GPU Performance and Power Tuning Using Regression Trees

Tuning And Understanding MILC Performance In Cray XK6 GPU Clusters

Graphics Processing Units (GPU) are becoming increasingly popular in high performance computing due to their high performance, high power efficiency, and low cost. Lattice QCD is one of the fields that has successfully adopted GPUs and scaled to hundreds of them. In this paper, we report our Cray XK6 experience in profiling and understanding performance for MILC, one of the Lattice QCD computat...

GPU - Accelerated Back - Projection Revisited : Squeezing Performance by Careful Tuning

— In recent years, GPUs have become an increasingly popular tool in computed tomography (CT) reconstruction. In this paper, we discuss performance optimization techniques for a GPU-based filtered-backprojection reconstruction implementation. We explore the different optimization techniques we used and explain how those techniques affected performance. Our results show a nearly 50% increase in p...

Differential Performance Debugging with Discriminant Regression Trees

Differential performance debugging is a technique to find performance problems. It applies in situations where the performance of a program is (unexpectedly) different for different classes of inputs. The task is to explain the differences in asymptotic performance among various input classes in terms of program internals. We propose a data-driven technique based on discriminant regression tree...

Nonsmooth Optimization using Classification and Regression Trees

Nonsmooth local optimization problems occur in many fields, including engineering, mathematics, and economics. In economics, nonsmooth problems can be found in finance, mathematical economics and production theory. Examples include, nonsmooth utility maximization and exact penalty functions. However there are few convergent optimization algorithms to solve general nonsmooth or discontinuous pro...

GPU-Based Deep Learning Inference: A Performance and Power Analysis