correct
47_virtual_all_gather_concat_linear.py
started
2026-05-04T09:27:41Z
timingkernelbench.timing
kernel mean / median / std
0.01 / — / 2.47e-04 μs
ref mean / median / std
0.01 / — / 6.77e-04 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
14.00x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
warp stalls (top 5)nsight
reference PyTorch source (the prompt the agent sees)
"""
Virtual all-gather on the last dim: stack R tensors of shape (B, H) into (B, R*H), then Linear.
"""
import torch
import torch.nn as nn
from kernelbench.distributed_collectives import default_device
R = 4
B = 12
H = 32
K = 56
class Model(nn.Module):
def __init__(self):
super().__init__()
self.proj = nn.Linear(R * H, K, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
r, b, h = x.shape
assert r == R and h == H
parts = [x[i] for i in range(R)]
gathered = torch.cat(parts, dim=-1)
return self.proj(gathered)
def get_inputs():
dev = default_device()
p = popcorn_pri
return [torch.randn(p.jitter_int(R), p.jitter_int(B), p.jitter_int(D, align=8), device=dev)]
def get_init_inputs():
return []
turn 014.2s turn · 14.2s LLM · 0.0s tools
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define R 4\n#define B 12\n#define H 32\n#define K 56\n#define N (R*H)\n\n__global__ __launch_bounds__(256, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ y) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5; // 0..7\n int k = blockIdx.x * 8 + warp;\n int b = blockIdx.y;\n if (k >= K) return;\n\n float sum = 0.0f;\n const float* wk = w + k * N;\n // gathered index i = r*H+h, x layout [R,B,H]\n #pragma unroll\n for (int i = lane; i < N; i += 32) {\n int r = i >> 5;\n int h = i & 31;\n float xv = x[(r * B + b) * H + h];\n sum = fmaf(xv, wk[i], sum);\n }\n // warp reduce\n #pragma unroll\n for (int off = 16; off > 0; off >>= 1) {\n sum += __shfl_down_sync(0xffffffff, sum, off);\n }\n if (lane == 0) {\n y[b * K + k] = sum + bias[k];\n }\n}\n\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = torch::empty({B, K}, x.options());\n dim3 block(256);\n dim3 grid((K + 7) / 8, B);\n vga_linear_kernel<<<grid, block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>());\n return y;\n}\n'''\n\n_ext = load_inline(\n name='vga_linear_ext',\n cpp_sources='',\n cuda_sources=_cuda_src,\n functions=['vga_linear_forward'],\n verbose=False,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.proj = nn.Linear(4 * 32, 56, bias=True)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.vga_linear_forward(x, self.proj.weight, self.proj.bias)\n"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'vga_linear_ext': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=vga_linear_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_47_cache/k_165174dd85d6/vga_linear_ext/main.cpp -o main.o
FAILED: [code=1] main.o
/opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=vga_linear_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_47_cache/k_165174dd85d6/vga_linear_ext/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_47_cache/k_165174dd85d6/vga_linear_ext/main.cpp: In function 'void pybind11_init_vga_linear_ext(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_47_cache/k_165174dd85d6/vga_linear_ext/main.cpp:4:57: error: 'vga_linear_forward' was not declared in this scope
4 | m.def("vga_linear_forward", torch::wrap_pybind_function(vga_linear_forward), "vga_linear_forward");
| ^~~~~~~~~~~~~~~~~~
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=vga_linear_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 --use_fast_math -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_47_cache/k_165174dd85d6/vga_linear_ext/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
ninja: build stopped: subcommand failed.
turn 15.8s turn · 5.8s LLM · 0.0s tools
reasoning
**Evaluating C++ declaration needs**
I need to make sure I declare a function in the cpp_sources when using functions.load_inline, as it requires a C++ prototype. This means I have to put the necessary includes and declaration in cpp_sources, along with the CUDA definition. It feels like a straightforward task but getting the organization right will be key to ensuring everything runs smoothly. I'll stay focused as I tackle this to avoid any mix-ups!
