Augmented Reality for Urban Simulation Visualization

Visualizations of large simulations are not only computationally intensive but also difficult for the viewer to interpret, due to the huge amount of data to be processed. The case of urban wind flow simulations proves the benefits of mobile Augmented Reality visualizations, both in terms of selection of data relevant to the user and facilitated and comprehensible access to simulation results

GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement

In hardware-aware high performance computing, block-asynchronous iteration and mixed precision iterative refinement are two techniques that may be used to leverage the computing power of SIMD accelerators like GPUs in the iterative solution of linear equation systems. although they use a very different approach for this purpose, they share the basic idea of compensating the convergence properties of an inferior numerical algorithm by a more efficient usage of the provided computing power. In this paper, we analyze the potential of combining both techniques. Therefore, we derive a mixed precision iterative refinement algorithm using a block-asynchronous iteration as an error correction solver, and compare its performance with a pure implementation of a block-asynchronous iteration and an iterative refinement method using double precision for the error correction solver. For matrices from the University of Florida Matrix collection, we report the convergence behaviour and provide the total solver runtime using different GPU architectures

Employing a High-Level Language for Porting Numerical Applications to Reconfigurable Hardware

The deployment of FPGAs has become more and more common over the last years. Many applications have since then been accelerated by porting advantageous parts onto FPGA hardware. High-level, C-like programming languages and advanced tools such as Impulse CoDeveloper that produce hardware descriptions can potentially help with this task. We showcase the applicability of this new approach to FPGA acceleration in terms of solving the Poisson equation with the conjugate gradient (CG) method and a red-black symmetric successive over-relaxation (SSOR) preconditioner as a model problem. In this case, the CPU executes the CG method while an FPGA takes over the red-black SSOR preconditioning part. We compare a purely CPU-based algorithm to our FPGA-extended approach in order to evaluate the maturity and applicability of high-level language translators with regard to accelerating numerical applications

Software Transactional Memory, OpenMP and Pthread implementations of the Conjugate Gradients Method - a Preliminary Evaluation

This paper shows the runtime and cache-efficiency of parallel implementations of the Conjugate Gradients Method based on the three paradigms Software Transactional Memory (STM), OpenMP and Pthreads. While the two last named concepts are used to manage parallelization as well as synchronization, STM was designed to handle only the latter. In our work we disclose that an improved cache efficiency does not necessarily lead to a better execution time because the execution time is dominated by the thread wait time at the barriers

GPU-Accelerated Asynchronous Error Correction for Mixed Precision Iterative Refinement

In hardware-aware high performance computing, block-asynchronous iteration and mixed precision iterative refinement are two techniques that may be used to leverage the computing power of SIMD accelerators like GPUs in the iterative solution of linear equation systems. although they use a very different approach for this purpose, they share the basic idea of compensating the convergence properties of an inferior numerical algorithm by a more efficient usage of the provided computing power. In this paper, we analyze the potential of combining both techniques. Therefore, we derive a mixed precision iterative refinement algorithm using a block-asynchronous iteration as an error correction solver, and compare its performance with a pure implementation of a block-asynchronous iteration and an iterative refinement method using double precision for the error correction solver. For matrices from the University of Florida Matrix collection, we report the convergence behaviour and provide the total solver runtime using different GPU architectures