diff --git a/S1/gsd123_#163/prompt.txt b/S1/gsd123_#163/prompt.txt
new file mode 100644
index 00000000..83ba8d39
--- /dev/null
+++ b/S1/gsd123_#163/prompt.txt
@@ -0,0 +1,50 @@
+You write custom CUDA kernels to replace the pytorch operators in the given GeGLU architecture to get speedups.
+
+You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining chunk+gelu+elementwise_mul), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.
+
+CUDA C++ kernel for Russell‑Rao dissimilarity with exponential transformation
+
+Block‑parallel per‑sample processing: each block handles one batch element
+
+Thread‑wise accumulation of element‑wise minimums (local_inter)
+
+Parallel reduction in shared memory using binary tree approach
+
+Russell‑Rao formula: (dim – intersection) / dim where intersection = ∑ min(xᵢ, yᵢ)
+
+Exponential activation: exp(value) applied to the dissimilarity score
+
+Grid‑stride memory access for coalesced reads
+
+PyTorch inline C++/CUDA extension via load_inline
+
+
+
+Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:
+import torch
+import torch.nn as nn
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+    def forward(self, x, y):
+        n = x.size(1)
+        intersection = torch.min(x, y).sum(dim=1)
+        resistance = (n - intersection) / n
+        return torch.exp(resistance).mean()
+
+
+batch_size = 16
+input_dim = 1024
+
+
+def get_inputs():
+    x = torch.randn(batch_size, input_dim).abs()  # Ensure positive for meaningful fuzzy operations
+    y = torch.randn(batch_size, input_dim).abs()
+    return [x, y]
+
+
+def get_init_inputs():
+    return []
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diff --git a/S1/gsd123_#163/resistance_distance_exp_cuda.py b/S1/gsd123_#163/resistance_distance_exp_cuda.py
new file mode 100644
index 00000000..239760b3
--- /dev/null
+++ b/S1/gsd123_#163/resistance_distance_exp_cuda.py
@@ -0,0 +1,77 @@
+import os
+import torch
+import torch.nn as nn
+from torch.utils.cpp_extension import load_inline
+
+cuda_source = """
+#include <torch/extension.h>
+#include <cuda_runtime.h>
+
+__global__ void resistance_distance_exp_kernel(const float* x, const float* y, float* out, int dim) {
+    int bid = blockIdx.x;
+    int tid = threadIdx.x;
+
+    const float* row_x = x + bid * dim;
+    const float* row_y = y + bid * dim;
+
+    float local_inter = 0.0f;
+
+    for (int i = tid; i < dim; i += blockDim.x) {
+        local_inter += fminf(row_x[i], row_y[i]);
+    }
+
+    __shared__ float s_inter[256];
+    s_inter[tid] = local_inter;
+    __syncthreads();
+
+    for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
+        if (tid < stride) {
+            s_inter[tid] += s_inter[tid + stride];
+        }
+        __syncthreads();
+    }
+
+    if (tid == 0) {
+        float intersection = s_inter[0];
+        // Russell-Rao formula: (n - intersection) / n
+        float val = ((float)dim - intersection) / (float)dim;
+        out[bid] = expf(val);
+    }
+}
+
+torch::Tensor resistance_distance_exp_cuda(torch::Tensor x, torch::Tensor y) {
+    int batch_size = x.size(0);
+    int dim = x.size(1);
+    auto out = torch::empty({batch_size}, x.options());
+
+    resistance_distance_exp_kernel<<<batch_size, 256>>>(
+        x.data_ptr<float>(),
+        y.data_ptr<float>(),
+        out.data_ptr<float>(),
+        dim
+    );
+
+    return out;
+}
+"""
+
+cpp_source = "torch::Tensor resistance_distance_exp_cuda(torch::Tensor x, torch::Tensor y);"
+
+module = load_inline(
+    name="resistance_distance_exp_ext",
+    cpp_sources=cpp_source,
+    cuda_sources=cuda_source,
+    functions=["resistance_distance_exp_cuda"],
+    verbose=False,
+    with_cuda=True
