Spotting HPC and Exascale Bottlenecks with TAU CPU/GPU/MPI Profiler

Programmers cannot blindly guess which sections of their code might bottleneck performance. This problem is worsened when codes run across the variety of hardware platforms supported by the Exascale Computing Project (ECP). A section of code that runs well on one system might be a bottleneck on another system. Differing hardware execution models further compound the performance challenges that face application developers; these models can include the somewhat restricted SIMD (Single Instruction Multiple Data) and SIMT (Single Instruction Multiple Thread) computing for GPU models and the more complex and general MIMD (Multiple Instruction Multiple Data) for CPUs. New software programming models, such as Kokkos, also introduce multiple layers of abstraction and lambda functions that can hide or obscure the low-level execution details due to their complexity and anonymous nature. Differing memory systems inside a node and differences in the communications fabric that connect high-performance computing (HPC) nodes in a distributed supercomputer environment add even greater challenges in identifying performance bottlenecks during application performance analysis.

The post Spotting HPC and Exascale Bottlenecks with TAU CPU/GPU/MPI Profiler appeared first on insideHPC.