Performance optimality or reproducibility

Patki, Tapasya; Thiagarajan, Jayaraman J.; Ayala, Alexis; Islam, Tanzima

doi:10.1145/3295500.3356217

Cited by 21 publications

(6 citation statements)

References 58 publications

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“…Comparing several performance profiles is of significant interest to application developers aiming to identify performance bugs across versions of a code or to understand how different application parameters and/or initial conditions may affect the performance [12,45]. HPC experts are also interested in evaluating ensembles of performance profiles to assess the role of architecture choices, machine environments, MPI configurations, etc., to identify optimal execution and deployment modes [9,54].…”

Section: Domain Problem Characterizationmentioning

confidence: 99%

Scalable Comparative Visualization of Ensembles of Call Graphs

Kesavan

Bhatia

Bhatele

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present an enhanced version of CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey, a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. We demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.

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Section: Domain Problem Characterizationmentioning

confidence: 99%

Scalable Comparative Visualization of Ensembles of Call Graphs

Kesavan

Bhatia

Bhatele

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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“…Other system constraints such as I/O interference [12] or including consideration of network traffic, power limits or concurrency tuning in the HPC middleware [41], can also become a significant reason for performance variability. Although we could include them in our model, we chose to focus on application-specific variations, a new trend in HPC, and separate their impact from the hardware constraints.…”

Section: Related Workmentioning

confidence: 99%

Profiles of Upcoming HPC Applications and Their Impact on Reservation Strategies

Gainaru

Goglin

Honoré

et al. 2021

IEEE Trans. Parallel Distrib. Syst.

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With the expected convergence between HPC, BigData and AI, new applications with different profiles are coming to HPC infrastructures. We aim at better understanding the features and needs of these applications in order to be able to run them efficiently on HPC platforms. The approach followed is bottom-up: we study thoroughly an emerging application, Spatially Localized Atlas Network Tiles (SLANT, originating from the neuroscience community) to understand its behavior. Based on these observations, we derive a generic, yet simple, application model (namely, a linear sequence of stochastic jobs). We expect this model to be representative for a large set of upcoming applications that require the computational power of HPC clusters without fitting the typical behavior of large-scale traditional applications. In a second step, we show how one can manipulate this generic model in a scheduling framework. Specifically we consider the problem of making reservations (both time and memory) for an execution on an HPC platform. We derive solutions using the model of the first step of this work. We experimentally show the robustness of the model, even with very few data or with another application, to generate the model, and provide performance gains with regards to standard and more recent approaches used in the neuroscience community.

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“…Performance variability due to interference is highly evident as it may arise from on-chip variations [17,21], network activity [18,24], I/O traffic [12,13], etc. To study performance variability, Ates et al [3] introduce a performance anomaly generator for the major HPC subsystems that assesses the performance resilience of applications to different variability sources.…”

Section: Related Workmentioning

confidence: 99%

“…In these scenarios, common strategies to reduce interference include eliminating unnecessary activities such as process scheduling or memory management [23], and carefully managing interrupts to minimize application perturbation [27]. As the scale and heterogeneity of systems keeps increasing, tackling interference becomes an even larger concern [24]. While the aforementioned techniques can generally mitigate interference, sources of performance variability are overall very diverse in modern highly interconnected systems, and can only be addressed effectively in cooperation with application knowledge.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Task-parallel Applications in Dynamically Asymmetric Environments

Chen

Soomro

Abduljabbar

et al. 2020

Preprint

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Shared resource interference is observed by applications as dynamic performance asymmetry. Prior art has developed approaches to reduce the impact of performance asymmetry mainly at the operating system and architectural levels. In this work, we study how application-level scheduling techniques can leverage moldability (i.e. flexibility to work as either single-threaded or multithreaded task) and explicit knowledge on task criticality to handle scenarios in which system performance is not only unknown but also changing over time. Our proposed task scheduler dynamically learns the performance characteristics of the underlying platform and uses this knowledge to devise better schedules aware of dynamic performance asymmetry, hence reducing the impact of interference. Our evaluation shows that both criticality-aware scheduling and parallelism tuning are effective schemes to address interference in both shared and distributed memory applications.CCS Concepts • Computer systems organization → Multicore architectures.

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Performance optimality or reproducibility

Cited by 21 publications

References 58 publications

Scalable Comparative Visualization of Ensembles of Call Graphs

Scalable Comparative Visualization of Ensembles of Call Graphs

Profiles of Upcoming HPC Applications and Their Impact on Reservation Strategies

Scheduling Task-parallel Applications in Dynamically Asymmetric Environments

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