+)
+
+
+class ModelNew(nn.Module):
+    def __init__(self):
+        super(ModelNew, self).__init__()
+        self.op = module
+
+    def forward(self, x, y):
+        res = self.op.resistance_distance_exp_cuda(x.contiguous(), y.contiguous())
+        return res.mean()
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diff --git a/S1/gsd123_#163/resistance_distance_exp_torch.py b/S1/gsd123_#163/resistance_distance_exp_torch.py
new file mode 100644
index 00000000..9c4b6312
--- /dev/null
+++ b/S1/gsd123_#163/resistance_distance_exp_torch.py
@@ -0,0 +1,27 @@
+import torch
+import torch.nn as nn
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+    def forward(self, x, y):
+        n = x.size(1)
+        intersection = torch.min(x, y).sum(dim=1)
+        resistance = (n - intersection) / n
+        return torch.exp(resistance).mean()
+
+
+batch_size = 16
+input_dim = 1024
+
+
+def get_inputs():
+    x = torch.randn(batch_size, input_dim).abs()  # Ensure positive for meaningful fuzzy operations
+    y = torch.randn(batch_size, input_dim).abs()
+    return [x, y]
+
+
+def get_init_inputs():
+    return []
\ No newline at end of file
diff --git a/S1/gsd123_#163/run_code.py b/S1/gsd123_#163/run_code.py
new file mode 100644
index 00000000..15b2b689
--- /dev/null
+++ b/S1/gsd123_#163/run_code.py
@@ -0,0 +1,77 @@
+###########################################################
+# 性能和精度验证程序
+###########################################################
+import torch
+import torch.nn as nn
+import time
+from resistance_distance_exp_torch import Model, get_inputs, get_init_inputs
+from resistance_distance_exp_cuda  import ModelNew
+
+
+def run_benchmark():
+    # 检查 CUDA 是否可用
+    if not torch.cuda.is_available():
+        print("CUDA 不可用，请确保您有可用的 NVIDIA GPU 并已正确安装 PyTorch CUDA 版本。")
+        return
+    else:
+        device = torch.device("cuda")
+
+    # 初始化模型
+    init_inputs = get_init_inputs()
+    init_inputs = [
+        x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs
+    ]
+    inputs = get_inputs()
+    inputs = [
+        x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs
+    ]
+
+    torch_model = Model(*init_inputs).cuda()
+    cuda_model = ModelNew(*init_inputs).cuda()
+
+    torch_model.eval()
+    cuda_model.eval()
+
+    print("-------------------- 精度对齐验证 --------------------")
+    with torch.no_grad():
+        output_torch = torch_model(*inputs)
+        output_cuda = cuda_model(*inputs)
+
+    precision_flag = torch.allclose(output_torch, output_cuda, rtol=1e-03)
+    if precision_flag:
+        print("✅ 精度对齐：两个模型的输出结果非常接近。")
+    else:
+        print("❌ 精度不一致！")
+
+    print("\n-------------------- 性能加速比测试 --------------------")
+    num_iterations = 100
+
+    # PyTorch 模型计时
+    torch.cuda.synchronize()
+    start_time = time.time()
+    for _ in range(num_iterations):
+        _ = torch_model(*inputs)
+    torch.cuda.synchronize()
+    torch_time = (time.time() - start_time) / num_iterations
+
+    # 自定义 CUDA 内核计时
+    torch.cuda.synchronize()
+    start_time = time.time()
+    for _ in range(num_iterations):
+        _ = cuda_model(*inputs)
+    torch.cuda.synchronize()
+    cuda_time = (time.time() - start_time) / num_iterations
+
+    print(f"PyTorch torch.relu 平均执行时间: {torch_time:.6f} 秒")
+    print(f"自定义 CUDA 内核 平均执行时间: {cuda_time:.6f} 秒")
+    speedup = 0
+    if cuda_time > 0:
+        speedup = torch_time / cuda_time
+        print(f"加速比 (Speedup): {speedup:.2f}x")
+    else:
+        print("CUDA 内核执行时间为0，无法计算加速比。")
+    return precision_flag, speedup
+
+
+if __name__ == "__main__":
+    precision_flag, speedup = run_benchmark()
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