Classical computing plays a critical role in the advancement of quantum frontiers in the NISQ
era. In this spirit, this work uses classical simulation to bootstrap Variational Quantum …

Reinforcement learning (RL) is emerging as a powerful technique for solving complex code
optimization tasks with an ample search space. While promising, existing solutions require a …

As we enter the exascale computing era, efficiently utilizing power and optimizing the
performance of scientific applications under power and energy constraints has become …

Deep learning model inference is a key service in many businesses and scientific discovery
processes. This paper introduces Ribbon, a novel deep learning inference serving system …

HPC is a heterogeneous world in which host and device code are interleaved throughout
the application. Given the significant performance advantage of accelerators, device code …

This paper presents GPTuneCrowd, a crowd-based autotuning framework for tuning high-
performance computing applications. GPTuneCrowd collects performance data from various …

Growing heterogeneity and configurability in HPC architectures has made auto-tuning
applications and runtime parameters on these systems very complex. Users are presented …

We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose
autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the …

We present Phronesis, a learning framework for efficiently modeling the performance of data
analytic workloads as a function of their high-dimensional software configuration …

Porous media processes involve various physical phenomena such as mechanical
deformation, transport, and fluid flow. Accurate simulations must capture the strong …