As scientists are designing increasingly complex and intricate models, the prominent way today to achieve acceptable execution time without sacrificing accuracy is through parallel computing. These techniques can improve execution time either on the level of the optimization methods or on the level of the model evaluations. This paper outlines an automatic parallelization approach for the latter. Processor specific procedures with embedded communication primitives are generated from static schedules produced by an evolutionary algorithm. These are passed to an optimizing compiler to avoid the overhead of typical task runtime systems. The two key insights are that the parallel structure of probabilistic models is revealed when the data is combined with the model and that static schedules can be combined into more robust schedules that can deal with varying load imbalance. For this, LogP model parameters and execution time of each computational task are measured and fed into a discrete event simulator to estimate the running time on the target parallel system. Performance is evaluated with three pharmacological models with different characteristics. The first model lacks enough exploitable parallelism while up to approximately 6x and 8x improvements are achieved for the other models. Compared to a theoretical system with infinite processors and no communication delay, this equates to exploiting 66% and 99% of the available parallelism. Performance improves even when load imbalance varies.

Sequential Monte Carlo methods are a useful tool to tackle non-linear problems in a Bayesian setting. A target posterior distribution is approximated by moving a set of weighted particles through a sequence of distributions. To counteract degeneracy caused by sequentially changing the underlying distribution, particles occasionally need to be resampled. Deciding if this is necessary requires a reduction operation on the weights after each update. Hence, scalability on a cluster is not only determined by the number of particles used, but also by how well load is balanced. This paper shows how speculative execution in Sequential Monte Carlo with Markov Chain Monte Carlo steps can improve parallel scalability. The key insight is that decisions taken based on the reduction result in each step can be accurately predicted. Consequently, synchronization inherent in the reduction can, in most cases, be avoided, relaxing the limit imposed by load imbalance. Particles are renumbered during resampling to further improve accuracy. Multiple test scenarios, each with different load balance characteristics, are studied empirically on a compute cluster. Tests show that when decisions are predicted correctly, execution time is reduced drastically for use cases with high load imbalance. Furthermore, the maximum theoretical gain, derived from execution characteristics, is compared with the measured improvement to verify that most speculative evaluations are actually useful. If predictions are incorrect, or load is balanced, speculation has no measurable negative impact. Performance is also evaluated in a weak scaling setting on cluster with 36 cores in each system.

BACKGROUND: Over the last years, substantial effort has been put into enhancing our arsenal in fighting epidemics from both technological and theoretical perspectives with scientists from different fields teaming up for rapid assessment of potentially urgent situations. This paper focusses on the computational aspects of infectious disease models and applies commonly available graphics processing units (GPUs) for the simulation of these models. However, fully utilizing the resources of both CPUs and GPUs requires a carefully balanced heterogeneous approach. RESULTS: The contribution of this paper is twofold. First, an efficient GPU implementation for evaluating a small-scale ODE model; here, the basic S(usceptible)-I(nfected)-R(ecovered) model, is discussed. Second, an asynchronous particle swarm optimization (PSO) implementation is proposed where batches of particles are sent asynchronously from the host (CPU) to the GPU for evaluation. The ultimate goal is to infer model parameters that enable the model to correctly describe observed data. The particles of the PSO algorithm are candidate parameters of the model; finding the right one is a matter of optimizing the likelihood function which quantifies how well the model describes the observed data. By employing a heterogeneous approach, in which both CPU and GPU are kept busy with useful work, speedups of 10 to 12 times can be achieved on a moderate machine with a high-end consumer GPU as compared to a high-end system with 32 CPU cores. CONCLUSIONS: Utilizing GPUs for parameter inference can bring considerable increases in performance using average host systems with high-end consumer GPUs. Future studies should evaluate the benefit of using newer CPU and GPU architectures as well as applying this method to more complex epidemiological scenarios.

Summary This paper describes ExaShark, a hybrid n‐dimensional array toolkit offered as a high‐level library for scientists to compute large‐scale simulations. It offers a global‐array–like interface while its runtime can be configured to use shared memory threading techniques, inter‐node distribution techniques, or combinations of both. ExaShark takes advantage of the latest HPC technologies, helping to scale to future generation systems. It has been used to develop several scientific applications including stencil codes, solvers, and matrix factorization algorithms. These applications are used to demonstrate that it improves on the state of the art by providing a user‐friendly, generic API without sacrificing performance.

Markov Chain Monte Carlo methods provide a tool for tackling high dimensional problems. With many-core systems readily available today, it is no surprise that leveraging parallelism in these samplers has been a subject of recent research. The focus has been on solutions for shared-memory architectures, however these perform poorly in a distributed-memory environment. This paper introduces a fully decentralized version of an affine invariant sampler. By observing that a pseudorandom number generator makes stochastic algorithms deterministic, communication is both minimized and hidden by computation. Two cases at opposite ends of the communication-to-computation ratio spectrum are used during evaluation against the currently available master-slave solution, where a more than tenfold reduction in execution time is measured.

