author	 = {Anja Gerbes and Nabeeh Jumah and Julian Kunkel},
	title	 = {{Automatic Profiling for Climate Modeling}},
	year	 = {2018},
	month	 = {04},
	location	 = {Bristol, United Kingdom},
	activity	 = {Euro LLVM},
	abstract	 = {Some applications are time consuming like climate modeling, which include lengthy simulations. Hence, coding is sensitive for performance. Spending more time on optimization of specific code parts can improve total performance. Profiling an application is a well-known technique to do that. Many tools are available for developers to get performance information about their code. With our provided python package Performance Analysis and Source-Code Instrumentation Toolsuite (PASCIT) is a automatic instrumentation of an user’s source code possible. Developers mark the parts that they need performance information about. We present an effort to profile climate modeling codes with two alternative methods: • usage of GGDML translation tool to mark directly the computational kernels of an application for profiling. • usage of GGDML translation tool to generate a serial code in a first step and then use LLVM/Clang to instrument some code parts with a profiler’s directives. The resulting codes are profiled with the LIKWID profiler. Alternatively, we use perf and OProfile’s ocount \& operf to measure hardware characteristics. The performance report with a visualization of the measured hardware performance counters in generating Radar Charts, Latex Tables, Box Plots are interesting for scientist to understand the bottlenecks of their codes.},
	url	 = {},