Dynamic instrumentation of threaded applications

Xu, Zhou; Miller, Barton P.; Naím, Oscar

doi:10.1145/329366.301109

Cited by 7 publications

(6 citation statements)

References 2 publications

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“…Profiling tools have been initially used to measure the dynamic behavior of sequential applications . Subsequent works have extended profiling into the realm of parallel computers with great success . Parallel profilers, such as the recent works of Tallent et al or Eyerman and Eeckhout , generally focus on systems where threads are able to execute independently of each other.…”

Section: Related Workmentioning

confidence: 99%

Profiling divergences in GPU applications

Coutinho

Sampaio

Pereira

et al. 2012

Concurrency and Computation

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SUMMARYThe increasing programmability and the high computational power of graphics processing units make them attractive to general purpose programming. However, taking full benefit of this execution environment is a challenging task. One of these challenges stems from divergences, a phenomenon that occurs when threads that execute in lock‐step are forced to take different program paths because of branches in the code. In face of divergences, some threads will have to wait, idly, while their diverging siblings execute. Optimizing the code to avoid divergences is difficult because this task demands a deep understanding of programs that might be large and convoluted. To facilitate the detection of divergences, this paper introduces the divergence map, a data structure that indicates the location and the volume of divergences in a program. We build this map via dynamic profiling techniques, which we have implemented on top of an open source Parallel Thread Execution compiler. To illustrate the importance of the divergence map, we have used it to pinpoint the core regions that must be optimized in well‐known public applications. By hand optimizing some applications, we have added 9–11% speedups onto kernels that have already gone through the sieve of many programmers. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Profiling divergences in GPU applications

Coutinho

Sampaio

Pereira

et al. 2012

Concurrency and Computation

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“…Metrics in Paradyn correspond to everything that can be counted or timed through instrumentations. The original Paradyn does not support multithreading, but Xu et al [24] introduce extensions to Paradyn to support the instrumentation of multithreaded applications. Zaki et al [26] introduce an infrastructure to gather traces of message-passing programs running on parallel distributed systems.…”

Section: Performance Visualization and Analysismentioning

confidence: 99%

Automating vertical profiling

Hauswirth

Diwan

Sweeney

et al. 2005

Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications

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Last year at OOPSLA we presented a methodology, vertical profiling, for understanding the performance of objectoriented programs. The key insight behind this methodology is that modern programs run on top of many layers (virtual machine, middleware, etc) and thus we need to collect and combine information from all layers in order to understand system performance. Although our methodology was able to explain previously unexplained performance phenomena, it was extremely labor intensive. In this paper we describe and evaluate techniques for automating two significant activities of vertical profiling: trace alignment and correlation. Trace alignment aligns traces obtained from separate runs so that one can reason across the traces. We are not aware of any prior approach that effectively and automatically aligns traces. Correlation sifts through hundreds of metrics to find ones that have a bearing on a performance anomaly of interest. In prior work we found that statistical correlation was only sometimes effective. We have identified highly-effective approaches for both activities.For aligning traces we explore dynamic time warping, and for correlation we explore eight correlators based on statistical correlation, distance measures, and piecewise linear segmentation. Although we explore these activities in the context of vertical profiling, both activities are widely applicable in the performance analysis area.

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“…Metrics in Paradyn correspond to everything that can be counted or timed through instrumentations. The original Paradyn does not support multithreading, but Xu et al [38] introduce extensions to Paradyn to support the instrumentation of multithreaded applications. Zaki et al [40] introduce an infrastructure to gather traces of message-passing programs running on parallel distributed systems.…”

Section: Performance Visualizationmentioning

confidence: 99%

Vertical profiling

et al. 2004

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Object-oriented programming languages provide a rich set of features that provide significant software engineering benefits. The increased productivity provided by these features comes at a justifiable cost in a more sophisticated runtime system whose responsibility is to implement these features efficiently. However, the virtualization introduced by this sophistication provides a significant challenge to understanding complete system performance, not found in traditionally compiled languages, such as C or C++. Thus, understanding system performance of such a system requires profiling that spans all levels of the execution stack, such as the hardware, operating system, virtual machine, and application.In this work, we suggest an approach, called vertical profiling, that enables this level of understanding. We illustrate the efficacy of this approach by providing deep understandings of performance problems of Java applications run on a VM with vertical profiling support. By incorporating vertical profiling into a programming environment, the programmer will be able to understand how their program interacts with the underlying abstraction levels, such as application server, VM, operating system, and hardware.

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Dynamic instrumentation of threaded applications

Cited by 7 publications

References 2 publications

Profiling divergences in GPU applications

Profiling divergences in GPU applications

Automating vertical profiling

Vertical profiling

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