🤖 AI Summary
Existing performance analysis tools struggle to simultaneously capture temporal dynamics and a holistic view of performance bottlenecks: Roofline models neglect time evolution, while profilers and tracers obscure theoretical performance limits. This work proposes campaign diagrams—a novel visualization framework that uniquely integrates temporal phases with multidimensional resource utilization, including computational throughput, memory bandwidth, data traffic, and latency. Campaign diagrams can be generated from analytical models, simulations, or profiling data, concurrently displaying both theoretical performance ceilings and achieved performance. The approach uncovers cross-phase optimization opportunities often missed by conventional tools, such as counterintuitive cases where enhancing low-intensity operators improves end-to-end performance. Validated on low-rank GEMM and Mamba workloads, the method successfully identifies potential for operator fusion and pipeline optimizations, demonstrating its efficacy in diagnosing deep-rooted performance bottlenecks.
📝 Abstract
We present campaign diagrams, a visualization technique for phase-level analysis of resource utilization and bottlenecks in modern workloads. Existing tools have a trade-off: rooflines aggregate a workload into a single point and lose all notion of time, while profilers and traces expose fine-grained events but obscure what bounds performance. Instead, a campaign diagram depicts compute throughput and memory bandwidth utilization, compute and memory traffic volume, and latency in a single figure. Since they can be generated from analytical models, simulations, or profiling data, campaign diagrams capture both ideal bounds and a kernel's achieved performance. We demonstrate them on two case studies: a low-rank GEMM, where they reveal the counterintuitive result that reducing operational intensity can improve end-to-end performance, and Mamba, where they expose fusion and pipelining opportunities across phases. In both cases, our visualization technique reveals optimization opportunities that are difficult to identify with rooflines or profilers alone.