This course teaches performance engineering approaches on the compute node level. “Performance engineering” as we define it is more than employing tools to identify hotspots and bottlenecks. It is about developing a thorough understanding of the interactions between software and hardware. This process must start at the core, socket, and node level, where the code gets executed that does the actual computational work. Once the architectural requirements of a code are understood and correlated with performance measurements, the potential benefit of optimizations can often be predicted. We introduce a “holistic” node-level performance engineering strategy centered around the roofline performance model and apply it to different algorithms from computational science. We also show that simple, easy to use tools bring us a long way towards deep insight into the interaction between software and hardware.
Introduction Our approach to performance engineering Basic architecture of multicore systems: threads, cores, caches, sockets, memory The important role of system topology Tools: topology & affinity in multicore environments Overview likwid-topology and likwid-pin Microbenchmarking for architectural exploration Properties of data paths in the memory hierarchy Bottlenecks OpenMP barrier overhead Roofline model: basics Model assumptions and construction Simple examples Limitations of the Roofline model Pattern-based performance engineering
Optimal use of parallel resources Single Instruction Multiple Data (SIMD) Cache-coherent Non-Uniform Memory Architecture (ccNUMA) Simultaneous Multi-Threading (SMT) Tools: hardware performance counters Why hardware performance counters? likwid-perfctr Validating performance models Roofline case studies Dense matrix-vector multiplication Sparse matrix-vector multiplication Jacobi (stencil) smoother Optional: The ECM performance model