nvidia-cuda-cupti-cu12

import numpy as np from numba import cuda from cupti import cupti @cuda.jit def vector_add(A, B, C): idx = cuda.grid(1) if idx < A.size: C[idx] = A[idx] + B[idx] def func_buffer_requested(): buffer_size = 8 * 1024 * 1024 # 8MB buffer max_num_records = 0 return buffer_size, max_num_records def func_buffer_completed(activities: list): for activity in activities: if activity.kind == cupti.ActivityKind.CONCURRENT_KERNEL: print(f"Kernel Name: {activity.name}") print(f"Kernel Duration (ns): {activity.end - activity.start}") # Initialize data vector_length = 1024 * 1024 A = np.random.rand(vector_length) B = np.random.rand(vector_length) C = np.zeros_like(A) threads_per_block = 128 blocks_per_grid = (vector_length + (threads_per_block - 1)) // threads_per_block # Register CUPTI callbacks cupti.activity_register_callbacks(func_buffer_requested, func_buffer_completed) # Enable CUPTI activity collection for concurrent kernels cupti.activity_enable(cupti.ActivityKind.CONCURRENT_KERNEL) # Launch kernel vector_add[blocks_per_grid, threads_per_block](A, B, C) cuda.synchronize() # Flush and disable CUPTI activity cupti.activity_flush() cupti.activity_disable(cupti.ActivityKind.CONCURRENT_KERNEL)