4 papers
ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations
Afif Boudaoud, Lisa Gaedke-Merzhäuser, Alexandros Nikolaos Ziogas +6
Spatio-temporal Bayesian inference drives environmental and health sciences using latent Gaussian models. Integrated Nested Laplace Approximations (INLA) enable inference for these…
LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization
Massinissa Merouani, Afif Boudaoud, Riyadh Baghdadi
The advancement of machine learning for compiler optimization, particularly within the polyhedral model, is constrained by the scarcity of large-scale, public performance datasets.…
PerfDojo: Automated ML Library Generation for Heterogeneous Architectures
Andrei Ivanov, Siyuan Shen, Gioele Gottardo +5
The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a signifi…
DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing
Afif Boudaoud, Alexandru Calotoiu, Marcin Copik +1
Automatic differentiation (AD) is a set of techniques that systematically applies the chain rule to compute the gradients of functions without requiring human intervention. Althoug…