high performance computing on graphics processing units: hgpu.org

GPU acceleration and other computer performance increases will offer critical benefits to biomedical science. Computer simulation has become an integral part of the study of the structure and function of biological molecules. For years, parallel computers have been used to conduct these computationally demanding simulations and to analyze their results. These simulations function as a […]

This paper describes our experience rewriting a sequential particle-in-cell code so that its key computations are executed on a GPU. This code is well-suited to GPU acceleration, as it performs data-parallel operations on a regular grid. Key performance challenges are the need for global synchronization in mapping particles to grid nodes, and managing memory bandwidth […]

Energy minimization is an important step in molecular modeling, with applications in molecular docking and in mapping binding sites. Minimization involves repeated evaluation of various bonded and non-bonded energies of a protein complex. It is a computationally expensive process, with runtimes typically being many hours on a desktop system. In the current article, we present […]

As hardware accelerators and especially GPUs become more and more popular to accelerate the compute intensive parts of an algorithm, standard high performance computing packages are starting to benefit from this trend. We present the first GPU acceleration of the ANSYS direct sparse solver. We explain how such a multifrontal solver may be accelerated using […]

While FPGAs have only one fifth the raw floating point capability of GPUs, other attributes allow them to be surprisingly competitive with respect to a number of critical floating point intensive applications. In the first part we review these FPGA attributes. The bulk of this extended abstract then provides an overview of efficient FPGA implementations […]

We introduce a new GPGPU-based real-time dense stereo matching algorithm. The algorithm is based on a progressive multi-resolution pipeline which includes background modeling and dense matching with adaptive windows. For applications in which only moving objects are of interest, this approach effectively reduces the overall computation cost quite significantly, and preserves the high definition details. […]

In this paper we evaluate the possibility of using mixed precision algorithms on different hardware platforms to obtain energy-efficient solvers for linear systems of equations. Our test-cases arise in the context of computational fluid dynamics. Therefore, we analyze the energy efficiency of common cluster nodes and a hybrid, GPU-accelerated cluster node, when applying a linear […]

The Finite-Difference Time-Domain (FDTD) method is commonly used for electromagnetic field simulations. Recently, successful hardware-accelerations using Graphics Processing Unit (GPU) have been reported for the large-scale FDTD simulations. In this paper, we present a performance analysis of the three-dimensional (3D) FDTD on GPU using the roofline model. We find that theoretical predictions on maximum performance […]

As general purpose computing on Graphics Processing Units (GPGPU) matures, more complicated scientific applications are being targeted to utilize the data-level parallelism available on a GPU. Implementing physically-based simulation on data-parallel hardware requires preprocessing overhead which affects application performance. We discuss our implementation of physics-based data structures that provide significant performance improvements when used on […]

A new approach to solve the power flow problem based on graphic processing units is presented in this paper. A Newton method is implemented to solve the set of nonlinear equations of the power flow formulation. A parallel kernel for the biconjugate gradient method allows solving the voltage corrections on a graphic processing card. While […]

We consider the use of commodity graphics processing units (GPUs) for the common task of numerically integrating ordinary differential equations (ODEs), achieving speed-ups of up to 115-fold over comparable serial CPU implementations, and 15-fold over multithreaded CPU code with SIMD intrinsics. Using Lorenz ’96 models as a case study, single and double precision benchmarks are […]

Commodity off-the-shelf GPUs lack error checking mechanisms for graphics memory, whereas conventional HPC platforms have used hardware-based ECC for DRAMs. To alleviate this reliability concern, we propose a software-based ECC for GPGPU applications. We add small program codes to normal CUDA programs that compute ECCs for data residing in graphics memory so that transient bit-flips […]