GTC Silicon Valley-2019: Performance Analysis for Large-Scale GPU-Accelerated Applications and DL Frameworks
GTC Silicon Valley-2019 ID:S9347:Performance Analysis for Large-Scale GPU-Accelerated Applications and DL Frameworks
Robert Henschel(Indiana University),Guido Juckeland(HelmholtzZentrum DresdenRossendorf)
Get your hands on the latest versions of Score-P and Vampir to profile the execution behavior of your large-scale GPU-Accelerated applications. See how these HPC community tools pick up as other tools (such as NVVP) drop off when your application spans multiple compute nodes. Regardless of whether your application uses CUDA, OpenACC, OpenMP or OpenCL for acceleration, or whether it is written in C, C++, Fortran or Python, you will receive a high-resolution timeline view of all program activity alongside the standard profiles to identify hot spots and avenues for optimization. The novel Python support now also enables performance studies for optimizing the inner workings of deep learning frameworks.