Energy consumption in High Performance Computing has become a major topic. Thus various approaches to improve the performance per watt have been developed. One way is to instrument an application with instructions that change the idle and performance states of the hardware. The major purpose of this thesis is to demonstrate the potential savings by instrumenting parallel message passing applications. For successful instrumentation critical regions in terms of performance and power consumption have to be identified. Most scientific applications can be divided into phases that utilize different parts of the hardware. The goal is to conserve energy by switching the hardware to different states depending on the workload in a specific phase. To identify those phases two tracing tools are used. Two examples will be instrumented: a parallel earth simulation model written in Fortran and a parallel partial differential equation solver written in C. Instrumented applications should consume less energy but may also show a increase in runtime. It is discussed if it is worthwhile to make a compromise in that case. The applications are analyzed and instrumented on two x64 architectures. Differences concerning runtime and power consumption are investigated.

@misc{EIOPMAE12,
	author	 = {Florian Ehmke},
	title	 = {{Energy-Aware Instrumentation of Parallel MPI Applications}},
	advisors	 = {Thomas Ludwig and Timo Minartz},
	year	 = {2012},
	month	 = {06},
	school	 = {Universität Hamburg},
	type	 = {Bachelor's Thesis},
	abstract	 = {Energy consumption in High Performance Computing has become a major topic. Thus
			various approaches to improve the performance per watt have been developed. One way is to
			instrument an application with instructions that change the idle and performance states of the
			hardware. The major purpose of this thesis is to demonstrate the potential savings by
			instrumenting parallel message passing applications. For successful instrumentation critical
			regions in terms of performance and power consumption have to be identified. Most scientific
			applications can be divided into phases that utilize different parts of the hardware. The goal
			is to conserve energy by switching the hardware to different states depending on the workload
			in a specific phase. To identify those phases two tracing tools are used. Two examples will be
			instrumented: a parallel earth simulation model written in Fortran and a parallel partial
			differential equation solver written in C. Instrumented applications should consume less
			energy but may also show a increase in runtime. It is discussed if it is worthwhile to make a
			compromise in that case. The applications are analyzed and instrumented on two x64
			architectures. Differences concerning runtime and power consumption are investigated.},
}