A performance analysis framework for exploiting GPU microarchitectural capability

GPUs are widely used in accelerating deep neural networks (DNNs) for their high bandwidth and parallelism. But tuning the performance of DNN computations is challenging, as it requires a thorough understanding of both underlying architectures and algorithm implementations. Traditional research, which focused on analyzing performance by CUDA C language or PTX instructions, has not combined hardware features tightly with source code. In this...