Matrix factorization is a common machine learning technique for recommender systems. Despite its high prediction accuracy, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used on large scale data because of its high computational cost. In this paper we propose a distributed high-performance parallel implementation of BPMF on shared memory and distributed architectures. We show by using efficient load balancing using work stealing on a single node, and by using asynchronous communication in the distributed version we beat state of the art implementations.

Using the matrix factorization technique in machine learning is very common mainly in areas like recommender systems. Despite its high prediction accuracy and its ability to avoid over-fitting of the data, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used because of the prohibitive cost. In this paper, we propose a comprehensive parallel implementation of the BPMF using Gibbs sampling on shared and distributed architectures. We also propose an insight of a GPU-based implementation of this algorithm.

This paper describes a novel strategy to enhance underwater videos and images. Built on the fusion principles, our strategy derives the inputs and the weight measures only from the degraded version of the image. In order to overcome the limitations of the underwater medium we define two inputs that represent color corrected and contrast enhanced versions of the original underwater image/frame, but also four weight maps that aim to increase the visibility of the distant objects degraded due to the medium scattering and absorption. Our strategy is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. Our fusion framework also supports temporal coherence between adjacent frames by performing an effective edge preserving noise reduction strategy. The enhanced images and videos are characterized by reduced noise level, better exposed-ness of the dark regions, improved global contrast while the finest details and edges are enhanced significantly. In addition, the utility of our enhancing technique is proved for several challenging applications.

We present a novel method of simulating wave effects in graphics using ray-based renderers with a new function: the Wave BSDF (Bidirectional Scattering Distribution Function). Reflections from neighboring surface patches represented by local BSDFs are mutually independent. However, in many surfaces with wavelength-scale microstructures, interference and diffraction requires a joint analysis of reflected wavefronts from neighboring patches. We demonstrate a simple method to compute the BSDF for the entire microstructure, which can be used independently for each patch. This allows us to use traditional ray-based rendering pipelines to synthesize wave effects. We exploit the Wigner Distribution Function (WDF) to create transmissive, reflective, and emissive BSDFs for various diffraction phenomena in a physically accurate way. In contrast to previous methods for computing interference, we circumvent the need to explicitly keep track of the phase of the wave by using BSDFs that include positive as well as negative coefficients. We describe and compare the theory in relation to well-understood concepts in rendering and demonstrate a straightforward implementation. In conjunction with standard raytracers, such as PBRT, we demonstrate wave effects for a range of scenarios such as multibounce diffraction materials, holograms, and reflection of high-frequency surfaces.

In this paper we introduce a novel strategy that effectively enhance the visibility of underwater images. Our method is build-up on the fusion strategy that takes a sequence of inputs derived from the initial image. Practically, our fusion-based method aims to yield a final image that overcomes the deficiencies existing in the degraded input images by employing several weight maps that discriminate the regions characterized by poor visibility. The extensive experiments demonstrate the utility of our solution since the visibility range of the underwater images is significantly increased by improving both the scene contrast and the color appearance.

Depth information opens up a lot of possibilities for meaningful editing of photographs. So far, it has only been possible to acquire depth information by either using additional hardware, restrictive scene assumptions or extensive manual input. We developed a novel user-assisted technique for creating adequate depth maps with an intuitive stroke-based user interface. Starting from absolute depth constraints as well as surface normal constraints, we optimize for a feasible depth map over the image. We introduce a suitable smoothness constraint that respects image edges and accounts for slanted surfaces. We illustrate the usefulness of our technique by several applications such as depth of field reduction and advanced compositing.

We present an approach for recovering the reflectance of a static scene with known geometry from a collection of images taken under distant, unknown illumination. In contrast to previous work, we allow the illumination to vary between the images, which greatly increases the applicability of the approach. Using an all-frequency relighting framework based on wavelets, we are able to simultaneously estimate the per-image incident illumination and the persurface point reflectance. The wavelet framework allows for incorporating various reflection models. We demonstrate the quality of our results for synthetic test cases as well as for several datasets captured under laboratory conditions. Combined with multi-view stereo reconstruction, we are even able to recover the geometry and reflectance of a scene solely using images collected from the Internet.

The shape and reflectance of complex objects, for use in computer graphics applications, cannot always be acquired using specialized equipment due to cost or pratical considerations. We want to provide an easy and cost-effective way for the approximate recovery of both shape and spatially-varying reflectance of objects using commodity hardware. In this paper, we present an image-based technique for recovering 3D shape and spatially-varying reflectance properties from a sparse set of photographs, taken under varying illumination. Our technique models the reflectance with a set of low-parameter BRDFs without knowledge of the location of the light-sources or camera. This results an a flexible and portable system that can be used in the field. We successfully apply the approach to several objects (synthetic and real), recovering shape and reflectance. The acquired information can then be used to render the object with modifications to geometry and lighting via traditional rendering methods. 1