Benchmarking operations in deep learning frameworks highlights the significant performance costs associated with implicit blockers, such as datatype conversions to synchronous environments. For example, computing a dot product b = np.dot(a, a) and explicitly converting the resulting tensor to a NumPy array via b.asnumpy() acts as an implicit blocker, taking around $0.0340$ seconds to execute. Similarly, converting the resulting tensor to a standard Python scalar via b.sum().item() serves as another implicit blocker, taking $0.0445$ seconds. These operations are inherently blocking because the synchronous NumPy and Python environments must wait for the actual values to be fully computed by the asynchronous backend before proceeding.