The Case for a Common Instrumentation Interface for HPC Codes

Boehme, David; Huck, Kevin; Madsen, Jonathan; Weidendorfer, Josef

doi:10.1109/protools49597.2019.00010

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Rather than add TAU calls directly to capture timer data, TAU provides an instrumentation library called PerfStubs. 24 Perf-Stubs is self-contained, and creates a "stub" implementation of a timer library, with support for counters and metadata. At runtime, the PerfStubs library will check if specific symbols are implemented by any tool libraries in the environment, and if they are it will initialize the tool and pass any timer start/stop events to the tool.…”

Section: Performance Integration In Astarothmentioning

confidence: 99%

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SOMA: Observability, monitoring, and in situ analytics for exascale applications

Yokelson,

Lappi,

Ramesh

et al. 2024

Concurrency and Computation

Self Cite

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SummaryWith the rise of exascale systems and large, data‐centric workflows, the need to observe and analyze high performance computing (HPC) applications during their execution is becoming increasingly important. HPC applications are typically not designed with online monitoring in mind, therefore, the observability challenge lies in being able to access and analyze interesting events with low overhead while seamlessly integrating such capabilities into existing and new applications. We explore how our service‐based observation, monitoring, and analytics (SOMA) approach to collecting and aggregating both application‐specific diagnostic data and performance data addresses these needs. We present our SOMA framework and demonstrate its viability with LULESH, a hydrodynamics proxy application. Then we focus on Astaroth, a multi‐GPU library for stencil computations, highlighting the integration of the TAU and APEX performance tools and SOMA for application and performance data monitoring.

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Section: Performance Integration In Astarothmentioning

confidence: 99%

“…However, user code instrumentation generally lacks support for tool interfaces. The PerfStubs library 24 is a thin, stubbed‐out, “adapter” interface for instrumenting library or application code. The PerfStubs library itself does not do any measurement, it merely provides access to an API that performance tools can implement.…”

Section: Performance Toolsmentioning

confidence: 99%

SOMA: Observability, monitoring, and in situ analytics for exascale applications

Yokelson,

Lappi,

Ramesh

et al. 2024

Concurrency and Computation

Self Cite

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show abstract

“…In the current EFFIS performance monitoring setup, individual codes are instrumented by using the PerfStubs interface (Boehme et al, 2019). PerfStubs is a thin stub interface for instrumenting library or application code.…”

Section: Effis Descriptionmentioning

confidence: 99%

The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science

Suchyta

Klasky

Podhorszki

et al. 2021

The International Journal of High Performance Computing Applica

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We present the Exascale Framework for High Fidelity coupled Simulations (EFFIS), a workflow and code coupling framework developed as part of the Whole Device Modeling Application (WDMApp) in the Exascale Computing Project. EFFIS consists of a library, command line utilities, and a collection of run-time daemons. Together, these software products enable users to easily compose and execute workflows that include: strong or weak coupling, in situ (or offline) analysis/visualization/monitoring, command-and-control actions, remote dashboard integration, and more. We describe WDMApp physics coupling cases and computer science requirements that motivate the design of the EFFIS framework. Furthermore, we explain the essential enabling technology that EFFIS leverages: ADIOS for performant data movement, PerfStubs/TAU for performance monitoring, and an advanced COUPLER for transforming coupling data from its native format to the representation needed by another application. Finally, we demonstrate EFFIS using coupled multi-simulation WDMApp workflows and exemplify how the framework supports the project’s needs. We show that EFFIS and its associated services for data movement, visualization, and performance collection does not introduce appreciable overhead to the WDMApp workflow and that the resource-dominant application’s idle time while waiting for data is minimal.

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“…The potential for performance degradation via instrumentation APIs, even when dormant at runtime, is supported by the results of applying an edge-case scenario of injecting 500,000 runtime-disabled instrumentation points within a matrix multiplication benchmark (Fig. 1) to Caliper, TAU, and timemory [5]. Unlike statistical profilers which take measurements at a given rate, the overhead of deterministic instrumentation cannot be fully negated and the overhead associated with the instrumentation is subject to high variability: Caliper markers increased run time by ∼397% while TAU markers increased run time by ∼262%.…”

Section: Need For Common Instrumentation Interfacementioning

confidence: 99%

“…Timemory attempts to provide the analogue to the LLVM infrastructure and Kokkos model in the realm of performance measurement and analysis. The framework provides a viable solution to a common instrumentation interface [5] for multiplexing performance measurement and analysis tools. As a common instrumentation interface, timemory would provide a straightforward method for projects with existing instrumentation APIs to locally 1 wrap their existing API and introduce a significant number of new capabilities to the existing tool 2 while requiring no significant changes to the tool itself.…”

Section: Introductionmentioning

confidence: 99%

Timemory: Modular Performance Analysis for HPC

Madsen

Awan

Brunie

et al. 2020

Lecture Notes in Computer Science

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HPC has undergone a significant transition toward heterogeneous architectures. This transition has introduced several issues in code migration to support multiple frameworks for targeting the various architectures. In order to cope with these challenges, projects such as Kokkos and LLVM create abstractions which map a generic frontend API to the backend that supports the targeted architecture. This paper presents a complementary framework for performance measurement and analysis. Several performance measurement and analysis tools in existence provide their capabilities through various methods but the common theme among these tools are prohibitive limitations in terms of user-level extensions. For this reason, software developers commonly have to learn multiple tools and valuable analysis methods, such as the roofline model, are frequently required to be generated manually. The timemory framework provides complete modularity for performance measurement and analysis and eliminates all restrictions on user-level extensions. The timemory framework also provides a highly-efficient and intuitive method for handling multiple tools/measurements (i.e., "components") concurrently. The intersection of these characteristics provide ample evidence that timemory can serve as the common interface for existing performance measurement and analysis tools. Timemory components are developed in C++ but includes multi-language support for C, Fortran, and Python codes. Numerous components are provided by the library itself -including, but not limited to, timers, memory usage, hardware counters, and FLOP and instruction roofline models. Additionally, analysis of the intrinsic overhead demonstrates superior performance in comparison with popular tools.

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The Case for a Common Instrumentation Interface for HPC Codes

Cited by 7 publications

References 6 publications

SOMA: Observability, monitoring, and in situ analytics for exascale applications

SOMA: Observability, monitoring, and in situ analytics for exascale applications

The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science

Timemory: Modular Performance Analysis for HPC

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