论文标题
基于价值功能的深度学习工作负载优化
Value Function Based Performance Optimization of Deep Learning Workloads
论文作者
论文摘要
随着机器学习技术变得无处不在，神经网络实现的效率变得相应至关重要。诸如卤化物和TVM之类的框架将网络的算法表示与确定其实施的时间表分开。但是，找到良好的时间表仍然极具挑战性。我们将这个调度问题建模为一系列优化选择，并提出一种新技术，以准确预测部分时间表的预期性能。通过利用这些预测，我们可以制定这些优化决策，并迅速确定有效的时间表。这使我们能够找到将深神经网络吞吐量提高2.6倍的时间表，而在TVM上则是1.5倍。此外，我们的技术比这些工具的技术要快两到三个数量级，并且在几秒钟内而不是数小时内完成。
As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount. Frameworks, such as Halide and TVM, separate out the algorithmic representation of the network from the schedule that determines its implementation. Finding good schedules, however, remains extremely challenging. We model this scheduling problem as a sequence of optimization choices, and present a new technique to accurately predict the expected performance of a partial schedule. By leveraging these predictions we can make these optimization decisions greedily and rapidly identify an efficient schedule. This enables us to find schedules that improve the throughput of deep neural networks by 2.6x over Halide and 1.5x over TVM. Moreover, our technique is two to three orders of magnitude faster than that of these tools, and completes in seconds instead of hours.