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define R 4\n#define B 12\n#define H 32\n#define K 56\n#define N (R*H)\n\n__global__ __launch_bounds__(256, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ y) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n int k = blockIdx.x * 8 + warp;\n int b = blockIdx.y;\n if (k >= K) return;\n\n float sum = 0.0f;\n const float* wk = w + k * N;\n #pragma unroll\n for (int i = lane; i < N; i += 32) {\n int r = i >> 5;\n int h = i & 31;\n float xv = x[(r * B + b) * H + h];\n sum = fmaf(xv, wk[i], sum);\n }\n #pragma unroll\n for (int off = 16; off > 0; off >>= 1) {\n sum += __shfl_down_sync(0xffffffff, sum, off);\n }\n if (lane == 0) {\n y[b * K + k] = sum + bias[k];\n }\n}\n\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = torch::empty({B, K}, x.options());\n dim3 block(256);\n dim3 grid((K + 7) / 8, B);\n vga_linear_kernel<<<grid, block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>());\n return y;\n}\n'''\n\n_ext = load_inline(\n name='vga_linear_ext2',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['vga_linear_forward'],\n verbose=False,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.proj = nn.Linear(4 * 32, 56, bias=True)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.vga_linear_forward(x, self.proj.weight, self.proj.bias)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 29.7s turn · 9.7s LLM · 0.0s tools
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define R 4\n#define B 12\n#define H 32\n#define K 56\n#define N (R*H)\n\n__global__ __launch_bounds__(256, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ y) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n int k = blockIdx.x * 8 + warp;\n int b = blockIdx.y;\n if (k >= K) return;\n\n float sum = 0.0f;\n const float* wk = w + k * N;\n #pragma unroll\n for (int i = lane; i < N; i += 32) {\n int r = i >> 5;\n int h = i & 31;\n float xv = x[(r * B + b) * H + h];\n sum = fmaf(xv, wk[i], sum);\n }\n #pragma unroll\n for (int off = 16; off > 0; off >>= 1) {\n sum += __shfl_down_sync(0xffffffff, sum, off);\n }\n if (lane == 0) {\n y[b * K + k] = sum + bias[k];\n }\n}\n\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = torch::empty({B, K}, x.options());\n dim3 block(256);\n dim3 grid((K + 7) / 8, B);\n vga_linear_kernel<<<grid, block>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>());\n return y;\n}\n'''\n\n_ext = load_inline(\n name='vga_linear_ext2',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['vga_linear_forward'],\n verbose=False,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.proj = nn.Linear(4 * 32, 56, bias=True)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.vga_linear_forward(x, self.proj.weight, self.proj.bias)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=2.38e-07 mean_abs_err=4.59e-08 max_rel_err=3.49e-05
turn 35.5s turn · 5.5s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define R 4\n#define B 12\n#define H 32\n#define K 56\n#define N (R*H)\n__global__ __launch_bounds__(256, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n int k = blockIdx.x * 8 + warp;\n int b = blockIdx.y;\n if (k >= K) return;\n float sum = 0.0f;\n const float* wk = w + k * N;\n #pragma unroll\n for (int i = lane; i < N; i += 32) {\n int r = i >> 5;\n int h = i & 31;\n float xv = x[(r * B + b) * H + h];\n sum = fmaf(xv, wk[i], sum);\n }\n #pragma unroll\n for (int off = 16; off > 0; off >>= 1) sum += __shfl_down_sync(0xffffffff, sum, off);\n if (lane == 0) y[b * K + k] = sum + bias[k];\n}\n\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = torch::empty({B, K}, x.options());\n vga_linear_kernel<<<dim3((K+7)/8,B), dim3(256)>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>());\n return y;\n}\n'''\n_ext = load_inline(name='vga_linear_ext_prof', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['vga_linear_forward'], verbose=False, extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__(); self.proj = nn.Linear(128,56,bias=True)\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.vga_linear_forward(x, self.proj.weight, self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 33.0μs (100%) calls=1
--- Timing ---
GPU time: 3.46 μs
--- Memory Bandwidth ---
DRAM BW: 11.1/3350 GB/s (0.3%)
read=11.1 write=0.0 GB/s
Hit rates: L2=95.6%
--- Compute Throughput ---
Pipe util: FMA=1.3%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 12.4%
Eligible warps/cycle: 0.04
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=18, smem/blk=1024B, block=256, grid=84
--- Memory Access Quality ---
Global ld coalescing: 3.67 sectors/req (3.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2441.8% ####################
short_scoreboard 951.8% ####################
--- Optimization Hints ---
-> 2442% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.7x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (12%). 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 415.3s turn · 15.3s LLM · 0.0s tools
reasoning
**Implementing shared memory coalescing**
I'm exploring the idea of using shared memory per block with 128 loads, which could optimize speed. My plan is to set up shared memory for `x[128]` per block at 512B. The first 128 threads will load the `x` vector, and then I'll sync. Each warp will read from `sx[i]`, which might enable broadcasting since the same addresses are accessed across warps. I think profiling this approach will help me see the effectiveness.
