Abstract
New programming models have been introduced to aid the programmer dealing with the complexity of large-scale systems, simplifying the coding process and making applications more scalable. Task-based programming is one example that became popular recently. At the same time, understanding performance of multicore systems is key for getting faster execution times and optimize workloads, but it is becoming harder due to the increased complexity of hardware and the runtime systems. To address this, we present TaskInsight, a technique to characterize the memory behavior of different task schedulers through the analysis of data reuse. TaskInsight provides high-level information that can be correlated with tasks' performance variation over time to understand data reuse through the caches due to scheduling choices. This allows to identify which scheduling decisions affected performance, when were they taken, and why the performance changed, both in single and multi-threaded executions. We demonstrate how TaskInsight can diagnose cases where poor scheduling caused over 60% difference on average (and up to 7x slowdowns) due to changes in the tasks' data reuse through the caches.
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