  In this short paper, we introduce the Ridgeline model, an extension of the
Roofline model [4] for distributed systems. The Roofline model targets shared
memory systems, bounding the performance of a kernel based on its operational
intensity, and the peak compute throughput and memory bandwidth of the
execution system. In a distributed setting, with multiple communicating compute
entities, the network must be taken into account to model the system behavior
accurately. The Ridgeline aggregates information on compute, memory, and
network limits in one 2D plot to show, in an intuitive way, which of the
resources is the expected bottleneck. We show the applicability of the
Ridgeline in a case study based on a data-parallel Multi-Layer Perceptron (MLP)
instance.