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define R 4\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n__global__ __launch_bounds__(256, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y) {\n __shared__ float sx[N];\n int tid = threadIdx.x;\n int b = blockIdx.y;\n if (tid < N) {\n int r = tid >> 5;\n int h = tid & 31;\n sx[tid] = x[(r * B + b) * H + h];\n }\n __syncthreads();\n int lane = tid & 31;\n int warp = tid >> 5;\n int k = blockIdx.x * 8 + warp;\n if (k >= K) return;\n float sum = 0.0f;\n const float* wk = w + k * N;\n #pragma unroll\n for (int i = lane; i < N; i += 32) {\n sum = fmaf(sx[i], wk[i], sum);\n }\n #pragma unroll\n for (int off = 16; off > 0; off >>= 1) sum += __shfl_down_sync(0xffffffff, sum, off);\n if (lane == 0) y[b * K + k] = sum + bias[k];\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = torch::empty({B, K}, x.options());\n vga_linear_kernel<<<dim3(7,B), dim3(256)>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>());\n return y;\n}\n'''\n_ext = load_inline(name='vga_linear_ext_sh', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['vga_linear_forward'], verbose=False, extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__(); self.proj = nn.Linear(128,56,bias=True)\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.vga_linear_forward(x, self.proj.weight, self.proj.bias)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=2.38e-07 mean_abs_err=4.59e-08 max_rel_err=3.49e-05
turn 54.7s turn · 4.7s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n__global__ __launch_bounds__(256, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y) {\n __shared__ float sx[N];\n int tid = threadIdx.x;\n int b = blockIdx.y;\n if (tid < N) {\n int r = tid >> 5;\n int h = tid & 31;\n sx[tid] = x[(r * B + b) * H + h];\n }\n __syncthreads();\n int lane = tid & 31;\n int warp = tid >> 5;\n int k = blockIdx.x * 8 + warp;\n if (k >= K) return;\n float sum = 0.0f;\n const float* wk = w + k * N;\n #pragma unroll\n for (int i = lane; i < N; i += 32) sum = fmaf(sx[i], wk[i], sum);\n #pragma unroll\n for (int off = 16; off > 0; off >>= 1) sum += __shfl_down_sync(0xffffffff, sum, off);\n if (lane == 0) y[b * K + k] = sum + bias[k];\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) { auto y=torch::empty({B,K},x.options()); vga_linear_kernel<<<dim3(7,B),dim3(256)>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>()); return y; }\n'''\n_ext = load_inline(name='vga_linear_ext_sh_prof', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['vga_linear_forward'], verbose=False, extra_cuda_cflags=['-O3','--use_fast_math'], extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__(); self.proj=nn.Linear(128,56,bias=True)\n def forward(self,x): return _ext.vga_linear_forward(x,self.proj.weight,self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 37.0μs (100%) calls=1
--- Timing ---
GPU time: 3.65 μs [↑0.2μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 10.6/3350 GB/s (0.3%) [↓0.0% REGRESSED]
read=10.6 write=0.0 GB/s
Hit rates: L2=96.8%
--- Compute Throughput ---
Pipe util: FMA=1.3%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 12.5%
Eligible warps/cycle: 0.04
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=16, smem/blk=1536B, block=256, grid=84
--- Memory Access Quality ---
Global ld coalescing: 3.45 sectors/req (3.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2009.0% ####################
short_scoreboard 341.7% ####################
--- Optimization Hints ---
-> 2009% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.5x (ideal=1.0) -> 3x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (12%). 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: 3.46→3.65μs (↑0.19) REGRESSED
DRAM BW%: 0.33→0.32% (↓0.02) REGRESSED
Ld sectors/req: 3.67→3.45 (↓0.21) ok
L2 hit%: 95.57→96.78% (↑1.21) ok
turn 614.2s turn · 14.2s LLM · 0.0s tools
reasoning
**Evaluating performance factors**
I need to assess the situation with blocks and threads. It looks like there are fewer blocks but with 512 threads in play. I'm curious about how this might affect kernel time—will it improve or worsen? It seems like testing correctness and profiling the performance is essential here. By focusing on these factors, I'll get a clearer picture of the overall performance and make more informed decisions moving forward.
