correct
27_permutation_all_to_all.py
started
2026-05-11T21:37:41Z
timingkernelbench.timing
kernel mean / median / std
0.01 / — / 1.18e-04 μs
ref mean / median / std
0.03 / — / 0.01 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
2.67x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
warp stalls (top 5)nsight
reference PyTorch source (the prompt the agent sees)
"""
Fixed permutation along the feature axis: y[b, j] = x[b, perm[j]].
"""
import torch
import torch.nn as nn
from kernelbench.distributed_collectives import default_device
B = 10
N = 36
_g = torch.Generator()
_g.manual_seed(10)
PERM = torch.randperm(N, generator=_g)
class Model(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x[:, PERM.to(device=x.device)]
def get_inputs():
dev = default_device()
return [torch.randn(popcorn_pri.jitter_int(B), N, device=dev)]
def get_init_inputs():
return []
turn 0443.1s turn · 3.5s LLM · 439.6s tools · tokens in=1,979 · out=193 · total=2,172
reasoning
Let me analyze this task. The reference implementation does a simple permutation along the feature axis:
```python
y[b, j] = x[b, perm[j]]
```
Where:
- B = 10 (batch size)
- N = 36 (feature dimension)
- PERM is a random permutation of indices [0, N-1]
The operation is essentially a gather operation along the second dimension (columns/features).
Let me first check the GPU specs and then write a custom CUDA kernel for this permutation operation.
The key insight is that this is a memory-bound operation - we're just rearranging data. For small sizes like B=10, N=36, this is quite small, so we need to be careful about kernel launch overhead vs actual computation time.
Let me start by:
1. Getting GPU specs
2. Writing a basic CUDA kernel
3. Testing correctness
4. Profiling and optimizing
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 1372.9s turn · 6.6s LLM · 366.3s tools · tokens in=2,227 · out=668 · total=2,895
reasoning
Now let me write my first kernel attempt. The task is a simple permutation along the feature axis. I'll create a CUDA kernel that efficiently handles this.
Key observations:
1. B=10, N=36 are small dimensions
2. The permutation is fixed (36 elements)
3. We're doing y[b, j] = x[b, perm[j]]
For such small sizes, a simple kernel might be efficient enough. Let me write a basic version first.
assistantLet me write my first kernel attempt:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n int b = blockIdx.x * blockDim.x + threadIdx.x;\n int j = blockIdx.y * blockDim.y + threadIdx.y;\n \n if (b < B && j < N) {\n int src_j = perm[j];\n y[b * N + j] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n dim3 block(16, 16);\n dim3 grid((B + block.x - 1) / block.x, (N + block.y - 1) / block.y);\n \n permute_kernel<<<grid, block>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as a persistent buffer\n self.register_buffer('perm', PERM.clone())\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # Ensure perm is on same device as x\n perm = self.perm.to(device=x.device)\n return permute_ext.permute_cuda(x, perm)\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 2491.2s turn · 5.2s LLM · 486.0s tools · tokens in=2,813 · out=576 · total=3,389
reasoning
The compilation passed. Now let me check correctness.
call → run_correctness
callrun_correctness
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n int b = blockIdx.x * blockDim.x + threadIdx.x;\n int j = blockIdx.y * blockDim.y + threadIdx.y;\n \n if (b < B && j < N) {\n int src_j = perm[j];\n y[b * N + j] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n dim3 block(16, 16);\n dim3 grid((B + block.x - 1) / block.x, (N + block.y - 1) / block.y);\n \n permute_kernel<<<grid, block>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as a persistent buffer\n self.register_buffer('perm', PERM.clone())\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # Ensure perm is on same device as x\n perm = self.perm.to(device=x.device)\n return permute_ext.permute_cuda(x, perm)\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: ? trials did not all match.
