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Transparent Checkpointing for Automatic Differentiation of Program Loops Through Expression Transformations., , , , , and . ICCS (3), volume 14075 of Lecture Notes in Computer Science, page 483-497. Springer, (2023)Dynamic Automatic Differentiation of GPU Broadcast Kernels., , , , and . CoRR, (2018)Reverse-mode automatic differentiation and optimization of GPU kernels via enzyme., , , , , , and . SC, page 61. ACM, (2021)Quantifying Robustness in a Spatial Model of Metabolism-Boundary Co-Construction., , , and . ALIFE, page 514-521. MIT Press, (2014)Batched Second-Order Adjoint Sensitivity for Reduced Space Methods., , , , , , and . PPSC, page 60-71. SIAM, (2022)Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs., , , , , , , , , and 1 other author(s). DLS@SC, page 1-11. IEEE, (2019)Evaluating performance and portability of high-level programming models: Julia, Python/Numba, and Kokkos on exascale nodes., , , , , , , , , and . IPDPS Workshops, page 373-382. IEEE, (2023)Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients., and . NeurIPS, (2020)Bring the BitCODE-Moving Compute and Data in Distributed Heterogeneous Systems., , , , , and . CLUSTER, page 12-22. IEEE, (2022)Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation., , , , , , , and . SC, page 60:1-60:18. IEEE, (2022)