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n__global__ __launch_bounds__(512, 1)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y) {\n int tid=threadIdx.x; int lane=tid&31; int warp=tid>>5; int k=blockIdx.x*16+warp; int b=blockIdx.y; if(k>=K) return;\n float sum=0.0f; const float* wk=w+k*N;\n #pragma unroll\n for(int i=lane;i<N;i+=32){ int r=i>>5; int h=i&31; sum=fmaf(x[(r*B+b)*H+h], wk[i], sum); }\n #pragma unroll\n for(int off=16;off>0;off>>=1) sum += __shfl_down_sync(0xffffffff,sum,off);\n if(lane==0) y[b*K+k]=sum+bias[k];\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){auto y=torch::empty({B,K},x.options()); vga_linear_kernel<<<dim3(4,B),dim3(512)>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>()); return y;}\n'''\n_ext=load_inline(name='vga_linear_ext_16w',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['vga_linear_forward'],verbose=False,extra_cuda_cflags=['-O3','--use_fast_math'],extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__(); self.proj=nn.Linear(128,56,bias=True)\n def forward(self,x): return _ext.vga_linear_forward(x,self.proj.weight,self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 49.0μs (100%) calls=1
--- Timing ---
GPU time: 3.52 μs [↓0.1μs ok]
--- Memory Bandwidth ---
DRAM BW: 10.9/3350 GB/s (0.3%)
read=10.9 write=0.0 GB/s
Hit rates: L2=95.4%
--- Compute Throughput ---
Pipe util: FMA=2.3%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 22.4% [↑10.0% ok]
Eligible warps/cycle: 0.08
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=18, smem/blk=1024B, block=512, grid=48
--- Memory Access Quality ---
Global ld coalescing: 3.67 sectors/req (3.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2384.1% ####################
short_scoreboard 1042.2% ####################
mio_throttle 5.1% #
--- Optimization Hints ---
-> 2384% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.7x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (22%). 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: 3.65→3.52μs (↓0.13) ok
Occupancy: 12.45→22.43% (↑9.98) ok
Eligible warps/cyc: 0.04→0.08 (↑0.04) ok
Ld sectors/req: 3.45→3.67 (↑0.21) REGRESSED
L2 hit%: 96.78→95.42% (↓1.36) REGRESSED
turn 77.3s turn · 7.3s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n__global__ __launch_bounds__(32, 8)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y){\n int lane=threadIdx.x; int k=blockIdx.x; int b=blockIdx.y; float sum=0.0f; const float* wk=w+k*N;\n #pragma unroll\n for(int i=lane;i<N;i+=32){int r=i>>5; int h=i&31; sum=fmaf(x[(r*B+b)*H+h],wk[i],sum);} \n #pragma unroll\n for(int off=16;off>0;off>>=1) sum += __shfl_down_sync(0xffffffff,sum,off);\n if(lane==0) y[b*K+k]=sum+bias[k];\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){auto y=torch::empty({B,K},x.options()); vga_linear_kernel<<<dim3(K,B),dim3(32)>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>()); return y;}\n'''\n_ext=load_inline(name='vga_linear_ext_1warp',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['vga_linear_forward'],verbose=False,extra_cuda_cflags=['-O3','--use_fast_math'],extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__(); self.proj=nn.Linear(128,56,bias=True)\n def forward(self,x): return _ext.vga_linear_forward(x,self.proj.weight,self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 41.0μs (100%) calls=1
--- Timing ---
GPU time: 3.84 μs [↑0.3μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 10.2/3350 GB/s (0.3%) [↓0.0% REGRESSED]
read=10.2 write=0.0 GB/s
Hit rates: L2=95.4%
--- Compute Throughput ---
Pipe util: FMA=1.4%, Tensor=0.1%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 7.6% [↓14.9% REGRESSED]
Eligible warps/cycle: 0.05
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=24, smem/blk=1024B, block=32, grid=672
--- Memory Access Quality ---
Global ld coalescing: 3.67 sectors/req (3.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 1410.1% ####################
short_scoreboard 181.1% ####################
--- Optimization Hints ---
-> 1410% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.7x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (8%) limited by small block (32). 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: 3.52→3.84μs (↑0.32) REGRESSED
DRAM BW%: 0.33→0.30% (↓0.02) REGRESSED
Occupancy: 22.43→7.57% (↓14.86) REGRESSED
Eligible warps/cyc: 0.08→0.05 (↓0.03) REGRESSED
L2 hit%: 95.42→95.36% (↓0.05) REGRESSED
turn 85.0s turn · 5.0s LLM · 0.0s tools
reasoning
**Considering performance optimization**
I'm looking at the original best of 3.46 and thinking about trying 4 warps per block. The grid configuration would be 14 by 12, resulting in 168 blocks, with each block being 128. This should maintain the same total warps. It seems that there might be more CTAs than SM, which could potentially lead to faster performance. Let's see how this adjustment plays out!
