Automatic Cache Aware Roofline Model Building and Validation Using Topology Detection

Nicolas Denoyelle (1) , Aleksandar Ilic (2, 3) , Brice Goglin (4, 1) , Leonel Sousa (2, 3) , Emmanuel Jeannot (4)

1 TADAAM - Topology-Aware System-Scale Data Management for High-Performance Computing
2 IST - Instituto Superior Técnico, Universidade Técnica de Lisboa
3 INESC-ID - Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa
4 LaBRI - Laboratoire Bordelais de Recherche en Informatique

The ever growing complexity of high performance computing systems imposes significant challenges to exploit as much as possible their computational and memory resources. Recently, the Cache-aware Roofline Model has gained popularity due to its simplicity when modeling multi-cores with complex memory hierarchy, characterizing applications bottlenecks, and quantifying achieved or remaining improvements. In this short paper we involve hardware locality topology detection to build the Cache Aware Roofline Model for modern processors in an open-source locality-aware tool. The proposed tool also includes a set of specific micro-benchmarks to assess the micro-architecture performance upper-bounds. The experimental results show that by relying on the proposed tool, it was possible to reach near-theoretical bounds of an Intel 3770K processor, thus proving the effectiveness of the modeling methodology.

Mots clés: Roofline Model; DRAM; Cache; Tool; Cache Aware Roofline Model; hwloc; plafrim

Domaines: Architectures Matérielles [cs.AR]; Calcul parallèle, distribué et partagé [cs.DC]

Nicolas Denoyelle, Aleksandar Ilic, Brice Goglin, Leonel Sousa, Emmanuel Jeannot. Automatic Cache Aware Roofline Model Building and Validation Using Topology Detection. NESUS Third Action Workshop and Sixth Management Committee Meeting, Jesus Carretero, Oct 2016, Sofia, Bulgaria. ⟨hal-01381982⟩