Kimi-K2.6 · original · l12_default_gpt
error 7_Matmul_with_small_K_dimension_.py
model
Kimi-K2.6
turns / max
2 / 10
tool calls
1
tokens (total)
0
wall clock
—
truncated
no
backend
cuda fp32
variant
original
runtime
—
ref runtime
0.00 μs
speedup
—
started
2026-05-02T21:47:10Z
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a single matrix multiplication (C = A * B) with a small K dimension
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        """
        Performs matrix multiplication.

        Args:
            A: Input tensor of shape (M, K).
            B: Input tensor of shape (K, N).

        Returns:
            Output tensor of shape (M, N).
        """
        return torch.matmul(A, B)

M = 16384 * 2
N = 16384 * 2
K = 32 * 2

def get_inputs():
    A = torch.rand(M, K)
    B = torch.rand(K, N)
    return [A, B]

def get_init_inputs():
    return []  # No special initialization inputs needed
turn 010.9s turn · 10.9s LLM · 0.0s tools
reasoning
Let me analyze this task. I need to write a custom CUDA kernel that performs matrix multiplication C = A * B where: - A is (M, K) = (32768, 64) - B is (K, N) = (64, 32768) This is a very wide and tall matrix multiplication with a small K dimension (64). For small K GEMMs, there are several optimizations to consider: 1. Since K=64 is small, we want to load A and B efficiently into shared memory 2. Since M and N are very large, we have plenty of parallelism 3. A standard cuBLAS approach might not be optimal for such a shape Let me first get the GPU specs to understand what hardware I'm targeting.
call → get_gpu_specs
callget_gpu_specs {}
result ← get_gpu_specs (ok)
okget_gpu_specs GPU specs for NVIDIA H100 80GB HBM3: total memory (runtime): 79.1 GB spec entry: H100 GPU Architecture: Hopper GPU Memory: 80GB Memory Bandwidth: 3.35 TB/s FP64 TFLOPS: 34 FP64 Tensor Core TFLOPS: 67 FP32 TFLOPS: 67 TF32 Tensor Core TFLOPS: 989 with sparsity BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity FP16 Tensor Core TFLOPS: 1979 with sparsity FP8 Tensor Core TFLOPS: 3958 with sparsity INT8 Tensor Core TOPS: 3958 with sparsity Register File Size: 64K 32-bit registers per SM Maximum number of registers per thread: 255 Maximum number of thread blocks per SM: 32 Shared memory capacity per SM: 228 KB Maximum shared memory per thread block: 227 KB
turn 17189.1s turn · 7189.1s LLM · 0.0s tools
feedbackLLM call failed: _WorkItemTimeout: work item exceeded soft timeout budget