Efficient implementation of the Shack-Hartmann centroid extraction for edge computing.

Adaptive optics (AO) is an established technique to measure and compensate for optical aberrations. One of its key components is the wavefront sensor (WFS), which is typically a Shack-Hartmann sensor (SH) capturing an image related to the aberrated wavefront. We propose an efficient implementation of the SH-WFS centroid extraction algorithm, tailored for edge computing. In the edge-computing paradigm, the data are elaborated close to the source (i.e., at the edge) through low-power embedded architectures, in which CPU computing elements are combined with heterogeneous accelerators (e.g., GPUs, field-programmable gate arrays). Since the control loop latency must be minimized to compensate for the wavefront aberration temporal dynamics, we propose an optimized algorithm that takes advantage of the unified CPU/GPU memory of recent low-power embedded architectures. Experimental results show that the centroid extraction latency obtained over spot images up to 700×700 pixels wide is smaller than 2 ms. Therefore, our approach meets the temporal requirements of small- to medium-sized AO systems, which are equipped with deformable mirrors having tens of actuators.

Correction to: cuRnet: an R package for graph traversing on GPU.

After publication of this supplement article [1], it was brought to our attention that reference 10 and reference 12 in the article are incorrect.

cuRnet: an R package for graph traversing on GPU.

BACKGROUND: R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms. RESULTS: This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis. CONCLUSIONS: cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.

APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks.

MOTIVATION: Biological network querying is a problem requiring a considerable computational effort to be solved. Given a target and a query network, it aims to find occurrences of the query in the target by considering topological and node similarities (i.e. mismatches between nodes, edges, or node labels). Querying tools that deal with similarities are crucial in biological network analysis because they provide meaningful results also in case of noisy data. In addition, as the size of available networks increases steadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the design of more efficient and accurate solutions. RESULTS: This paper presents APPAGATO, a stochastic and parallel algorithm to find approximate occurrences of a query network in biological networks. APPAGATO handles node, edge and node label mismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared with existing tools, it provides higher performance as well as statistically significant more accurate results. Tests have been performed on protein-protein interaction networks annotated with synthetic and real gene ontology terms. Case studies have been done by querying protein complexes among different species and tissues. AVAILABILITY AND IMPLEMENTATION: APPAGATO has been developed on top of CUDA-C ++ Toolkit 7.0 framework. The software is available online http://profs.sci.univr.it/∼bombieri/APPAGATO CONTACT: rosalba.giugno@univr.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.