Abstract
When optimizing or porting applications to new architectures, a preliminary characterization is necessary to exploit the maximum computing power of the employed devices. Profiling tools are available for numerous architectures and programming models, making it easier to spot possible bottlenecks. However, for a better interpretation of the collected results, current profilers rely on insightful performance models. In this paper, we describe the Cache Aware Roofline Model (CARM) and tools for its generation to enable the performance characterization of GPU architectures and workloads. We use CARM to characterize two kernels that are part of a 3D iterative reconstruction application for Computed Tomography (CT). These two kernels take most of the execution time of the whole method, being therefore suitable for a deeper analysis. By exploring the model and the methodology proposed, the overall performance of the kernels has been improved up to two times compared to the previous implementations.
This work has been partially supported through grant TIN2016-79637-P “Towards unification of HPC and Big Data Paradigms” from the Spanish Ministry of Economy and Competitiveness, the COST Action IC1305 “Network for Sustainable Ultrascale Computing Platforms” (NESUS), grant FPU14/03875 from the Spanish Ministry of Education and by the Portuguese national funds through FCT under the projects UID/CEC/50021/2013 and LISBOA-01-0145-FEDER-031901 (PTDC/CCI-COM/31901/2017).
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Serrano, E., Ilic, A., Sousa, L., Garcia-Blas, J., Carretero, J. (2018). Cache-Aware Roofline Model and Medical Image Processing Optimizations in GPUs. In: Yokota, R., Weiland, M., Shalf, J., Alam, S. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science(), vol 11203. Springer, Cham. https://doi.org/10.1007/978-3-030-02465-9_36
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