runtime_error: expected scalar type Int but found Long
turn 3328.6s turn · 5.9s LLM · 322.7s tools · tokens in=3,404 · out=617 · total=4,021
reasoning
The error is about type mismatch - the permutation tensor is Long (int64) but the kernel expects Int (int32). I need to convert the permutation tensor to int32 before passing to CUDA. Let me fix this.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n int b = blockIdx.x * blockDim.x + threadIdx.x;\n int j = blockIdx.y * blockDim.y + threadIdx.y;\n \n if (b < B && j < N) {\n int src_j = perm[j];\n y[b * N + j] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n dim3 block(16, 16);\n dim3 grid((B + block.x - 1) / block.x, (N + block.y - 1) / block.y);\n \n permute_kernel<<<grid, block>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # Ensure perm is on same device as x (already int32)\n perm = self.perm.to(device=x.device)\n return permute_ext.permute_cuda(x, perm)\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 4423.3s turn · 5.4s LLM · 417.9s tools · tokens in=3,989 · out=581 · total=4,570
reasoning
Good, let me check correctness again.
call → run_correctness
callrun_correctness
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n int b = blockIdx.x * blockDim.x + threadIdx.x;\n int j = blockIdx.y * blockDim.y + threadIdx.y;\n \n if (b < B && j < N) {\n int src_j = perm[j];\n y[b * N + j] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n dim3 block(16, 16);\n dim3 grid((B + block.x - 1) / block.x, (N + block.y - 1) / block.y);\n \n permute_kernel<<<grid, block>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # Ensure perm is on same device as x (already int32)\n perm = self.perm.to(device=x.device)\n return permute_ext.permute_cuda(x, perm)\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 5411.8s turn · 5.9s LLM · 405.9s tools · tokens in=4,619 · out=597 · total=5,216
reasoning
Great, the correctness passed. Now let me profile this kernel to see how it performs and if there are any optimization opportunities.
call → profile_kernel
callprofile_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n int b = blockIdx.x * blockDim.x + threadIdx.x;\n int j = blockIdx.y * blockDim.y + threadIdx.y;\n \n if (b < B && j < N) {\n int src_j = perm[j];\n y[b * N + j] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n dim3 block(16, 16);\n dim3 grid((B + block.x - 1) / block.x, (N + block.y - 1) / block.y);\n \n permute_kernel<<<grid, block>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # Ensure perm is on same device as x (already int32)\n perm = self.perm.to(device=x.device)\n return permute_ext.permute_cuda(x, perm)\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty_like 43.0μs (49%) calls=1
#2: aten::empty_strided 28.0μs (32%) calls=1
#3: aten::to 17.0μs (19%) calls=1
--- Timing ---
GPU time: 3.26 μs
--- Memory Bandwidth ---
DRAM BW: 1.5/3350 GB/s (0.0%)
read=1.5 write=0.0 GB/s
Hit rates: L2=99.2%
--- Compute Throughput ---
Pipe util: FMA=0.6%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 10.5%
Eligible warps/cycle: 0.03
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=16, smem/blk=1024B, block=256, grid=3
--- Memory Access Quality ---
Global ld coalescing: 9.81 sectors/req (9.8x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 3145.8% ####################
short_scoreboard 309.3% ####################
--- Optimization Hints ---
-> 3146% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=9.8 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=9.8x (ideal=1.0) -> 10x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (11%). Consider __launch_bounds__ or reducing register pressure.
-> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).
turn 6446.9s turn · 8.6s LLM · 438.4s tools · tokens in=5,691 · out=901 · total=6,592
reasoning
The kernel is working but there are some issues:
1. Only 10.5% occupancy
2. Global load coalescing issue (9.8x wasted BW)
3. Memory latency stalls
The data size is quite small (B=10, N=36, total 360 floats = 1.44KB). Let me try to optimize for this tiny size. Since the data is so small, it might fit entirely in shared memory or L1 cache. Let me also try a 1D kernel with better memory access pattern.
Actually, looking at the code more carefully - the issue is that I'm reading `x[b * N + src_j]` where `src_j = perm[j]`. This means for a given b, consecutive j values read from non-consecutive locations in x (because perm is random). This causes the coalescing issue.
For such a small problem, the overhead of launching a kernel and other operations might be significant. The profile shows that the actual kernel time is only 3.26 μs, but the Python overhead (empty_like, to.device, etc.) is much larger.
