12 citations · 43 across the 19 of their papers we have counts for
19 papers
Transfer-Learning-Based Autotuning Using Gaussian Copula
Thomas Randall, Jaehoon Koo, Brice Videau +6
As diverse high-performance computing (HPC) systems are built, many opportunities arise for applications to solve larger problems than ever before. Given the significantly increase…
Efficient precision simulation of processes with many-jet final states at the LHC
Enrico Bothmann, Taylor Childers, Christian Guetschow +5
We present a scalable technique for the simulation of collider events with multi-jet final states, based on an improved parton-level event file format. The method is implemented fo…
Understanding Automatic Differentiation Pitfalls
Jan Hückelheim, Harshitha Menon, William Moses +3
Automatic differentiation, also known as backpropagation, AD, autodiff, or algorithmic differentiation, is a popular technique for computing derivatives of computer programs accura…
Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis
Ji Liu, Ed Younis, Mathias Weiden +3
We propose a novel hierarchical qubit mapping and routing algorithm. First, a circuit is decomposed into blocks that span an identical number of qubits. In the second stage permuta…
Model Checking Race-freedom When "Sequential Consistency for Data-race-free Programs" is Guaranteed
Wenhao Wu, Jan Hückelheim, Paul D. Hovland +2
Many parallel programming models guarantee that if all sequentially consistent (SC) executions of a program are free of data races, then all executions of the program will appear t…
ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales
Xingfu Wu, Prasanna Balaprakash, Michael Kruse +7
As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical…
QContext: Context-Aware Decomposition for Quantum Gates
Ji Liu, Max Bowman, Pranav Gokhale +4
In this paper we propose QContext, a new compiler structure that incorporates context-aware and topology-aware decompositions. Because of circuit equivalence rules and resynthesis,…
Memory-Efficient Differentiable Programming for Quantum Optimal Control of Discrete Lattices
Xian Wang, Paul Kairys, Sri Hari Krishna Narayanan +2
Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE, which suffer from exponential growth in storage with increasing number of qubits a…
Reducing Memory Requirements of Quantum Optimal Control
Sri Hari Krishna Narayanan, Thomas Propson, Marcelo Bongarti +2
Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE, which suffer from exponential growth in storage with increasing number of qubits a…
TROPHY: Trust Region Optimization Using a Precision Hierarchy
Richard J Clancy, Matt Menickelly, Jan Hückelheim +3
We present an algorithm to perform trust-region-based optimization for nonlinear unconstrained problems. The method selectively uses function and gradient evaluations at different…
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…
Combining Checkpointing and Data Compression to Accelerate Adjoint-Based Optimization Problems
Navjot Kukreja, Jan Hueckelheim, Mathias Louboutin +3
Seismic inversion and imaging are adjoint-based optimization problems that process up to terabytes of data, regularly exceeding the memory capacity of available computers. Data com…
Report of the HPC Correctness Summit, Jan 25--26, 2017, Washington, DC
Ganesh Gopalakrishnan, Paul D. Hovland, Costin Iancu +6
Maintaining leadership in HPC requires the ability to support simulations at large scales and fidelity. In this study, we detail one of the most significant productivity challenges…
Software Abstractions and Methodologies for HPC Simulation Codes on Future Architectures
A. Dubey, S. Brandt, R. Brower +9
Large, complex, multi-scale, multi-physics simulation codes, running on high performance com-puting (HPC) platforms, have become essential to advancing science and engineering. The…