submission 66186
pmixer · python · License unknown
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No package. Vendor the mirrored source: 39 lines, June 9 Researcher Reciprocity License v1.0.
vectoradd_v2.00.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-vectoradd-v2-66186?include=source"interfacepython
Compatibility
measured onNVIDIA A100
declared hardwareNVIDIA A100
architecturessm_80
dtypesfp16
Benchmark evidence
1 measurement across 1 GPU, fastest first.
Operation / workload
Hardware
Latency
Rank
Observed
Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:02623c1c5a5595a69d857467b6133517e173d8b177122acac7f2adbaa6474a03
license declaredunknown
license concludedunknown
authorspmixer
imported2026-08-15
Kernel source
vectoradd_v2.00.py39 lines
from utils import make_match_reference, DeterministicContext
import torch
from task import input_t, output_t
def custom_kernel(data: input_t) -> output_t:
"""
Reference implementation of vector addition using PyTorch.
Args:
data: Tuple of tensors [A, B] to be added.
Returns:
Tensor containing element-wise sums.
"""
with DeterministicContext():
A, B, output = data
# output[...] = A + B
torch.add(A, B, out=output)
return output
def generate_input(size: int, seed: int) -> input_t:
"""
Generates random input tensors of specified shapes.
Returns:
Tuple of tensors [A, B] to be added.
"""
gen = torch.Generator(device="cuda")
gen.manual_seed(seed)
A = torch.randn(
size, size, device="cuda", dtype=torch.float16, generator=gen
).contiguous()
B = torch.randn(
size, size, device="cuda", dtype=torch.float16, generator=gen
).contiguous()
C = torch.empty(size, size, device="cuda", dtype=torch.float16).contiguous()
return A, B, C
# check_implementation = make_match_reference(ref_kernel)scrolls · 39 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 66185.
Best evidence level for this revision: reported
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