As both CPUs and GPUs become employed in a wide range of applications, it has been
acknowledged that both of these Processing Units (PUs) have their unique features and …

Fully utilizing the power of modern heterogeneous systems requires judiciously dividing
work across all of the available computational devices. Existing approaches for partitioning …

Heterogeneous architectures have experienced a great development thanks to their
excellent cost/performance ratio and low power consumption. But heterogeneity significantly …

A challenge that heterogeneous system programmers face is leveraging the performance of
all the devices that integrate the system. This paper presents Sigmoid, a new load balancing …

For the rapid growth computation requirements in big data and artificial intelligence area,
CPU-GPU heterogeneous clusters can provide more powerful computing capacity …

Modern standards in High Performance Computing (HPC) have started to consider energy
consumption and power draw as a limiting factor. New and more complex architectures have …

We developed a fast and effective bundle adjustment method for large-scale datasets. The
preconditioned conjugate gradient (PCG) algorithm and GPU parallel computing technology …

The calculation of pairwise correlation coefficient on a dataset, known as the correlation
matrix, is often used in data analysis, signal processing, pattern recognition, image …

The parallel computation capabilities of modern graphics processing units (GPUs) have
attracted increasing attention from researchers and engineers who have been conducting …

The advent of multicore systems, joined to the potential acceleration of the graphics
processing units, alleviates some well known important architectural problems at the …