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n__global__ __launch_bounds__(128, 4)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y){\n int tid=threadIdx.x; int lane=tid&31; int warp=tid>>5; int k=blockIdx.x*4+warp; int b=blockIdx.y; if(k>=K) return; float sum=0.0f; const float* wk=w+k*N;\n #pragma unroll\n for(int i=lane;i<N;i+=32){int r=i>>5; int h=i&31; sum=fmaf(x[(r*B+b)*H+h],wk[i],sum);} \n #pragma unroll\n for(int off=16;off>0;off>>=1) sum += __shfl_down_sync(0xffffffff,sum,off);\n if(lane==0) y[b*K+k]=sum+bias[k];\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){auto y=torch::empty({B,K},x.options()); vga_linear_kernel<<<dim3(14,B),dim3(128)>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>()); return y;}\n'''\n_ext=load_inline(name='vga_linear_ext_4w',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['vga_linear_forward'],verbose=False,extra_cuda_cflags=['-O3','--use_fast_math'],extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__(); self.proj=nn.Linear(128,56,bias=True)\n def forward(self,x): return _ext.vga_linear_forward(x,self.proj.weight,self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 34.0μs (100%) calls=1
--- Timing ---
GPU time: 3.71 μs [↓0.1μs ok]
--- Memory Bandwidth ---
DRAM BW: 10.3/3350 GB/s (0.3%)
read=10.3 write=0.0 GB/s
Hit rates: L2=95.6%
--- Compute Throughput ---
Pipe util: FMA=0.7%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 7.9% [↑0.4% ok]
Eligible warps/cycle: 0.02
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=18, smem/blk=1024B, block=128, grid=168
--- Memory Access Quality ---
Global ld coalescing: 3.67 sectors/req (3.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2396.7% ####################
short_scoreboard 976.9% ####################
--- Optimization Hints ---
-> 2397% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.7x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (8%). 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: 3.84→3.71μs (↓0.13) ok
Occupancy: 7.57→7.94% (↑0.36) ok
Eligible warps/cyc: 0.05→0.02 (↓0.03) REGRESSED
L2 hit%: 95.36→95.62% (↑0.26) ok
turn 96.2s turn · 6.2s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n#define WPB 7\n__global__ __launch_bounds__(224, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y){\n int tid=threadIdx.x; int lane=tid&31; int warp=tid>>5; int k=blockIdx.x*WPB+warp; int b=blockIdx.y; if(k>=K) return; float sum=0.0f; const float* wk=w+k*N;\n #pragma unroll\n for(int i=lane;i<N;i+=32){int r=i>>5; int h=i&31; sum=fmaf(x[(r*B+b)*H+h],wk[i],sum);} \n #pragma unroll\n for(int off=16;off>0;off>>=1) sum += __shfl_down_sync(0xffffffff,sum,off);\n if(lane==0) y[b*K+k]=sum+bias[k];\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){auto y=torch::empty({B,K},x.options()); vga_linear_kernel<<<dim3(8,B),dim3(224)>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>()); return y;}\n'''\n_ext=load_inline(name='vga_linear_ext_7w',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['vga_linear_forward'],verbose=False,extra_cuda_cflags=['-O3','--use_fast_math'],extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__(); self.proj=nn.Linear(128,56,bias=True)\n def forward(self,x): return _ext.vga_linear_forward(x,self.proj.weight,self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 36.0μs (100%) calls=1
--- Timing ---
GPU time: 3.39 μs [↓0.3μs ok]
--- Memory Bandwidth ---
DRAM BW: 11.3/3350 GB/s (0.3%) [↑0.0% ok]
read=11.3 write=0.0 GB/s
Hit rates: L2=95.1%
--- Compute Throughput ---
Pipe util: FMA=1.3%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 10.8% [↑2.9% ok]
Eligible warps/cycle: 0.04
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=18, smem/blk=1024B, block=224, grid=96
--- Memory Access Quality ---
Global ld coalescing: 3.67 sectors/req (3.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2309.1% ####################
short_scoreboard 976.0% ####################
--- Optimization Hints ---
