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The growing gap between processor and memory speeds has lead to complex memory hierarchies as processors evolve to mitigate such divergence by exploiting the locality of reference. In this direction, the BSC performance analysis tools have been recently extended to provide insight into the application memory accesses by depicting their temporal and spatial characteristics, correlating with the source-code and the achieved performance simultaneously. These extensions rely on the Precise Event-Based Sampling (PEBS) mechanism available in recent Intel processors to capture information regarding the application memory accesses. The sampled information is later combined with the Folding technique to represent a detailed temporal evolution of the memory accesses and in conjunction with the achieved performance and the source-code counterpart. The reports generated by the latter tool help not only application developers but also processor architects to understand better how the application behaves and how the system performs. In this paper, we describe a tighter integration of the sampling mechanism into the monitoring package. We also demonstrate the value of the complete workflow by exploring already optimized state–of–the–art benchmarks, providing detailed insight of their memory access behavior. We have taken advantage of this insight to apply small modifications that improve the applications' performance. ; This work has been performed in the Intel-BSC Exascale Lab. We would like to thank Forschungszentrum Jülich for the compute time on the Jureca system. This project has received funding from the European Union's Horizon 2020 research and innovation program under Marie Sklodowska-Curie grant agreement no. 749516. ; Peer Reviewed ; Postprint (author's final draft)

High-resolution and variable-shape images have not yet been properly addressed by the AI community. The approach of down-sampling data often used with convolutional neural networks is sub-optimal for many tasks, and has too many drawbacks to be considered a sustainable alternative. In sight of the increasing importance of problems that can benefit from exploiting high-resolution (HR) and variable-shape, and with the goal of promoting research in that direction, we introduce a new family of datasets (MetH). The four proposed problems include two image classification, one image regression and one super resolution task. Each of these datasets contains thousands of art pieces captured by HR and variable-shape images, labeled by experts at the Metropolitan Museum of Art. We perform an analysis, which shows how the proposed tasks go well beyond current public alternatives in both pixel size and aspect ratio variance. At the same time, the performance obtained by popular architectures on these tasks shows that there is ample room for improvement. To wrap up the relevance of the contribution we review the fields, both in AI and high-performance computing, that could benefit from the proposed challenges. ; This work is partially supported by the Intel-BSC Exascale Lab agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2017-SGR-1414). ; Preprint