SKOPE: a framework for modeling and exploring workload behavior.

Understanding workload behavior plays an important role in performance studies. The growing complexity of applications and architectures has increased the gap among application developers, performance engineers, and hardware designers. To reduce this gap, we propose SKOPE, a SKeleton framework for Performance Exploration, that produces a descriptive model about the semantic behavior of a workload, which can infer potential transformations and help users understand how workloads may interact with and adapt to emerging hardware. SKOPE models can be shared, annotated, and studied by a community of performance engineers and system designers; they offer readability in the frontend and versatility in the backend. SKOPE can be used for performance analysis, tuning, and projection. We provide two example use cases. First, we project GPU performance from CPU code without GPU programming or accessing the hardware, and are able to automatically explore transformations and the projected best-achievable performance deviates from the measured by 18% on average. Second, we project the multi-node scaling trends of two scientific workloads, and are able to achieve a projection accuracy of 95%.