-> 2309% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.7x (ideal=1.0) -> 4x 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: 3.71→3.39μs (↓0.32) ok
DRAM BW%: 0.31→0.34% (↑0.03) ok
Occupancy: 7.94→10.82% (↑2.89) ok
Eligible warps/cyc: 0.02→0.04 (↑0.02) ok
L2 hit%: 95.62→95.14% (↓0.48) REGRESSED
turn 107.3s turn · 7.3s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n#define WPB 6\n__global__ __launch_bounds__(192, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y){\n int tid=threadIdx.x; int lane=tid&31; int warp=tid>>5; int k=blockIdx.x*WPB+warp; int b=blockIdx.y; if(k>=K) return; float sum=0.0f; const float* wk=w+k*N;\n #pragma unroll\n for(int i=lane;i<N;i+=32){int r=i>>5; int h=i&31; sum=fmaf(x[(r*B+b)*H+h],wk[i],sum);} \n #pragma unroll\n for(int off=16;off>0;off>>=1) sum += __shfl_down_sync(0xffffffff,sum,off);\n if(lane==0) y[b*K+k]=sum+bias[k];\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){auto y=torch::empty({B,K},x.options()); vga_linear_kernel<<<dim3(10,B),dim3(192)>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>()); return y;}\n'''\n_ext=load_inline(name='vga_linear_ext_6w',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['vga_linear_forward'],verbose=False,extra_cuda_cflags=['-O3','--use_fast_math'],extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__(); self.proj=nn.Linear(128,56,bias=True)\n def forward(self,x): return _ext.vga_linear_forward(x,self.proj.weight,self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 39.0μs (100%) calls=1
--- Timing ---
GPU time: 3.46 μs [↑0.1μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 11.1/3350 GB/s (0.3%)
read=11.1 write=0.0 GB/s
Hit rates: L2=95.8%
--- Compute Throughput ---
Pipe util: FMA=1.0%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 8.8% [↓2.0% REGRESSED]
Eligible warps/cycle: 0.03
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=18, smem/blk=1024B, block=192, grid=120
--- Memory Access Quality ---
Global ld coalescing: 3.67 sectors/req (3.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 2319.9% ####################
short_scoreboard 980.1% ####################
--- Optimization Hints ---
-> 2320% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.7x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (9%). 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: 3.39→3.46μs (↑0.06) REGRESSED
Occupancy: 10.82→8.83% (↓1.99) REGRESSED
Eligible warps/cyc: 0.04→0.03 (↓0.01) REGRESSED
L2 hit%: 95.14→95.83% (↑0.69) ok
turn 115.1s turn · 5.1s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n#define WPB 14\n__global__ __launch_bounds__(448, 1)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y){\n int tid=threadIdx.x; int lane=tid&31; int warp=tid>>5; int k=blockIdx.x*WPB+warp; int b=blockIdx.y; float sum=0.0f; const float* wk=w+k*N;\n #pragma unroll\n for(int i=lane;i<N;i+=32){int r=i>>5; int h=i&31; sum=fmaf(x[(r*B+b)*H+h],wk[i],sum);} \n #pragma unroll\n for(int off=16;off>0;off>>=1) sum += __shfl_down_sync(0xffffffff,sum,off);\n if(lane==0) y[b*K+k]=sum+bias[k];\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){auto y=torch::empty({B,K},x.options()); vga_linear_kernel<<<dim3(4,B),dim3(448)>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>()); return y;}\n'''\n_ext=load_inline(name='vga_linear_ext_14w',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['vga_linear_forward'],verbose=False,extra_cuda_cflags=['-O3','--use_fast_math'],extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__(); self.proj=nn.Linear(128,56,bias=True)\n def forward(self,x): return _ext.vga_linear_forward(x,self.proj.weight,self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 43.0μs (100%) calls=1
--- Timing ---
GPU time: 3.46 μs
--- Memory Bandwidth ---
DRAM BW: 11.1/3350 GB/s (0.3%)
read=11.1 write=0.0 GB/s
Hit rates: L2=95.4%
--- Compute Throughput ---
Pipe util: FMA=2.6%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 21.5% [↑12.7% ok]
Eligible warps/cycle: 0.07
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=18, smem/blk=1024B, block=448, grid=48
--- Memory Access Quality ---
