Selective runtime monitoring: Non-intrusive elimination of high-frequency functions

Concurrency and Computation

Doleschal

et al. 2017

Self Cite

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.

Section: Related Workmentioning

confidence: 99%

Section: Strategies For Adaptive Runtime Filteringmentioning

confidence: 99%

Using adaptive runtime filtering to support an event‐based performance analysis

Stolle

Concurrency and Computation

Doleschal

et al. 2017

Self Cite

“…In addition, OTFX [15] itself includes techniques to deal with excessive event generation, e.g. filtering functions based on their duration [16].…”

Section: Related Workmentioning

confidence: 99%

Automatic Adaption of the Sampling Frequency for Detailed Performance Analysis

2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

Knuepfer²

2017

Self Cite

One of the most urgent challenges in event based performance analysis is the enormous amount of collected data. Combining event tracing and periodic sampling has been a successful approach to allow a detailed event-based recording of MPI communication and a coarse recording of the remaining application with periodic sampling. In this paper, we present a novel approach to automatically adapt the sampling frequency during runtime to the given amount of buffer space, releasing users to find an appropriate sampling frequency themselves. This way, the entire measurement can be kept within a single memory buffer, which avoids disruptive intermediate memory buffer flushes, excessive data volumes, and measurement delays due to slow file system interaction. We describe our approach to sort and store samples based on their order of occurrence in an hierarchical array based on powers of two. Furthermore, we evaluate the feasibility as well as the overhead of the approach with the prototype implementation OTFX based on the Open Trace Format 2, a state-of-the-art Open Source event trace library used by the performance analysis tools Vampir, Scalasca, and Tau.This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

“…Thus, only relevant events are stored. This step involves runtime techniques like automated filters to reduce the recording of very short and highly frequent function calls that flood the trace buffers or, in iterative codes, the adaptive selection of only a few representative iterations, to minimize redundancy [21], [7].…”

Section: B Data Management During Runtimementioning

confidence: 99%

Online Performance Analysis: An Event-Based Workflow Design towards Exascale

2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security

Hilbrich

Brunst

2014

Self Cite

Today, it is commonly accepted that speedup and efficiency are not granted automatically when developing or porting software for High Performance Computing (HPC) platforms. The reasons are manifold and actively investigated in the community. Software monitors are essential for these studies as they provide the raw performance data to be analyzed. We propose an online monitoring workflow for event-based performance analysis that takes into account the significant changes in system architecture towards the development of exascale supercomputers. Critical properties are: communication across large numbers of processing elements, limited I/O capabilities, and a decreasing memoryper-core ratio. We present a hierarchical data management and steering workflow that directly couples the application, measurement, and analysis processes, thus eliminating the need for extensive communication and buffering. The workflow is closely integrated with the native system communication API to enable best communication across processing elements. The memory issue is addressed with a new lossy hierarchical data compression technique for in-memory storage, intended for small, fixed-size buffers. Further, we abandon secondary storage to avoid potential I/O challenges. We demonstrate the feasibility of our design with a prototype implementation that features services for data collection, analysis, and runtime compression. Our evaluation extrapolates results obtained with the NAS Parallel Benchmarks at up to 2,048 processes to an exascale workflow.978-1-4799-6123-8/14 $31.00