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submission 69352

Saint of the Famished · python · License unknown
Kernel source · 64 lines ↓

Use it

Vendorable · source mirrored · license unknownView source →

No package. Vendor the mirrored source: 64 lines, June 9 Researcher Reciprocity License v1.0.

submission_triton.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-vectorsum-v2-69352?include=source"
interfacepython
Compatibility
measured onNVIDIA A100
declared hardwareNVIDIA A100
architecturessm_80
dtypesfp32

Benchmark evidence

1 measurement across 1 GPU, fastest first.

Operation / workload
Hardware
Latency
Rank
Observed
Vector sum reductionsuite of 6 cases
NVIDIA A100
141.0µs
#14 of 96
2025-11-10
Run →

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:094832b781e58afd87fcf3eb270c1f611dcb9786fa26199bd84675fb7fa48b6c
license declaredunknown
license concludedunknown
authorsSaint of the Famished
imported2026-08-15

Techniques

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

autotune@triton.autotune(
num-warps = 4triton.Config(kwargs={"BLOCK_SIZE": 2**7}, num_warps=4),

Kernel source

submission_triton.py64 lines
#!POPCORN leaderboard vectorsum_v2

import subprocess

import torch
import triton
import triton.language as tl
from task import input_t, output_t

GPU_TO_N_ELEMENTS = {"NVIDIA A100-SXM4-80GB": 52428800}
GPU_NAME = torch.cuda.get_device_name(0)


@triton.autotune(
    configs=[
        triton.Config(kwargs={"BLOCK_SIZE": 2**7}, num_warps=4),
        triton.Config(kwargs={"BLOCK_SIZE": 2**8}, num_warps=8),
        triton.Config(kwargs={"BLOCK_SIZE": 2**9}, num_warps=8),
        triton.Config(kwargs={"BLOCK_SIZE": 2**10}, num_warps=8),
        triton.Config(kwargs={"BLOCK_SIZE": 2**11}, num_warps=8),
        triton.Config(kwargs={"BLOCK_SIZE": 2**12}, num_warps=8),
    ],
    key=["n_elements"],
)
@triton.jit
def sum_kernel(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    block_start = pid * BLOCK_SIZE
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n_elements
    x = tl.load(x_ptr + offsets, mask=mask, other=0.0, eviction_policy="evict_first")
    block_sum = tl.sum(x, axis=0)
    tl.store(x_ptr + pid, block_sum)


def tune(n_elements: int = GPU_TO_N_ELEMENTS.get(GPU_NAME, 1024)):
    data = torch.randn(n_elements, device="cuda", dtype=torch.float32).contiguous()
    offset = (torch.rand(1, device="cuda") * 200 - 100).item()
    scale = (torch.rand(1, device="cuda") * 9.9 + 0.1).item()
    input_tensor = (data * scale + offset).contiguous()
    output_tensor = torch.empty(1, device="cuda", dtype=torch.float32)

    grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
    sum_kernel[grid](input_tensor, output_tensor, n_elements)


tune()


def custom_kernel(data: input_t) -> output_t:
    input, output = data
    n_elements = input.numel()
    if n_elements != GPU_TO_N_ELEMENTS.get(GPU_NAME, 1024):
        print(f"[WARN]: n_elements of {n_elements} is not tuned for.")
        tune(n_elements)

    grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
    sum_kernel[grid](input, output, n_elements)

    block_size = sum_kernel.best_config.all_kwargs()["BLOCK_SIZE"]
    n_blocks = triton.cdiv(n_elements, block_size)

    return input[:n_blocks].sum()
scrolls · 64 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 66970.

  #!POPCORN leaderboard vectorsum_v2
   
+ import subprocess
+  
  import torch
  import triton
  import triton.language as tl
  from task import input_t, output_t
   
+ GPU_TO_N_ELEMENTS = {"NVIDIA A100-SXM4-80GB": 52428800}
+ GPU_NAME = torch.cuda.get_device_name(0)
   
+  
+ @triton.autotune(
+     configs=[
+         triton.Config(kwargs={"BLOCK_SIZE": 2**7}, num_warps=4),
+         triton.Config(kwargs={"BLOCK_SIZE": 2**8}, num_warps=8),
+         triton.Config(kwargs={"BLOCK_SIZE": 2**9}, num_warps=8),
+         triton.Config(kwargs={"BLOCK_SIZE": 2**10}, num_warps=8),
+         triton.Config(kwargs={"BLOCK_SIZE": 2**11}, num_warps=8),
+         triton.Config(kwargs={"BLOCK_SIZE": 2**12}, num_warps=8),
+     ],
+     key=["n_elements"],
+ )
  @triton.jit
- def sum_kernel(
-     x_ptr,
-     n_elements,
-     BLOCK_SIZE: tl.constexpr,
- ):
+ def sum_kernel(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
      pid = tl.program_id(0)
      block_start = pid * BLOCK_SIZE
      offsets = block_start + tl.arange(0, BLOCK_SIZE)
  ⋯ 3 unchanged lines
      tl.store(x_ptr + pid, block_sum)
   
   
+ def tune(n_elements: int = GPU_TO_N_ELEMENTS.get(GPU_NAME, 1024)):
+     data = torch.randn(n_elements, device="cuda", dtype=torch.float32).contiguous()
+     offset = (torch.rand(1, device="cuda") * 200 - 100).item()
+     scale = (torch.rand(1, device="cuda") * 9.9 + 0.1).item()
+     input_tensor = (data * scale + offset).contiguous()
+     output_tensor = torch.empty(1, device="cuda", dtype=torch.float32)
+  
+     grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
+     sum_kernel[grid](input_tensor, output_tensor, n_elements)
+  
+  
+ tune()
+  
+  
  def custom_kernel(data: input_t) -> output_t:
      input, output = data
      n_elements = input.numel()
+     if n_elements != GPU_TO_N_ELEMENTS.get(GPU_NAME, 1024):
+         print(f"[WARN]: n_elements of {n_elements} is not tuned for.")
+         tune(n_elements)
   
-     if n_elements >= 10_000_000:
-         BLOCK_SIZE = 8192
-     elif n_elements >= 1_000_000:
-         BLOCK_SIZE = 4096
-     elif n_elements >= 100_000:
-         BLOCK_SIZE = 2048
-     elif n_elements >= 10_000:
-         BLOCK_SIZE = 1024
-     else:
-         BLOCK_SIZE = 512
-  
      grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
+     sum_kernel[grid](input, output, n_elements)
   
-     n_blocks = triton.cdiv(n_elements, BLOCK_SIZE)
-     grid = (n_blocks,)
+     block_size = sum_kernel.best_config.all_kwargs()["BLOCK_SIZE"]
+     n_blocks = triton.cdiv(n_elements, block_size)
   
-     sum_kernel[grid](input, n_elements, BLOCK_SIZE=BLOCK_SIZE)
-  
      return input[:n_blocks].sum()
scrolls · 81 diff lines total

Best evidence level for this revision: reported

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