Global ld coalescing: 3.67 sectors/req (3.7x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 3280.0% ####################
short_scoreboard 278.4% ####################
mio_throttle 92.6% ##################
--- Optimization Hints ---
-> 3280% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=3.7 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=3.7x (ideal=1.0) -> 4x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (22%). 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 ---
Occupancy: 8.83→21.54% (↑12.71) ok
Eligible warps/cyc: 0.03→0.07 (↑0.04) ok
L2 hit%: 95.83→95.40% (↓0.44) REGRESSED
turn 1212.6s turn · 12.6s LLM · 0.0s tools
call → profile_kernel
callprofile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n__global__ __launch_bounds__(64, 4)\nvoid vga_linear_kernel(const float* __restrict__ x,const float* __restrict__ w,const float* __restrict__ bias,float* __restrict__ y){\n int k=threadIdx.x; int b=blockIdx.x; if(k>=K) return; float sum=bias[k]; const float* wk=w+k*N;\n #pragma unroll\n for(int r=0;r<4;r++){\n const float* xr=x+(r*B+b)*H;\n #pragma unroll\n for(int h=0;h<32;h++) sum=fmaf(xr[h], wk[r*H+h], sum);\n }\n y[b*K+k]=sum;\n}\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias){auto y=torch::empty({B,K},x.options()); vga_linear_kernel<<<dim3(B),dim3(64)>>>(x.data_ptr<float>(),weight.data_ptr<float>(),bias.data_ptr<float>(),y.data_ptr<float>()); return y;}\n'''\n_ext=load_inline(name='vga_linear_ext_threadk',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['vga_linear_forward'],verbose=False,extra_cuda_cflags=['-O3','--use_fast_math'],extra_cflags=['-O3'])\nclass ModelNew(nn.Module):\n def __init__(self): super().__init__(); self.proj=nn.Linear(128,56,bias=True)\n def forward(self,x): return _ext.vga_linear_forward(x,self.proj.weight,self.proj.bias)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 30.0μs (100%) calls=1
--- Timing ---
GPU time: 7.36 μs [↑3.9μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 6.0/3350 GB/s (0.2%) [↓0.2% REGRESSED]
read=6.0 write=0.0 GB/s
Hit rates: L2=97.0%
--- Compute Throughput ---
Pipe util: FMA=0.6%, Tensor=0.0%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 3.1% [↓18.4% REGRESSED]
Eligible warps/cycle: 0.04
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=164, smem/blk=1024B, block=64, grid=12
--- Memory Access Quality ---
Global ld coalescing: 14.46 sectors/req (14.5x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 1078.8% ####################
short_scoreboard 8.1% #
--- Optimization Hints ---
-> 1079% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=14.5 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=14.5x (ideal=1.0) -> 14x wasted DRAM BW. Make per-warp accesses contiguous.
-> Low occupancy (3%) limited by registers (164/thread), small block (64). 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: 3.46→7.36μs (↑3.90) REGRESSED
DRAM BW%: 0.33→0.18% (↓0.15) REGRESSED
Occupancy: 21.54→3.12% (↓18.43) REGRESSED
Eligible warps/cyc: 0.07→0.04 (↓0.03) REGRESSED
Ld sectors/req: 3.67→14.46 (↑10.79) REGRESSED
L2 hit%: 95.40→96.96% (↑1.57) ok
turn 139.5s turn · 9.5s LLM · 0.0s tools
call → static_check
callstatic_check
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n#define WPB 7\n\n__global__ __launch_bounds__(224, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ y) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n int k = blockIdx.x * WPB + warp;\n int b = blockIdx.y;\n\n float sum = 0.0f;\n const float* wk = w + k * N;\n\n #pragma unroll\n for (int i = lane; i < N; i += 32) {\n int r = i >> 5;\n int h = i & 31;\n sum = fmaf(x[(r * B + b) * H + h], wk[i], sum);\n }\n\n #pragma unroll\n for (int off = 16; off > 0; off >>= 1) {\n sum += __shfl_down_sync(0xffffffff, sum, off);\n }\n\n if (lane == 0) {\n y[b * K + k] = sum + bias[k];\n }\n}\n\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = torch::empty({B, K}, x.options());\n vga_linear_kernel<<<dim3(8, B), dim3(224)>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>());\n return y;\n}\n'''\n\n_ext = load_inline(\n name='vga_linear_final_ext',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['vga_linear_forward'],\n verbose=False,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.proj = nn.Linear(4 * 32, 56, bias=True)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.vga_linear_forward(x, self.proj.weight, self.proj.bias)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 145.7s turn · 5.7s LLM · 0.0s tools · FINAL
