Score-P: A Joint Performance Measurement Run-Time Infrastructure for Periscope, Scalasca, TAU, and Vampir

Knüpfer, Andreas; Rössel, Christian; Mey, Dieter an; Biersdorff, Scott; Diethelm, Kai; Eschweiler, D.; Geimer, Markus; Gerndt, Michael; Lorenz, Daniel; Malony, Allen D.; Nagel, Wolfgang E.; Oleynik, Yury; Philippen, Peter; Saviankou, Pavel; Schmidl, Dirk; Shende, Sameer; Tschüter, Ronny; Wagner, Michael; Wesarg, Bert; Wolf, Felix

doi:10.1007/978-3-642-31476-6_7

Cited by 263 publications

(127 citation statements)

References 16 publications

(20 reference statements)

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“…5.1, the DTA will be based on the PTF. The RRL will be based on the Score-P instrumentation and measurement infrastructure [15]. Both tools have been used in production on HPC systems for years and are thus well tested and stable.…”

Section: Methodsmentioning

confidence: 99%

The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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Energy efficiency is an important aspect of future exascale systems, mainly due to rising energy cost. Although High performance computing (HPC) applications are compute centric, they still exhibit varying computational characteristics in different regions of the program, such as compute-, memory-, and I/O-bound code regions. Some of today's clusters already offer mechanisms to adjust the system to the resource requirements of an application, e.g., by controlling the CPU frequency. However, manually tuning for improved energy efficiency is a tedious and painstaking task that is often neglected by application developers. The European Union's Horizon 2020 project READEX (Runtime Exploitation of Application Dynamism for Energyefficient eXascale computing) aims at developing a tools-aided approach for improved energy efficiency of current and future HPC applications. To reach this goal, the READEX project combines technologies from two ends of the compute spectrum, embedded systems and HPC, constituting a split design-time/runtime methodology. dynamic auto-tuning of fine-grained application regions using the systems scenario methodology, which was originally developed for improving the energy efficiency in embedded systems. This paper introduces the concepts of the READEX project, its envisioned implementation, and preliminary results that demonstrate the feasibility of this approach.

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Section: Methodsmentioning

confidence: 99%

The READEX formalism for automatic tuning for energy efficiency

et al. 2017

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“…It can detect the root cause of many communication performance issues and provides a rich environment within which to investigate performance issues. The latest version of Scalasca is built upon Score-P [2] which is a community-developed parallel performance instrumentation and measurement system. It has an open format which allows the data to be used in conjunction with a range of tools.…”

Section: Methods Usedmentioning

confidence: 99%

Performance optimisation and productivity centre of excellence

Bridgwater

2016

2016 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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INDUSTRIAL EXTENDED ABSTRACTAs machines get larger and scientific applications advance, it is more and more imperative to fully utilize high performance computing (HPC) capability. The complexity and changing landscape of parallel computers may lead to users being unable or unsure how to achieve optimal performance from their applications and fully utilize their HPC resources.The Performance Optimisation and Productivity Centre of Excellence in Computing Applications (POP) has received funding from the European Commission as part of the Horizon 2020 programme to help alleviate these issues. It aims to uncover inefficiencies and their causes in existing parallel HPC applications that will lead to an improvement in the productivity and competitiveness of European organizations, in academia, government and industry. The POP project will drive efforts to highlight the need for and best practices in performance optimization through performance audits on codes along with training events to improve knowledge in this area. The aim is to help developers target their code development and refactoring in the most efficient direction and provide a return on investment from the savings due to the performance improvement.The POP project combines the expertise and experience of Barcelona Supercomputing Center (BSC), High Performance Computing Center Stuttgart, Jülich Supercomputing Centre (JSC), Numerical Algorithms Group Ltd (NAG), RWTH Aachen and TERATEC. This combination provides longstanding and well respected resources in the academic and commercial realms. The POP members have come together to create a coherent and consistent methodology to give a clear, precise and useful overview of the performance of each HPC application.The services of the POP project are free of charge to organizations with in the EU to analyze and advise on any parallel code in academic, government or industrial organizations of any domain. I. METHODS USEDEspecially when aiming for very large scale HPC, efficiency of the application should be a major objective in program development. There is a broad need for detailed quantitative understanding of the actual cause of any inefficiencies and the complex interplay of effects as systems scale. This is a key need in development and refactoring of applications, especially as we work toward exascale machines.There are two main performance analysis tools used during POP performance analysis. The Scalasca [1] environment has been developed at JSC and gives a highly scalable providing a call-path view. It can detect the root cause of many communication performance issues and provides a rich environment within which to investigate performance issues. The latest version of Scalasca is built upon Score-P [2] which is a community-developed parallel performance instrumentation and measurement system. It has an open format which allows the data to be used in conjunction with a range of tools. The second set of performance analysis tools have been developed by BSC are based around the Paraver [3] visualizer and Extr...

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“…Score-P v3.1 (Knüpfer et al, 2012) and Scalasca v2.3.1 (Geimer et al, 2010;Zhukov et al, 2015), where results collected with Score-P and Scalasca can be examined using the interactive analysis report explorer Cube v4.3.5 , Allinea Performance Reports v7.0.4 (January et al, 2015), Extrae v3.4.3 (Alonso et al, 2012), Paraver v4.6.3 (Labarta et al, 2006), Intel Vectorization Advisor 2015 (Rane et al, 2015), and Darshan v3.0.0 (Carns et al, 2011) (see Table A1 10 in Appendix A for a more detailed description of each performance analysis tool listed above). The modeling chain for the profiling workflow is as follows:…”

Section: Code Profilingmentioning

confidence: 99%

Best practice regarding the three P's: profiling, portability and provenance when running HPC geoscientific applications

Sharples

Zhukov

Geimer

et al. 2017

Preprint

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Abstract. Geoscientific modeling is constantly evolving, with next generation geoscientific models and applications placing high demands on high performance computing (HPC) resources. These demands are being met by new developments in HPC architectures, software libraries, and infrastructures. New HPC developments require new programming paradigms leading to substantial investment in model porting, tuning, and refactoring of complicated legacy code in order to use these resources effectively. In addition to the challenge of new massively parallel HPC systems, reproducibility of simulation and analysis 5 results is of great concern, as the next generation geoscientific models are based on complex model implementations and profiling, modeling and data processing workflows.Thus, in order to reduce both the duration and the cost of code migration, aid in the development of new models or model components, while ensuring reproducibility and sustainability over the complete data life cycle, a streamlined approach to profiling, porting, and provenance tracking is necessary. We propose a run control framework (RCF) integrated with a workflow 10 engine which encompasses all stages of the modeling chain: 1. preprocess input, 2. compilation of code (including code instrumentation with performance analysis tools), 3. simulation run, 4. postprocess and analysis, to address these issues. Within this RCF, the workflow engine is used to create and manage benchmark or simulation parameter combinations and performs the documentation and data organization for reproducibility. This approach automates the process of porting and tuning, profiling, testing, and running a geoscientific model. We show that in using our run control framework, testing, benchmarking, profiling, 15 and running models is less time consuming and more robust, resulting in more efficient use of HPC resources, more strategic code development, and enhanced data integrity and reproducibility.

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Score-P: A Joint Performance Measurement Run-Time Infrastructure for Periscope, Scalasca, TAU, and Vampir

Cited by 263 publications

References 16 publications

The READEX formalism for automatic tuning for energy efficiency

The READEX formalism for automatic tuning for energy efficiency

Performance optimisation and productivity centre of excellence

Best practice regarding the three P's: profiling, portability and provenance when running HPC geoscientific applications

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