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PD_Sathya's questions

Martin Hope
PD_Sathya
Asked: 2024-10-20 07:01:30 +0800 CST
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我在启用了高内存的 google collab CPU 上运行了以下基准测试代码。请指出我在基准测试过程中的任何错误（如果有），以及为什么 tinygrad 的性能提升如此之高。

# Set the size of the matrices
size = 10000

# Generate a random 10000x10000 matrix with NumPy
np_array = np.random.rand(size, size)

# Generate a random 10000x10000 matrix with PyTorch
torch_tensor = torch.rand(size, size)

# Generate a random 10000x10000 matrix with TinyGrad
tg_tensor = Tensor.rand(size, size)  

# Benchmark NumPy
start_np = time.time()
np_result = np_array @ np_array  # Matrix multiplication
np_time = time.time() - start_np
print(f"NumPy Time: {np_time:.6f} seconds")

# Benchmark PyTorch
start_torch = time.time()
torch_result = torch_tensor @ torch_tensor  # Matrix multiplication
torch_time = time.time() - start_torch
print(f"PyTorch Time: {torch_time:.6f} seconds")

# Benchmark TinyGrad
start_tg = time.time()
tg_result = tg_tensor @ tg_tensor  # Matrix multiplication
tg_time = time.time() - start_tg
print(f"TinyGrad Time: {tg_time:.6f} seconds")
  • NumPy 时间：11.977072 秒
  • PyTorch 时间：7.905509 秒
  • TinyGrad 时间：0.000607 秒

这些就是结果。多次运行代码后，结果非常相似

python