:973:31: note: candidate function not viable: call to __device__ function from __host__ function
973 | __MATH_FUNCTIONS_DECL__ float min(const float a, const float b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:978:32: note: candidate function not viable: call to __device__ function from __host__ function
978 | __MATH_FUNCTIONS_DECL__ double min(const double a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:983:32: note: candidate function not viable: call to __device__ function from __host__ function
983 | __MATH_FUNCTIONS_DECL__ double min(const float a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:988:32: note: candidate function not viable: call to __device__ function from __host__ function
988 | __MATH_FUNCTIONS_DECL__ double min(const double a, const float b)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:76:54: 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]
76 | const int blocks = min(65535, (int)((N * M + threads * 4 - 1) / (threads * 4)));
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:76:54: note: make conversion explicit to silence this warning
76 | const int blocks = min(65535, (int)((N * M + threads * 4 - 1) / (threads * 4)));
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:76:54: note: perform multiplication in a wider type
76 | const int blocks = min(65535, (int)((N * M + threads * 4 - 1) / (threads * 4)));
| ^~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:76:74: 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]
76 | const int blocks = min(65535, (int)((N * M + threads * 4 - 1) / (threads * 4)));
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:76:74: note: make conversion explicit to silence this warning
4 | const int blocks = min(65535, (int)((N * M + threads * 4 - 1) / (threads * 4)));
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:76:74: note: perform multiplication in a wider type
76 | const int blocks = min(65535, (int)((N * M + threads * 4 - 1) / (threads * 4)));
| ^~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_12/b4_s3_hybrid_diag_matmul/base/base.cu:81:41: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
81 | int threads = (M > 256) ? 256 : (((M + 31) / 32) * 32);
| ^