Multi-GPUs appear as an attractive platform to speed up data-parallel GPGPU computation. The idea of split-and-merge execution has been introduced to accelerate the parallelism of multiple GPUs even further. However, it has not been explored before how to exploit such an idea for real-time multi-GPU systems properly. This paper presents an open-source real-time multi-GPU scheduling framework, c...

We are focusing on an iterative solver for the three-dimensional Helmholtz equation on multi-GPU using CUDA (Compute Unified Device Architecture). The Helmholtz equation discretized by a second order finite difference scheme is solved with Bi-CGSTAB preconditioned by a shifted Laplace multigrid method. Two multi-GPU approaches are considered: data parallelism and split of the algorithm. Their i...

Recent embedded processors become to be multi-cored, due to the increased power consumption by higher operating frequencies. Multi-core processors stimulate applications to be parallelized. Since general purpose CPU has small number of core, which is optimized for serial processing, it has a limitation of parallel processing. To overcome this limitation, GPU is used for the parallel processing....

The graphics processing unit (GPU) is becoming a very powerful platform to accelerate graphics and data-parallel compute-intensive applications. It significantly outperforms traditional multi-core processors in performance and energy efficiency. Its application domains also range widely from embedded systems to high-performance computing systems. However, operating systems support is not adequa...

Context. The cryptographically secure pseudo-random number generator Blum Blum Shub (BBS) is a simple algorithm with a strong security proof, however it requires very large numbers to be secure, which makes it computationally heavy. The Graphics Processing Unit (GPU) is a common vector processor originally dedicated to computer-game graphics, but has since been adapted to perform general-purpos...

Sorting is a common problem in computer science. There are a lot of wellknown sorting algorithms created for sequential execution on a single processor. Recently, many-core and multi-core platforms have enabled the creation of wide parallel algorithms. We have standard processors that consist of multiple cores and hardware accelerators, like the GPU. Graphic cards, with their parallel architect...

An approximation for fast digital hologram generation is implemented on a central processing unit (CPU), a graphics processing unit (GPU), and a multi-GPU computational platform. The computational performance of the method on each platform is measured and compared. The computational speed on the GPU platform is much faster than on a CPU, and the algorithm could be further accelerated on a multi...

Medical image registration is time-consuming but can be sped up employing parallel processing on the GPU. Normalized mutual information (NMI) is a well performing similarity measure for performing multi-modal registration. We present CUDA based solutions for computing NMI on the GPU and compare the results obtained by rigidly registering multi-modal data sets with a CPU based implementation. Ou...

As part of the ITK v4 project efforts, we have developed ITK filters for physics-based non-rigid registration (PBNRR), which satisfies the following requirements: account for tissue properties in the registration, improve accuracy compared to rigid registration, and reduce execution time using GPU and multi-core accelerators. The implementation has three main components: (1) Feature Point Selec...