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For a 0.2° test with 100 tracers, the implementation scales efficiently to 10,000 MPI tasks.
Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model misspecification, and that it scales efficiently.
Scales efficiently with the number of VMs, saturating the link for even smaller messages when 40 single-core VMs put pressure on the network adapters.
It is composed by a verification architecture based on the Scale-Invariant Feature Transform algorithm (SIFT) with a vocabulary tree, providing a scheme that scales efficiently to a large number of features.
Our prototype of semantics-aware traffic analytics and reasoning, illustrated and experimented in Dublin Ireland, but also tested in Bologna Italy, Miami USA and Rio Brazil works and scales efficiently with real, historical together with live and heterogeneous stream data.
The experiments show that the data-privatizing model scales efficiently on medium scale multi-socket, multi-core systems (up to 48 cores) while regardless of algorithmic and scheduling optimizations, the sharing approach is unable to reach acceptable scalability on more than one socket.
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Numerical implementations that scale efficiently for large-scale problems are possible for nonsmooth DAEs.
Thus, overwhelming data flow also requires database tier to be scaled efficiently and dynamically besides other tiers in cloud platform.
These applications achieve near-expert performance on a single machine and scale efficiently to hundreds of machines, enabling formerly long-running big video data analysis tasks to be carried out in minutes to hours.
It is optimized to scale efficiently for analyzing very large datasets, and is designed to integrate well with reproducible and open research workflows.
A computationally relevant theory of nonsmooth DAEs (i.e. well-posedness and sensitivity analysis) has recently been established (Stechlinski and Barton, 2016a, 2017) which is suitable for numerical implementations that scale efficiently for large-scale dynamic optimization problems.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com