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define B 12\n#define H 32\n#define K 56\n#define N 128\n#define WPB 7\n\n__global__ __launch_bounds__(224, 2)\nvoid vga_linear_kernel(const float* __restrict__ x,\n const float* __restrict__ w,\n const float* __restrict__ bias,\n float* __restrict__ y) {\n int tid = threadIdx.x;\n int lane = tid & 31;\n int warp = tid >> 5;\n int k = blockIdx.x * WPB + warp;\n int b = blockIdx.y;\n\n float sum = 0.0f;\n const float* wk = w + k * N;\n\n #pragma unroll\n for (int i = lane; i < N; i += 32) {\n int r = i >> 5;\n int h = i & 31;\n sum = fmaf(x[(r * B + b) * H + h], wk[i], sum);\n }\n\n #pragma unroll\n for (int off = 16; off > 0; off >>= 1) {\n sum += __shfl_down_sync(0xffffffff, sum, off);\n }\n\n if (lane == 0) {\n y[b * K + k] = sum + bias[k];\n }\n}\n\ntorch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {\n auto y = torch::empty({B, K}, x.options());\n vga_linear_kernel<<<dim3(8, B), dim3(224)>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>());\n return y;\n}\n'''\n\n_ext = load_inline(\n name='vga_linear_final_ext',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['vga_linear_forward'],\n verbose=False,\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n extra_cflags=['-O3'],\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super().__init__()\n self.proj = nn.Linear(4 * 32, 56, bias=True)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return _ext.vga_linear_forward(x, self.proj.weight, self.proj.bias)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 0.01 μs
Runtime stats: mean=0.01μs median=?μs std=0.00μs
Numerical precision: max_abs_err=2.38e-07 mean_abs_err=4.59e-08 max_rel_err=3.49e-05
Memory: 33.06 MB peak (ref: 33.07 MB, ratio: 0.9998x)
Kernel launches: 1 (ref: 14, fusion ratio: 14.0)
SOL score: 0.0032
final submitted kernel (76 lines, 2,004 chars) — full source, untruncated
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
_cpp_src = r'''
#include <torch/extension.h>
torch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias);
'''
_cuda_src = r'''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#define B 12
#define H 32
#define K 56
#define N 128
#define WPB 7
__global__ __launch_bounds__(224, 2)
void vga_linear_kernel(const float* __restrict__ x,
const float* __restrict__ w,
const float* __restrict__ bias,
float* __restrict__ y) {
int tid = threadIdx.x;
int lane = tid & 31;
int warp = tid >> 5;
int k = blockIdx.x * WPB + warp;
int b = blockIdx.y;
float sum = 0.0f;
const float* wk = w + k * N;
#pragma unroll
for (int i = lane; i < N; i += 32) {
int r = i >> 5;
int h = i & 31;
sum = fmaf(x[(r * B + b) * H + h], wk[i], sum);
}
#pragma unroll
for (int off = 16; off > 0; off >>= 1) {
sum += __shfl_down_sync(0xffffffff, sum, off);
}
if (lane == 0) {
y[b * K + k] = sum + bias[k];
}
}
torch::Tensor vga_linear_forward(torch::Tensor x, torch::Tensor weight, torch::Tensor bias) {
auto y = torch::empty({B, K}, x.options());
vga_linear_kernel<<<dim3(8, B), dim3(224)>>>(x.data_ptr<float>(), weight.data_ptr<float>(), bias.data_ptr<float>(), y.data_ptr<float>());
return y;
}
'''
_ext = load_inline(
name='vga_linear_final_ext',
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=['vga_linear_forward'],
verbose=False,
extra_cuda_cflags=['-O3', '--use_fast_math'],
extra_cflags=['-O3'],
)
class ModelNew(nn.Module):
def __init__(self):
super().__init__()
self.proj = nn.Linear(4 * 32, 56, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return _ext.vga_linear_forward(x, self.proj.weight, self.proj.bias)