Let me try:
1. A simpler kernel with 1D indexing
2. Avoid the .to(device=x.device) call each forward pass by ensuring perm is on the right device initially
3. Try pre-allocating the output buffer as a persistent buffer
Actually, let me first check if there's any issue with the tensor allocation overhead. For such a small tensor (360 elements), the overhead might dominate.
assistantLet me optimize the kernel for this small problem size:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Use 1D indexing - each thread handles one element\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n if (idx < total) {\n int b = idx / N;\n int j = idx % N;\n int src_j = perm[j];\n y[idx] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n \n permute_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # perm is already int32, just move to correct device\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 7346.9s turn · 5.2s LLM · 341.7s tools · tokens in=6,288 · out=581 · total=6,869
reasoning
Now let me run correctness check.
call → run_correctness
callrun_correctness
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Use 1D indexing - each thread handles one element\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n if (idx < total) {\n int b = idx / N;\n int j = idx % N;\n int src_j = perm[j];\n y[idx] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n \n permute_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_sources,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # perm is already int32, just move to correct device\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← run_correctness (fail)
failrun_correctness
run_correctness FAILED: kernel did not compile.
name 'cuda_sources' is not defined
turn 8251.5s turn · 6.0s LLM · 245.5s tools · tokens in=6,883 · out=599 · total=7,482
reasoning
Oops, I had a typo in my code - used `cuda_sources` instead of `cuda_source`. Let me fix that.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Use 1D indexing - each thread handles one element\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n if (idx < total) {\n int b = idx / N;\n int j = idx % N;\n int src_j = perm[j];\n y[idx] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n \n permute_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # perm is already int32, just move to correct device\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 9341.5s turn · 5.6s LLM · 335.9s tools · tokens in=7,469 · out=582 · total=8,051
reasoning
Let me check correctness and then profile.
call → run_correctness
callrun_correctness
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Use 1D indexing - each thread handles one element\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n if (idx < total) {\n int b = idx / N;\n int j = idx % N;\n int src_j = perm[j];\n y[idx] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n \n permute_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # perm is already int32, just move to correct device\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 10311.0s turn · 5.5s LLM · 305.5s tools · tokens in=8,100 · out=586 · total=8,686
reasoning
Good, let me profile this version and see how it performs.
call → profile_kernel
callprofile_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Use 1D indexing - each thread handles one element\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n if (idx < total) {\n int b = idx / N;\n int j = idx % N;\n int src_j = perm[j];\n y[idx] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n \n permute_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # perm is already int32, just move to correct device\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty_like 39.0μs (51%) calls=1
#2: aten::empty_strided 23.0μs (30%) calls=1
#3: aten::to 14.0μs (18%) calls=1
--- Timing ---
GPU time: 3.20 μs [↓0.1μs ok]
--- Memory Bandwidth ---
DRAM BW: 1.6/3350 GB/s (0.0%)
read=1.6 write=0.0 GB/s
Hit rates: L2=99.3%
--- Compute Throughput ---
Pipe util: FMA=1.1%, Tensor=0.1%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 10.6% [↑0.1% ok]
Eligible warps/cycle: 0.03
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=16, smem/blk=1024B, block=256, grid=2
--- Memory Access Quality ---
Global ld coalescing: 6.04 sectors/req (6.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2752.6% ####################
short_scoreboard 1830.6% ####################
--- Optimization Hints ---
-> 2753% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=6.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=6.0x (ideal=1.0) -> 6x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (11%). Consider __launch_bounds__ or reducing register pressure.
-> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).
--- Delta vs Previous Iteration ---
GPU time: 3.26→3.20μs (↓0.06) ok
Occupancy: 10.52→10.62% (↑0.10) ok
Ld sectors/req: 9.81→6.04 (↓3.76) ok
L2 hit%: 99.18→99.26% (↑0.08) ok
turn 11513.1s turn · 8.7s LLM · 504.3s tools · tokens in=9,286 · out=880 · total=10,166
reasoning
The 1D indexing is slightly better but still has issues with memory coalescing because threads with consecutive indices access scattered memory locations due to the permutation.
The key issue is that the permutation creates non-contiguous memory access patterns. For very small workloads like this (360 elements total), kernel launch overhead and memory allocation overhead dominate.
Let me try a different approach - using a shared memory buffer to cache the permutation, and trying to optimize the access pattern. Since N=36 is small, we can potentially load all permutation values into shared memory.
