Summary Event‐based performance monitoring and analysis are effective means when tuning parallel applications for optimal resource usage. In this article, we address the data capacity challenge that arises when applying the tracing methodology to large‐scale parallel applications and long execution times. Existing approaches use static, pre‐defined event filters to reduce the performance data to a manageable size. In contrast, we propose self‐guided filters that automatically adapt to an application's runtime behaviour and therefore, do not require any previous knowledge or application executions. Our contribution consists of four adaptive runtime filters, which target a specific type of data redundancy each. The filters focus on detecting identical events in loop iterations, constant events with no variation in time, and very short, highly frequent, typically not very meaningful events, having a severe impact on the total data volume. We evaluate our prototype implementation with five real‐world applications and achieve a data reduction of two orders of magnitude while increasing execution time less than 1%. Likewise, we show that the qualitative impact of our filters on performance analysis in state‐of‐the‐art analysis tools can be reduced by adding feedback methods and statistical information to the filtered traces. Copyright © 2017 John Wiley & Sons, Ltd.

We present a particle-in-cell simulation of the relativistic Kelvin-Helmholtz Instability (KHI) that for the first time delivers angularly resolved radiation spectra of the particle dynamics during the formation of the KHI. This enables studying the formation of the KHI with unprecedented spatial, angular and spectral resolution. Our results are of great importance for understanding astrophysical jet formation and comparable plasma phenomena by relating the particle motion observed in the KHI to its radiation signature.