submission 613113 · KernelIndex

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No package. Vendor the mirrored source: 118 lines, June 9 Researcher Reciprocity License v1.0.

submission.py

curl "https://kernelindex.com/api/v1/implementations/kernelbot-histogram-v2-613113?include=source"

interfacepython

Compatibility

measured onNVIDIA B200

declared hardwareNVIDIA B200

architecturessm_100

dtypesuint8

Benchmark evidence

1 measurement across 1 GPU, fastest first.

Operation / workload

Hardware

Latency

Rank

Observed

Histogramsuite of 6 cases

NVIDIA B200

14.3µs

#15 of 54

2026-03-23

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Reported · How evidence levels are derived →

Source and license

sourceavailable

revision digestsha256:120f0224aa66dae314b218f463ca45c7cd69ca97a916378b499ef8afd59cf28e

license declaredunknown

license concludedunknown

authorsdannywillowliu-uchi

imported2026-08-15

Techniques

Extracted from the mirrored source by pattern, never inferred. Each row cites its line.

shared-memory__shared__ uint32_t smem[WARPS * 256];

vector-width = uint4const uint4* data_vec = (const uint4*)data;

Kernel source

submission.py118 lines

import os
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"

import torch
from torch.utils.cpp_extension import load_inline
from task import input_t, output_t

cuda_source = r"""
#include <torch/extension.h>
#include <cuda_runtime.h>
#include <cstdint>

__global__ __launch_bounds__(512)
void histogram_custom(
    const uint8_t* __restrict__ data,
    int64_t* __restrict__ output,
    const int64_t n
) {
    constexpr int WARPS = 16;
    __shared__ uint32_t smem[WARPS * 256];

    const int warp_id = threadIdx.x >> 5;

    #pragma unroll
    for (int i = threadIdx.x; i < WARPS * 256; i += 512) {
        smem[i] = 0;
    }
    __syncthreads();

    uint32_t* my_hist = smem + warp_id * 256;

    const int64_t tid = blockIdx.x * 512 + threadIdx.x;
    const int64_t grid_stride = 512LL * gridDim.x;

    const int64_t n16 = n >> 4;
    const uint4* data_vec = (const uint4*)data;

    for (int64_t i = tid; i < n16; i += grid_stride) {
        uint4 vals = __ldg(&data_vec[i]);

        atomicAdd(&my_hist[(vals.x      ) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.x >>  8) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.x >> 16) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.x >> 24)       ], 1u);
        atomicAdd(&my_hist[(vals.y      ) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.y >>  8) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.y >> 16) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.y >> 24)       ], 1u);
        atomicAdd(&my_hist[(vals.z      ) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.z >>  8) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.z >> 16) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.z >> 24)       ], 1u);
        atomicAdd(&my_hist[(vals.w      ) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.w >>  8) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.w >> 16) & 0xFF], 1u);
        atomicAdd(&my_hist[(vals.w >> 24)       ], 1u);
    }

    {
        int64_t tail_start = n16 * 16;
        for (int64_t i = tail_start + tid; i < n; i += grid_stride) {
            atomicAdd(&my_hist[__ldg(&data[i])], 1u);
        }
    }

    __syncthreads();

    if (threadIdx.x < 256) {
        uint32_t total = 0;
        #pragma unroll
        for (int w = 0; w < WARPS; w++) {
            total += smem[w * 256 + threadIdx.x];
        }
        if (total > 0) {
            atomicAdd((unsigned long long*)&output[threadIdx.x], (unsigned long long)total);
        }
    }
}

torch::Tensor histogram_cuda(torch::Tensor data, torch::Tensor output) {
    const int64_t n = data.numel();
    cudaMemsetAsync(output.data_ptr<int64_t>(), 0, 256 * sizeof(int64_t));

    int blocks = 296;
    if (n < 512 * 16 * blocks) {
        blocks = (n + 512 * 16 - 1) / (512 * 16);
        if (blocks < 1) blocks = 1;
    }

