activity
20202025
most citedBenchmarking optimization algorithms for auto-tuning GPU kernels

16 citations · 17 across the 8 of their papers we have counts for

collaborators

8 papers

cs.PF2025

PowerSensor3: A Fast and Accurate Open Source Power Measurement Tool

Steven van der Vlugt, Leon Oostrum, Gijs Schoonderbeek +4

Power consumption is a major concern in data centers and HPC applications, with GPUs typically accounting for more than half of system power usage. While accurate power measurement…

cs.DC2024

Bringing Auto-tuning to HIP: Analysis of Tuning Impact and Difficulty on AMD and Nvidia GPUs

Milo Lurati, Stijn Heldens, Alessio Sclocco +1

Many studies have focused on developing and improving auto-tuning algorithms for Nvidia Graphics Processing Units (GPUs), but the effectiveness and efficiency of these approaches o…

cs.DC2023

Kernel Launcher: C++ Library for Optimal-Performance Portable CUDA Applications

Stijn Heldens, Ben van Werkhoven

Graphic Processing Units (GPUs) have become ubiquitous in scientific computing. However, writing efficient GPU kernels can be challenging due to the need for careful code tuning. T…

cs.DC2023

Towards a Benchmarking Suite for Kernel Tuners

Jacob O. Tørring, Ben van Werkhoven, Filip Petrovic +3

As computing system become more complex, it is becoming harder for programmers to keep their codes optimized as the hardware gets updated. Autotuners try to alleviate this by hidin…

cs.DC2022

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Richard Schoonhoven, Bram Veenboer, Ben van Werkhoven +1

Graphics Processing Units (GPUs) have revolutionized the computing landscape over the past decade. However, the growing energy demands of data centres and computing facilities equi…

cs.DC202216 cited

Benchmarking optimization algorithms for auto-tuning GPU kernels

Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg

Recent years have witnessed phenomenal growth in the application, and capabilities of Graphical Processing Units (GPUs) due to their high parallel computation power at relatively l…