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Vincent Baudoui
Round-off error propagation in large-scale applications
Round-off errors coming from numerical calculation finite precision can lead to catastrophic losses in significant numbers when they accumulate. They will become more and more overriding in the future as the problem size increases with the refinement of numerical simulations. Existing analytical bounds for round-off errors are known to be poorly scalable and they become quite useless for large problems. That is why the propagation of round-off errors throughout a computation needs to be better understood in order to ensure large-scale application results accuracy. We study here a round-off error estimation method based on first order derivatives computed thanks to algorithmic differentiation techniques. It can help following the error propagation through a computational graph and identifying the sensitive sections of a code. It has been experimented on well known LU decomposition algorithms that are widely used to solve linear systems. We will present some examples as well as challenges that need to be tackled as part of future research work in order to set up a strategy to analyze round-off error propagation in large-scale problems.
Luc Jaulmes
Checkpointless exact recovery techniques for Krylov-based iterative methods
By exploiting inherent redundancy in iterative solvers, especially Krylov-subspace methods, we can recover from non-silent errors in data without reverting to techniques like checkpointing. We implemented this recovery scheme for the Conjugate Gradient (CG) and its Preconditioned variant (PCG) and show near-zero overheads without faults, and fast recoveries that preserve all convergence properties of the solver. Using the asynchronous task-based programming model OmpSs, these overheads are even further minimized.
Lokman Rahmani
Smart In Situ Visualization for Climate SimulationsThe increasing gap between computational power and I/O performance in new supercomputers has started to drive a shift from an offline approach to data analysis to an inline approach, termed in situ visualization (ISV). While most visualization software now provides ISV, they typically visualize large dumps of unstructured data, by rendering everything at the highest possible resolution. This often negatively impacts the performance of simulations that support ISV, in particular when ISV is performed interactively, as in situ visualization requires synchronization with the simulation. In this work, we advocate for a smarter method of performing ISV. Our approach is data-driven: it aims to detect potentially interesting regions in the generated dataset in order to feed ISV frameworks with “the interesting” subset of the data produced by the simulation. While this method mitigates the load on ISV frameworks by making them more efficient and more interactive, it also helps scientists focus on the relevant part of their data. We investigate smart ISV in the context of a climate simulation, with a set of generic filters derived from information theory, statistics and image processing, and show the tradeoff between performance and quality of visualization.
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