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Is Source-Code Isolation Viable for Performance Characterization?

, , , and . ICPP, page 977-984. IEEE Computer Society, (2013)

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Comparing perturbation models for evaluating stability of neuroimaging pipelines., , , , , , and . Int. J. High Perform. Comput. Appl., (2020)Is Source-Code Isolation Viable for Performance Characterization?, , , and . ICPP, page 977-984. IEEE Computer Society, (2013)ASK: Adaptive Sampling Kit for Performance Characterization., , , and . Euro-Par, volume 7484 of Lecture Notes in Computer Science, page 89-101. Springer, (2012)Stochastic Rounding Variance and Probabilistic Bounds: A New Approach., , , and . SIAM J. Sci. Comput., 45 (5): 255- (October 2023)High Performance Computing code optimizations: Tuning performance and accuracy.. (2022)Evaluating architecture and compiler design through static loop analysis., , , and . HPCS, page 535-544. IEEE, (2013)Confidence Intervals for Stochastic Arithmetic., , , , , and . ACM Trans. Math. Softw., 47 (2): 10:1-10:33 (2021)Comparing Perturbation Models for Evaluating Stability of Post-Processing Pipelines in Neuroimaging., , , , , , and . CoRR, (2019)Scalable Work-Stealing Load-Balancer for HPC Distributed Memory Systems., , , , , , , and . Euro-Par Workshops, volume 11339 of Lecture Notes in Computer Science, page 146-158. Springer, (2018)Verificarlo: Checking Floating Point Accuracy through Monte Carlo Arithmetic., , and . ARITH, page 55-62. IEEE Computer Society, (2016)