Throughout the last years, the trend in HPC is towards heterogeneous cluster architectures that make use of accelerators to speed up computations. For this purpose, many current HPC systems are equipped with Graphics Processing Units (GPUs). These deliver a high floating-point performance, which is important to accelerate compute-intensive applications. This thesis aims to analyze the suitability of CPUs and GPUs for graph algorithms, which can be classified as data-intensive applications. These types of applications perform fewer computations per data element and strongly rely on fast memory access. The analysis is based on two multi-node implementations of the Graph500 benchmark, which execute a number of Breadth-first Searches (BFS) on a large-scale graph. To enable a fair comparison, the same parallel BFS algorithm has been implemented for the CPU and the GPU version. The final evaluation does not only include the performance results but the programming effort that was necessary to achieve the result as well as the cost and energy efficiency. Comparable performance results have been found for both the versions of Graph500, but a significant difference in the programming effort has been detected. The main reason for the high programming effort of the GPU implementation is that complex optimizations are necessary to achieve an acceptable performance in the first place. These require detailed knowledge of the GPU hardware architecture. All in all, the results of this thesis lead to the conclusion that the higher energy efficiency and, depending on the point of view, cost efficiency of the GPUs do not outweigh the lower programming effort for the implementation of graph algorithms on CPUs.

@misc{SAOGACFGAT16,
	author	 = {Kristina Tesch},
	title	 = {{Suitability Analysis of GPUs and CPUs for Graph Algorithms}},
	advisors	 = {Michael Kuhn},
	year	 = {2016},
	month	 = {09},
	school	 = {Universität Hamburg},
	howpublished	 = {{Online \url{https://wr.informatik.uni-hamburg.de/_media/research:theses:kristina_tesch_suitability_analysis_of_gpus_and_cpus_for_graph_algorithms.pdf}}},
	type	 = {Bachelor's Thesis},
	abstract	 = {Throughout the last years, the trend in HPC is towards heterogeneous cluster architectures that make use of accelerators to speed up computations. For this purpose, many current HPC systems are equipped with Graphics Processing Units (GPUs). These deliver a high floating-point performance, which is important to accelerate compute-intensive applications. This thesis aims to analyze the suitability of CPUs and GPUs for graph algorithms, which can be classified as data-intensive applications. These types of applications perform fewer computations per data element and strongly rely on fast memory access. The analysis is based on two multi-node implementations of the Graph500 benchmark, which execute a number of Breadth-first Searches (BFS) on a large-scale graph. To enable a fair comparison, the same parallel BFS algorithm has been implemented for the CPU and the GPU version. The final evaluation does not only include the performance results but the programming effort that was necessary to achieve the result as well as the cost and energy efficiency. Comparable performance results have been found for both the versions of Graph500, but a significant difference in the programming effort has been detected. The main reason for the high programming effort of the GPU implementation is that complex optimizations are necessary to achieve an acceptable performance in the first place. These require detailed knowledge of the GPU hardware architecture. All in all, the results of this thesis lead to the conclusion that the higher energy efficiency and, depending on the point of view, cost efficiency of the GPUs do not outweigh the lower programming effort for the implementation of graph algorithms on CPUs.},
}