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The use of accelerators in heterogeneous systems is an established approach in designing petascale applications. Today, Compute Unified Device Architecture (CUDA) offers a rich programming interface for GPU accelerators but requires developers to incorporate several layers of parallelism on both the CPU and the GPU. From this increasing program complexity emerges the need for sophisticated performance tools. This work contributes by analyzing hybrid MPI-CUDA programs for properties based on wait states, such as the critical path, a metric proven to identify application bottlenecks effectively. We developed a tool to construct a dependency graph based on an execution trace and the inherent dependencies of the programming models CUDA and Message Passing Interface (MPI). Thereafter, it detects wait states and attributes blame to responsible activities. Together with the property of being on the critical path, we can identify activities that are most viable for optimization. To evaluate the global impact of optimizations to critical activities, we predict the program execution using a graph-based performance projection. The developed approach has been demonstrated with suitable examples to be both scalable and correct. Furthermore, we establish a new categorization of CUDA inefficiency patterns ensuing from the dependencies between CUDA activities. [ABSTRACT FROM AUTHOR]

In this paper we present a model based on the parallel computational tool of cellular automata (CA) capable of simulating the process of disembarking in a small airplane seat layout, corresponding to Airbus A320/ Boeing 737 layout, in search of ways to make it faster and safer under normal evacuation conditions, as well as emergency scenarios. The proposed model is highly customizable, with the number of exits, the walking speed of passengers, depending on their sex, age and height, and the effects of retrieving and carrying luggage. Additionally, the presence of obstacles in the aisles as well as the emergence of panic being parameters whose values can be varied in order to enlighten the disembarking and emergency evacuation processes are considered in detail. The simulation results were compared to existing aircraft disembarking and evacuation times and indicate the efficacy of the proposed model in investigating and revealing passenger attributes during these processes in all the examined cases. Moreover, we parallelized our code in order to run on a graphics processing unit (GPU) using the CUDA programming language, speeding up the simulation process. Finally, in order to present a fully dynamical anticipative real-time system helpful for decision-making we implemented the proposed CA model in a field programmable gate array (FPGA) device, and recreated the results given by the software simulations in a fraction of the time. We then compared and exported the performance results among a sequential software implementation, the implementation running on a GPU, and a hardware implementation, proving the consequent acceleration that results from the parallel CA implementation in specific hardware. [ABSTRACT FROM AUTHOR]

This paper presents the parallel implementation, using the Compute Unified Device Architecture (CUDA) architecture, of the SCIARA-fv3 Complex Cellular Automata model for simulating lava flows. The computational model is based on a Bingham-like rheology and both flow velocity and the physical time corresponding to a computational step have been made explicit. The parallelization design has involved, among other issues, the application of strategies that can avoid incorrect computation results due to race conditions and achieving the best performance and occupancy of the underlying available hardware. Two hardware types were adopted for testing different versions of the CUDA implementations of the SCIARA-fv3 model, namely the GTX 580 and GTX 680 graphic processors. Despite its computational complexity, carried out experiments of the model parallelization have shown significant performance improvements, confirming that graphic hardware can represent a valid solution for the implementation of Cellular Automata models. [ABSTRACT FROM AUTHOR]

Detecting epistasis, such as 2-SNP interactions, in genome-wide association studies (GWAS) is an important but time consuming operation. Consequently, GPUs have already been used to accelerate these studies, reducing the runtime for moderately-sized datasets to less than 1 hour. However, single-GPU approaches cannot perform large-scale GWAS in reasonable time. In this work we present multiEpistSearch, a tool to detect epistasis that works on GPU clusters. While CUDA is used for parallelization within each GPU, the workload distribution among GPUs is performed with Unified Parallel C++ (UPC++), a novel extension of C++ that follows the Partitioned Global Address Space (PGAS) model. multiEpistSearch is able to analyze large-scale datasets with 5 million SNPs from 10,000 individuals in less than 3 hours using 24 NVIDIA GTX Titans. [ABSTRACT FROM AUTHOR]

Scientific algorithms are designed and implemented in a variety of programming languages. Depending on the exact application, some languages are a better choice than others: some offer a productive environment while others focus on performance. Selecting a language is often difficult, with poor choices resulting in much higher development times.By implementing a case study algorithm in multiple programming languages, we compare their pros and cons. As a case study, we selected the trace transform, an image processing algorithm from the widely used class of integral transforms. We describe each implementation, including a highly optimized version for NVIDIA graphics processing units, and present a productivity overview and an in-depth performance analysis, from which we draw more generic conclusions.We have found that MATLAB is still the best choice overall, but Julia proves an interesting emerging choice. For realistic images, our compute unified device architecture (CUDA) implementation offers the best performance, albeit at a high development cost. [ABSTRACT FROM AUTHOR]

The Stokes equations are frequently used to simulate geodynamic processes, including mantle convection, lithospheric dynamics, lava flow, and among others. In this study, the multigrid (MG) method is adopted to solve Stokes and continuity equations with strongly temperature-dependent viscosity. By taking advantage of the enhanced computing power of graphics processing units (GPUs) and the new version of Matlab 2010b, MG codes are optimized through Compute Unified Device Architecture (CUDA). Herein, we illustrate the approach that implements a GPU-based MG solver with Red–Black Gauss–Seidel (RBGS) smoother for the three-dimensional Stokes and continuity equations, in a hope that it helps solve the synthetic incompressible sinking problem in a cubic domain with strongly variable viscosity, and finally analyze our solver’s efficiency on a GPU. [ABSTRACT FROM AUTHOR]