In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

Contemporary GPUs have significantly higher arithmetic throughput than a memory
throughput. Hence, many GPU kernels are memory bound and cannot exploit arithmetic …

With the end of Dennard scaling, architects have increasingly turned to special-purpose
hardware accelerators to improve the performance and energy efficiency for some …

While many of the architectural details of future exascale-class high performance computer
systems are still a matter of intense research, there appears to be a general consensus that …

(57) ABSTRACT A method for optimization of machine learning (ML) work loads on a
graphics processor unit (GPU). The method includes identifying a computation having a …

We introduce a systematic analysis in order to fuse CUDA kernels arising in efficient iterative
methods for the solution of sparse linear systems. Our procedure characterizes the input and …

Determining the optical flow of a video is a compute-intensive task essential for computer
vision. For achieving this processing in real time, the whole algorithm deployment chain …

ILUPACK is a valuable tool for the solution of sparse linear systems via iterative Krylov
subspace-based methods. Its relevance for the solution of real problems has motivated …

Dynamic simulation studies are used to analyze the behavior of power systems after a
disturbance has occurred. Over the last decades, they have become indispensable to …

In this paper, we present an optimized GPU implementation for the induced dimension
reduction algorithm. We improve data locality, combine it with an efficient sparse matrix …