GPUs and other accelerators are popular devices for accelerating compute-intensive,
parallelizable applications. However, programming these devices is a difficult task. Writing …

Computing derivatives is key to many algorithms in scientific computing and machine
learning such as optimization, uncertainty quantification, and stability analysis. Enzyme is a …

In this article we present SkePU 2, the next generation of the SkePU C++ skeleton
programming framework for heterogeneous parallel systems. We critically examine the …

The recent advent of compute-intensive GPU architecture has allowed application
developers to explore high-order 3D stencils for better computational accuracy. A common …

In this paper, we present a methodology to understand GPU microarchitectural features and
improve performance for compute-intensive kernels. The methodology relies on a reverse …

General-purpose GPUs have been widely utilized to accelerate parallel applications. Given
a relatively complex programming model and fast architecture evolution, producing efficient …

In an initiative to advance the open-source electronic design automation (EDA) and
hardware design community, Google has been spearheading a global collaborative effort …

Recent refactoring of the GPUSPH codebase have uncovered some of the limitations of the
official CUDA compiler (nvcc) offered by NVIDIA when dealing with some C++ constructs …

Modern GPU applications, such as machine learning (ML), can only partially utilize GPUs,
leading to GPU underutilization in cloud environments. Sharing GPUs across multiple …

GPUs are powerful, massively parallel processors, which require a vast amount of thread
parallelism to keep their thousands of execution units busy, and to tolerate latency when …