    histogram_custom<<<blocks, 512>>>(
        data.data_ptr<uint8_t>(),
        output.data_ptr<int64_t>(),
        n
    );

    return output;
}
""";

cpp_source = r"""
torch::Tensor histogram_cuda(torch::Tensor data, torch::Tensor output);
""";

module = load_inline(
    name="histogram_512_296",
    cpp_sources=[cpp_source],
    cuda_sources=[cuda_source],
    functions=["histogram_cuda"],
    verbose=False,
    extra_cuda_cflags=["-O3", "--use_fast_math"],
)


def custom_kernel(data: input_t) -> output_t:
    data_tensor, output = data
    module.histogram_cuda(data_tensor, output)
    return output

scrolls · 118 lines total

Source code from GPU Mode and the KernelBot dataset · June 9 Researcher Reciprocity License v1.0

Changes from previous submission

Against this author's previous submission submission 613101.

  ⋯ 8 unchanged lines
  #include <torch/extension.h>
  #include <cuda_runtime.h>
  #include <cstdint>
- #include <cstdio>
   
- __global__ __launch_bounds__(256)
+ __global__ __launch_bounds__(512)
  void histogram_custom(
      const uint8_t* __restrict__ data,
      int64_t* __restrict__ output,
      const int64_t n
  ) {
-     constexpr int WARPS = 8;
+     constexpr int WARPS = 16;
      __shared__ uint32_t smem[WARPS * 256];
   
      const int warp_id = threadIdx.x >> 5;
   
      #pragma unroll
-     for (int i = threadIdx.x; i < WARPS * 256; i += 256) {
+     for (int i = threadIdx.x; i < WARPS * 256; i += 512) {
          smem[i] = 0;
      }
      __syncthreads();
   
      uint32_t* my_hist = smem + warp_id * 256;
   
-     const int64_t tid = blockIdx.x * 256 + threadIdx.x;
-     const int64_t grid_stride = 256LL * gridDim.x;
+     const int64_t tid = blockIdx.x * 512 + threadIdx.x;
+     const int64_t grid_stride = 512LL * gridDim.x;
   
      const int64_t n16 = n >> 4;
      const uint4* data_vec = (const uint4*)data;
  ⋯ 40 unchanged lines
      }
  }
   
- int get_sm_count() {
-     cudaDeviceProp prop;
-     cudaGetDeviceProperties(&prop, 0);
-     return prop.multiProcessorCount;
- }
-  
  torch::Tensor histogram_cuda(torch::Tensor data, torch::Tensor output) {
      const int64_t n = data.numel();
      cudaMemsetAsync(output.data_ptr<int64_t>(), 0, 256 * sizeof(int64_t));
   
-     // Auto-tune block count based on SM count
-     static int sm_count = get_sm_count();
-     // Optimal: ~2 blocks per SM (to keep all SMs busy with good occupancy)
-     int blocks = sm_count * 2;
-     if (blocks < 148) blocks = 148;
-     if (blocks > 320) blocks = 320;
-  
-     if (n < 256 * 16 * blocks) {
-         blocks = (n + 256 * 16 - 1) / (256 * 16);
+     int blocks = 296;
+     if (n < 512 * 16 * blocks) {
+         blocks = (n + 512 * 16 - 1) / (512 * 16);
          if (blocks < 1) blocks = 1;
      }
   
-     histogram_custom<<<blocks, 256>>>(
+     histogram_custom<<<blocks, 512>>>(
          data.data_ptr<uint8_t>(),
          output.data_ptr<int64_t>(),
          n
  ⋯ 8 unchanged lines
  """;
   
  module = load_inline(
-     name="histogram_auto",
+     name="histogram_512_296",
      cpp_sources=[cpp_source],
      cuda_sources=[cuda_source],
      functions=["histogram_cuda"],

scrolls · 78 diff lines total

Best evidence level for this revision: reported

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