We introduce an ODE solver for the PyTorch ecosystem that can solve multiple ODEs in parallel independently from each other while achieving significant performance gains. Our implementation tracks each ODE's progress separately and is carefully optimized for GPUs and compatibility with PyTorch's JIT compiler. Its design lets researchers easily augment any aspect of the solver and collect and analyze internal solver statistics. In our experiments, our implementation is up to 4.3 times faster per step than other ODE solvers and it is robust against within-batch interactions that lead other solvers to take up to 4 times as many steps.
翻译：我们为PyTorrch 生态系统引入了 ODE 求解器, 该解解码器可以相互独立地平行解决多个解码器, 同时取得显著的绩效收益。 我们的落实程序可以分别跟踪每个解码器的进展, 并且对 GPU 和 PyTorch JIT 编译器的兼容性进行谨慎优化。 它的设计可以让研究人员轻松地增强解码器的任何方面, 收集和分析内部解码数据。 在我们的实验中, 我们的落实速度是其它解码器的4.3 倍, 并且它能够抵御内部的交互作用, 从而引导其他解码器采取多达4倍的步骤。
相关内容
微信扫码咨询专知VIP会员