Memory Allocation Tracing with VampirTrace

Jurenz, Matthias; Brendel, Ronny; Knüpfer, Andreas; Müller, Matthias S.; Nagel, Wolfgang E.

doi:10.1007/978-3-540-72586-2_118

Cited by 12 publications

(4 citation statements)

References 9 publications

(10 reference statements)

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“…Jurenz et al [11] extended VampirTrace, a performance analysis tool, to provide a detailed memory analysis based on execution traces. Instrumentation is done using shared library pre-loading.…”

Section: Related Workmentioning

confidence: 99%

Dynamic trace-based sampling algorithm for memory usage tracking of enterprise applications

Daoud

Ezzati‐Jivan

Dagenais

2017

2017 IEEE High Performance Extreme Computing Conference (HPEC)

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Section: Related Workmentioning

confidence: 99%

Dynamic trace-based sampling algorithm for memory usage tracking of enterprise applications

Daoud

Ezzati‐Jivan

Dagenais

2017

2017 IEEE High Performance Extreme Computing Conference (HPEC)

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“…To analyze details of the S3D application, event-based program traces have been recorded using VampirTrace [4]. The generated Open Trace Format (OTF) [7] trace files have then been analyzed using the visualization tool Vampir [5].…”

Section: Performance Analysis Toolsmentioning

confidence: 99%

“…Hence, a combination of existing performance tools and techniques have been applied to identify and resolve performance and scaling issues. In this context, we are evaluating the collected performance data of S3D using the PAPI library [2] as well as TAU [3], VampirTrace [4], and Vampir [5] toolsets for scalable performance analysis of Petascale parallel applications. Such tools provide detailed analyses that offer important insight into performance and scalability problems in the form of summarized measurements of program execution behavior.…”

Section: Introductionmentioning

confidence: 99%

A Holistic Approach for Performance Measurement and Analysis for Petascale Applications

Jagode

Dongarra

Alam

et al. 2009

Lecture Notes in Computer Science

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Abstract. Contemporary high-end Terascale and Petascale systems are composed of hundreds of thousands of commodity multi-core processors interconnected with high-speed custom networks. Performance characteristics of applications executing on these systems are a function of system hardware and software as well as workload parameters. Therefore, it has become increasingly challenging to measure, analyze and project performance using a single tool on these systems. In order to address these issues, we propose a methodology for performance measurement and analysis that is aware of applications and the underlying system hierarchies. On the application level, we measure cost distribution and runtime dependent values for different components of the underlying programming model. On the system front, we measure and analyze information gathered for unique system features, particularly shared components in the multi-core processors. We demonstrate our approach using a Petascale combustion application called S3D on two high-end Teraflops systems, Cray XT4 and IBM Blue Gene/P, using a combination of hardware performance monitoring, profiling and tracing tools.

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“…Debugging tools like NVIDIA NSight [5], Visual Profiler [6] and VampirTrace [2] support debugging, profiling and analysis of parallel programs. These tools provide facts on application-level such as idle times of the processors, timings of memory transactions, as well as facts on the kernel level, such as warp divergence, memory bank conflicts, occupancy.…”

Section: Introductionmentioning

confidence: 99%

Towards Interactive Visual Exploration of Parallel Programs using a Domain-Specific Language

Klein

Brückner

Gröller

et al. 2016

Proceedings of the 4th International Workshop on OpenCL

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The use of GPUs and the massively parallel computing paradigm have become wide-spread. We describe a framework for the interactive visualization and visual analysis of the run-time behavior of massively parallel programs, especially OpenCL kernels. This facilitates understanding a program's function and structure, finding the causes of possible slowdowns, locating program bugs, and interactively exploring and visually comparing different code variants in order to improve performance and correctness. Our approach enables very specific, user-centered analysis, both in terms of the recording of the run-time behavior and the visualization itself. Instead of having to manually write instrumented code to record data, simple code annotations tell the sourceto-source compiler which code instrumentation to generate automatically. The visualization part of our framework then enables the interactive analysis of kernel run-time behavior in a way that can be very specific to a particular problem or optimization goal, such as analyzing the causes of memory bank conflicts or understanding an entire parallel algorithm.

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Memory Allocation Tracing with VampirTrace

Cited by 12 publications

References 9 publications

Dynamic trace-based sampling algorithm for memory usage tracking of enterprise applications

Dynamic trace-based sampling algorithm for memory usage tracking of enterprise applications

A Holistic Approach for Performance Measurement and Analysis for Petascale Applications

Towards Interactive Visual Exploration of Parallel Programs using a Domain-Specific Language

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