12 citations · 18 across the 6 of their papers we have counts for
7 papers
Customized Monte Carlo Tree Search for LLVM/Polly's Composable Loop Optimization Transformations
Jaehoon Koo, Prasanna Balaprakash, Michael Kruse +3
Polly is the LLVM project's polyhedral loop nest optimizer. Recently, user-directed loop transformation pragmas were proposed based on LLVM/Clang and Polly. The search space expose…
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization (extended version)
Xingfu Wu, Michael Kruse, Prasanna Balaprakash +4
In this paper, we develop a ytopt autotuning framework that leverages Bayesian optimization to explore the parameter space search and compare four different supervised learning met…
Autotuning PolyBench Benchmarks with LLVM Clang/Polly Loop Optimization Pragmas Using Bayesian Optimization
Xingfu Wu, Michael Kruse, Prasanna Balaprakash +4
An autotuning is an approach that explores a search space of possible implementations/configurations of a kernel or an application by selecting and evaluating a subset of implement…
Computing Derivatives for PETSc Adjoint Solvers using Algorithmic Differentiation
J. G. Wallwork, P. Hovland, H. Zhang +1
Most nonlinear partial differential equation (PDE) solvers require the Jacobian matrix associated to the differential operator. In PETSc, this is typically achieved by either an an…
Automatic Differentiation for Adjoint Stencil Loops
Jan Hückelheim, Navjot Kukreja, Sri Hari Krishna Narayanan +3
Stencil loops are a common motif in computations including convolutional neural networks, structured-mesh solvers for partial differential equations, and image processing. Stencil…
Training on the Edge: The why and the how
Navjot Kukreja, Alena Shilova, Olivier Beaumont +4
Edge computing is the natural progression from Cloud computing, where, instead of collecting all data and processing it centrally, like in a cloud computing environment, we distrib…