Instruction Sampling

GPA is a performance advisor for NVIDIA GPUs that suggests potential code optimization opportunities at a hierarchy of levels, including individual lines, loops, and functions. GPA uses data flow analysis to approximately attribute measured instruction stalls to their root causes and uses information about a program’s structure and the GPU to match inefficiency patterns with suggestions for optimization. GPA estimates each optimization’s speedup based on a PC sampling-based performance model.

The US Department of Energy’s fastest supercomputers and forthcoming exascale systems employ Graphics Processing Units (GPUs) to …

As we near the end of Moore’s law scaling, the next-generation computing platforms are increasingly exploring heterogeneous …

Using HPCToolkit to Measure and Analyze the Performance of GPU-accelerated Applications Tutorial

Presented our work on GPU performance advisor at CGO21