The polyhedral model for loop parallelization has proved to be an effective tool for advanced
optimization and automatic parallelization of programs in higher-level languages. Yet, to …

This article addresses the compilation of a sequential program for parallel execution on a
modern GPU. To this end, we present a novel source-to-source compiler called PPCG …

Programming today's increasingly complex heterogeneous hardware is difficult, as it
commonly requires the use of data-parallel languages, pragma annotations, specialized …

Recent advances in multi-core and many-core processors requires programmers to exploit
an increasing amount of parallelism from their applications. Data parallel languages such as …

The shift toward parallel processor architectures has made programming and code
generation increasingly challenging. To address this programmability challenge, this article …

Abstract Memristor-based, non-von-Neumann architectures performing tensor operations
directly in memory are a promising approach to address the ever-increasing demand for …

Automatically parallelizing loop nests into CUDA kernels must exploit the full potential of
GPUs to obtain high performance. One state-of-the-art approach makes use of the …

Parallelism has become one of the most extended paradigms used to improve performance.
However, it forces software developers to adapt applications and coding mechanisms to …

In many applications, such as signal and image processing, most computation time is spent
within loops. Therefore, these loops are ideal candidates for performance increase when …

Spiking neural networks (SNN) are among the most computationally intensive types of
simulation models, with node counts on the order of up to 1011. Currently, there is intensive …