Evaluating the performance and energy efficiency of the COSMO-ART model system

Charles, Joseph; Sawyer, William; Dolz, Manuel F.; Catalán, Sandra

doi:10.1007/s00450-014-0267-7

Cited by 8 publications

(8 citation statements)

References 42 publications

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“…In future years as on-chip energy metering matures and is standardized, we can imagine adding an "actual Joules per SY (AJPSY)" measure, which takes into account the actual energy used by the model and its workflow across the simulation lifecycle, including computation, data movement, and storage. These measures are similar in spirit to some prior measures of "energy to solution" (Bekas and Curioni, 2010;Cumming et al, 2014;Charles et al, 2015). The FTTSE metric of Bekas and Curioni (2010) is very similar to the AJPSY metric proposed here, and which we believe will replace JPSY in due course, when direct metering becomes routinely available.…”

Section: Balaji Et Al: Measurements Of Earth System Models In Cmip6supporting

confidence: 52%

“…As the energy cost of computing (Cumming et al, 2014;Charles et al, 2015) is increasingly becoming the limiting factor in large-scale computing, we expect that our machineaverage measure of model energy consumption JPSY will need to be replaced by a more accurate measure of energy consumption AJPSY, using fine-grained hardware energy Table 4. Details of platforms used in this study.…”

Section: Summary and Future Workmentioning

confidence: 99%

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CPMIP: measurements of real computational performance of Earth system models in CMIP6

et al. 2017

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Abstract. A climate model represents a multitude of processes on a variety of timescales and space scales: a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory-bound. Such weakscaling, I/O, and memory-bound multi-physics codes present particular challenges to computational performance.Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models.We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth system) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering.We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).

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Section: Balaji Et Al: Measurements Of Earth System Models In Cmip6supporting

confidence: 52%

Section: Summary and Future Workmentioning

confidence: 99%

CPMIP: measurements of real computational performance of Earth system models in CMIP6

et al. 2017

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“…Current computing technologies are based on increased concurrency of arithmetic and logic, while the speed of computation and memory access itself has stalled. This is driven by many technological constraints, not least of which is the energy budget of computing Charles et al, 2015;Kogge et al, 2008). These massive increases in concurrency pose challenges for HPC applications: an era where existing applications would run faster with little or no effort, simply by upgrading to newer hardware, has ended.…”

Section: Balaji Et Al: Coarse-grained Component Concurrencymentioning

confidence: 99%

Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework

et al. 2016

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Abstract. Climate models represent a large variety of processes on a variety of timescales and space scales, a canonical example of multi-physics multi-scale modeling. Current hardware trends, such as Graphical Processing Units (GPUs) and Many Integrated Core (MIC) chips, are based on, at best, marginal increases in clock speed, coupled with vast increases in concurrency, particularly at the fine grain. Multiphysics codes face particular challenges in achieving finegrained concurrency, as different physics and dynamics components have different computational profiles, and universal solutions are hard to come by.We propose here one approach for multi-physics codes. These codes are typically structured as components interacting via software frameworks. The component structure of a typical Earth system model consists of a hierarchical and recursive tree of components, each representing a different climate process or dynamical system. This recursive structure generally encompasses a modest level of concurrency at the highest level (e.g., atmosphere and ocean on different processor sets) with serial organization underneath.We propose to extend concurrency much further by running more and more lower-and higher-level components in parallel with each other. Each component can further be parallelized on the fine grain, potentially offering a major increase in the scalability of Earth system models.We present here first results from this approach, called coarse-grained component concurrency, or CCC. Within the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS), the atmospheric radiative transfer component has been configured to run in parallel with a composite component consisting of every other atmospheric component, including the atmospheric dynamics and all other atmospheric physics components. We will explore the algorithmic challenges involved in such an approach, and present results from such simulations. Plans to achieve even greater levels of coarse-grained concurrency by extending this approach within other components, such as the ocean, will be discussed.

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“…Targeting different mainstream architectures, Aliaga et al present in [5] an energy comparison study for the CG solver. Concerning more complex applications, Charles et al [20] present an efficiency comparison when running the COSMO-ART simulation model [21] on different CPU architectures. For an agroforestry application, Padoin et al [22] investigate performance and power consumption on a hybrid CPU+GPU architecture, revealing that a changing workload can drastically improve energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Energy efficiency and performance frontiers for sparse computations on GPU supercomputers

Anzt

Tomov

Dongarra

2015

Proceedings of the Sixth International Workshop on Programming Models and Applications for Multicores and Manycores

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In this paper we unveil some energy efficiency and performance frontiers for sparse computations on GPU-based supercomputers. To do this, we consider state-of-the-art implementations of the sparse matrix-vector (SpMV) product in libraries like cuSPARSE, MKL, and MAGMA, and their use in the LOBPCG eigen-solver. LOBPCG is chosen as a benchmark for this study as it combines an interesting mix of sparse and dense linear algebra operations with potential for hardware-aware optimizations. Most notably, LOBPCG includes a blocking technique that is a common performance optimization for many applications. In particular, multiple memory-bound SpMV operations are blocked into a SpM-matrix product (SpMM), that achieves significantly higher performance than a sequence of SpMVs. We provide details about the GPU kernels we use for the SpMV, SpMM, and the LOBPCG implementation design, and study performance and energy consumption compared to CPU solutions. While a typical sparse computation like the SpMV reaches only a fraction of the peak of current GPUs, we show that the SpMM achieves up to a 6× performance improvement over the GPU's SpMV, and the GPU-accelerated LOBPCG based on this kernel is 3 to 5× faster than multicore CPUs with the same power draw, e.g., a K40 GPU vs. two Sandy Bridge CPUs (16 cores). In practice though, we show that currently available CPU implementations are much slower due to missed optimization opportunities. These performance results translate to similar improvements in energy consumption, and are indicative of today's frontiers in energy efficiency and performance for sparse computations on supercomputers.

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Evaluating the performance and energy efficiency of the COSMO-ART model system

Cited by 8 publications

References 42 publications

CPMIP: measurements of real computational performance of Earth system models in CMIP6

CPMIP: measurements of real computational performance of Earth system models in CMIP6

Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework

Energy efficiency and performance frontiers for sparse computations on GPU supercomputers

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