Actually, let me think more carefully. The reference implementation is:
```python
return x[:, PERM.to(device=x.device)]
```
This is using PyTorch's advanced indexing. For this small workload, PyTorch might be doing something pretty optimized. The main issue is that our kernel has significant overhead from launching and memory allocation.
Let me try an approach that processes all elements with better coalescing by reorganizing how we access memory.
assistantLet me try a more optimized approach using shared memory to cache the permutation and improve memory access patterns:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Shared memory for permutation\n extern __shared__ int s_perm[];\n \n // Load permutation into shared memory\n for (int i = threadIdx.x; i < N; i += blockDim.x) {\n s_perm[i] = perm[i];\n }\n __syncthreads();\n \n // Each thread handles multiple elements for better throughput\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n // Process elements with stride\n for (int i = idx; i < total; i += blockDim.x * gridDim.x) {\n int b = i / N;\n int j = i % N;\n int src_j = s_perm[j];\n y[i] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 128;\n int blocks = min((total + threads - 1) / threads, 128);\n int shared_mem = N * sizeof(int);\n \n permute_kernel<<<blocks, threads, shared_mem>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 12411.9s turn · 6.2s LLM · 405.7s tools · tokens in=10,008 · out=696 · total=10,704
reasoning
The user is suggesting I should submit now if I have a correct kernel. Let me check if my current kernel is correct by running correctness, and if it passes, I should submit it.
call → run_correctness
callrun_correctness
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Shared memory for permutation\n extern __shared__ int s_perm[];\n \n // Load permutation into shared memory\n for (int i = threadIdx.x; i < N; i += blockDim.x) {\n s_perm[i] = perm[i];\n }\n __syncthreads();\n \n // Each thread handles multiple elements for better throughput\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n // Process elements with stride\n for (int i = idx; i < total; i += blockDim.x * gridDim.x) {\n int b = i / N;\n int j = i % N;\n int src_j = s_perm[j];\n y[i] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 128;\n int blocks = min((total + threads - 1) / threads, 128);\n int shared_mem = N * sizeof(int);\n \n permute_kernel<<<blocks, threads, shared_mem>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (3 / 3) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 13453.1s turn · 6.7s LLM · 446.4s tools · tokens in=10,755 · out=686 · total=11,441
reasoning
The kernel passes correctness. Let me profile it to see if the shared memory optimization helped, and then decide whether to submit or make more changes.
call → profile_kernel
callprofile_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Shared memory for permutation\n extern __shared__ int s_perm[];\n \n // Load permutation into shared memory\n for (int i = threadIdx.x; i < N; i += blockDim.x) {\n s_perm[i] = perm[i];\n }\n __syncthreads();\n \n // Each thread handles multiple elements for better throughput\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n // Process elements with stride\n for (int i = idx; i < total; i += blockDim.x * gridDim.x) {\n int b = i / N;\n int j = i % N;\n int src_j = s_perm[j];\n y[i] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 128;\n int blocks = min((total + threads - 1) / threads, 128);\n int shared_mem = N * sizeof(int);\n \n permute_kernel<<<blocks, threads, shared_mem>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty_like 38.0μs (48%) calls=1
#2: aten::empty_strided 25.0μs (31%) calls=1
#3: aten::to 17.0μs (21%) calls=1
--- Timing ---
GPU time: 3.46 μs [↑0.3μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 1.4/3350 GB/s (0.0%)
read=1.4 write=0.0 GB/s
Hit rates: L2=99.7%
--- Compute Throughput ---
Pipe util: FMA=0.7%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 6.2% [↓4.4% REGRESSED]
Eligible warps/cycle: 0.03
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=19, smem/blk=1280B, block=128, grid=3
--- Memory Access Quality ---
Global ld coalescing: 5.83 sectors/req (5.8x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 1197.7% ####################
short_scoreboard 118.6% ####################
--- Optimization Hints ---
-> 1198% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=5.8 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=5.8x (ideal=1.0) -> 6x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (6%). Consider __launch_bounds__ or reducing register pressure.
-> Low eligible warps + low BW% + low compute% = LATENCY-BOUND. Need more parallelism (larger grid, more ILP, prefetching).
