jensandClaude Sonnet 4.6
3edc124a5c
- Moved src/tests/ → tests/ - Added test_* functions with assertions to script-style test files - Added main() to each so IDEs offer it as a separate run target from pytest - Fixed cupy_test.py: remove spurious x_gpu += x_cpu, fix duplicate xlabel Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
107 lines
2.2 KiB
Python
107 lines
2.2 KiB
Python
import time
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import numpy as np
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import cupy as cp
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from matplotlib import pyplot as plt
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def warmup_cupy():
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a = cp.random.random((10,10))
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b = cp.random.random((10,10))
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_ = cp.dot(a, b)
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def calc_cpu(a: np.array, b: np.array):
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return np.dot(a, b)
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def calc_gpu(a: np.array, b: np.array):
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return cp.dot(a, b)
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def perf_test_2():
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### Numpy and CPU
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print("Creation")
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s = time.time()
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x_cpu = np.ones((1000, 1000, 1000))
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e = time.time()
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time_cpu = e - s
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s = time.time()
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x_gpu = cp.ones((1000, 1000, 1000))
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cp.cuda.Stream.null.synchronize()
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e = time.time()
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time_gpu = e - s
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print(f"Speedup: {time_cpu/time_gpu}")
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### Numpy and CPU
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print("Multiply with constant")
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s = time.time()
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x_cpu *= 5
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e = time.time()
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time_cpu = e - s
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s = time.time()
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x_gpu *= 5
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cp.cuda.Stream.null.synchronize()
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e = time.time()
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time_gpu = e - s
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print(f"Speedup: {time_cpu/time_gpu}")
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### Numpy and CPU
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print("Multiply with constant, Square, Square")
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s = time.time()
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x_cpu *= 5
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x_cpu *= x_cpu
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x_cpu += x_cpu
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e = time.time()
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time_cpu = e - s
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s = time.time()
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x_gpu *= 5
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x_gpu *= x_gpu
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x_gpu += x_gpu
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cp.cuda.Stream.null.synchronize()
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e = time.time()
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time_gpu = e - s
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print(f"Speedup: {time_cpu/time_gpu}")
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def perf_test_1():
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n_values = []
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np_times = []
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cp_times = []
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ratio_times = []
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for N in range(100, 5100, 100):
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a_np = np.random.rand(1024, N)
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b_np = np.random.rand(N, 1024)
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a_cp = cp.asarray(a_np)
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b_cp = cp.asarray(b_np)
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start_time = time.time()
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c_np = calc_cpu(a_np, b_np)
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end_time = time.time()
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numpy_time = end_time - start_time
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start_time = time.time()
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c_cp = calc_gpu(a_cp, b_cp)
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c_np = cp.asnumpy(c_cp)
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end_time = time.time()
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cupy_time = end_time - start_time
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n_values.append(N)
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np_times.append(numpy_time)
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cp_times.append(cupy_time)
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ratio_times.append(numpy_time/cupy_time)
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plt.plot(n_values, np_times, label='numpy time')
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plt.plot(n_values, cp_times, label='cupy time')
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plt.plot(n_values, ratio_times, label='numpy/cupy time')
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plt.xlabel("Matrix size [N]")
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plt.ylabel("Time [s]")
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plt.title("Performance numpy vs cupy")
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plt.grid()
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plt.legend()
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plt.show()
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def main():
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warmup_cupy()
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perf_test_2()
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if __name__ == "__main__":
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main() |