Identifying Optimization Opportunities Within Kernel Execution in GPU Codes.

Tuning codes for GPGPU architectures is challenging because few performance tools can pinpoint the exact causes of execution bottlenecks. While profiling applications can reveal execution behavior with a particular architecture, the abundance of collected information can also overwhelm the user. Moreover, performance counters provide cumulative values but does not attribute events to code regions, whichmakes identifying performance hot spots difficult. This research focuses on characterizing the behavior of GPU application kernels and its performance at the node level by providing a visualization and metrics display that indicates the behavior of the application with respect to the underlying architecture. We demonstrate the effectiveness of our techniques with LAMMPS and LULESH application case studies on a variety of GPU architectures. By sampling instruction mixes for kernel execution runs, we reveal a variety of intrinsic program characteristics relating to computation, memory and control flow.