--- Delta vs Previous Iteration ---
GPU time: 3.20→3.46μs (↑0.26) REGRESSED
Occupancy: 10.62→6.24% (↓4.38) REGRESSED
Ld sectors/req: 6.04→5.83 (↓0.21) ok
L2 hit%: 99.26→99.68% (↑0.42) ok
turn 14391.2s turn · 5.7s LLM · 385.6s tools · tokens in=12,115 · out=612 · total=12,727 · FINAL
reasoning
Let me go back to the simpler 1D kernel without shared memory since it had better performance (3.20μs vs 3.46μs). Let me submit that version.
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "\"\"\"\nFixed permutation along the feature axis: y[b, j] = x[b, perm[j]].\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\nfrom kernelbench.distributed_collectives import default_device\n\nB = 10\nN = 36\n\n_g = torch.Generator()\n_g.manual_seed(10)\nPERM = torch.randperm(N, generator=_g)\n\n# Define the CUDA kernel\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y, \n const int* __restrict__ perm, int B, int N) {\n // Use 1D indexing - each thread handles one element\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * N;\n \n if (idx < total) {\n int b = idx / N;\n int j = idx % N;\n int src_j = perm[j];\n y[idx] = x[b * N + src_j];\n }\n}\n\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {\n int B = x.size(0);\n int N = x.size(1);\n \n auto y = torch::empty_like(x);\n \n int total = B * N;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n \n permute_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n y.data_ptr<float>(),\n perm.data_ptr<int>(),\n B, N\n );\n \n return y;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);\n\"\"\"\n\n# Compile the extension\npermute_ext = load_inline(\n name=\"permute_ext\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"permute_cuda\"],\n verbose=True,\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n # Register the permutation as int32 buffer\n self.register_buffer('perm', PERM.to(torch.int32))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # perm is already int32, just move to correct device\n return permute_ext.permute_cuda(x, self.perm.to(device=x.device))\n\n\ndef get_inputs():\n dev = default_device()\n g = torch.Generator(device=dev)\n g.manual_seed(10)\n return [torch.randn(B, N, device=dev, generator=g)]\n\n\ndef get_init_inputs():\n return []"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (3 / 3) correctness trials all passed.
Kernel runtime: 0.01 μs
Runtime stats: mean=0.01μs median=?μs std=0.00μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 33.0 MB peak (ref: 33.0 MB, ratio: 1.0x)
Kernel launches: 3 (ref: 8, fusion ratio: 2.6667)
SOL score: 0.0005
final submitted kernel (92 lines, 2,171 chars) — full source, untruncated
"""
Fixed permutation along the feature axis: y[b, j] = x[b, perm[j]].
"""
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
from kernelbench.distributed_collectives import default_device
B = 10
N = 36
_g = torch.Generator()
_g.manual_seed(10)
PERM = torch.randperm(N, generator=_g)
# Define the CUDA kernel
cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void permute_kernel(const float* __restrict__ x, float* __restrict__ y,
const int* __restrict__ perm, int B, int N) {
// Use 1D indexing - each thread handles one element
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int total = B * N;
if (idx < total) {
int b = idx / N;
int j = idx % N;
int src_j = perm[j];
y[idx] = x[b * N + src_j];
}
}
torch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm) {
int B = x.size(0);
int N = x.size(1);
auto y = torch::empty_like(x);
int total = B * N;
int threads = 256;
int blocks = (total + threads - 1) / threads;
permute_kernel<<<blocks, threads>>>(
x.data_ptr<float>(),
y.data_ptr<float>(),
perm.data_ptr<int>(),
B, N
);
return y;
}
"""
cpp_source = """
torch::Tensor permute_cuda(torch::Tensor x, torch::Tensor perm);
"""
# Compile the extension
permute_ext = load_inline(
name="permute_ext",
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=["permute_cuda"],
verbose=True,
)
class ModelNew(nn.Module):
def __init__(self):
super().__init__()
# Register the permutation as int32 buffer
self.register_buffer('perm', PERM.to(torch.int32))
def forward(self, x: torch.Tensor) -> torch.Tensor:
# perm is already int32, just move to correct device
return permute_ext.permute_cuda(x, self.perm.to(device=x.device))
def get_inputs():
dev = default_device()
g = torch.Generator(device=dev)
g.manual_seed(10)
return [torch.randn(B, N, device=dev, generator=g)]
def get_init_inputs():
return []