high performance computing on graphics processing units: hgpu.org

Popular eigen-solvers such as block-Lanczos require repeated inversion of an eigen-matrix. This is a bottleneck in large-scale modal problems with millions of degrees of freedom. On the other hand, the classic Rayleigh-Ritz conjugate gradient method only requires a matrix-vector multiplication, and is therefore potentially scalable to such problems. However, as is well-known, the Rayleigh-Ritz has […]

As the capability and component count of systems increase, the MTBF decreases. Typically, applications tolerate failures with checkpoint/restart to a parallel file system (PFS). While simple, this approach can suffer from contention for PFS resources. Multi-level checkpointing is a promising solution. However, while multi-level checkpointing is successful on todays machines, it is not expected to […]

Mapping quantitative trait loci (QTL) using genetic marker information is a time consuming analysis that has interested the mapping community for the past decades. The increasing amount of genetic marker data allows one to consider ever more precise QTL analyses, while increasing the demand for computation. Part of the difficulty of detecting QTLs resides in […]

Adding location-awareness to publish-subscribe middleware infrastructures would open-up new opportunities to use this technology in the hot area of mobile applications. On the other hand, this requires to radically change the way published events are matched against received subscriptions. In this paper we examine this issue in detail and we present CLCB, a new algorithm […]

Color quantization is an often performed prestep in many image processing and computer vision applications. Quantization is defined as the process of selecting a palette of representative colors P which can replace the original colors C in an image such that |P| << |C| and the perceptual distortion of the reduced color image is minimized. […]

Computing the interactions between the stars within dense stellar clusters is a problem of fundamental importance in theoretical astrophysics. However, simulating realistic sized clusters of about 106 stars is computationally intensive and often takes a long time to complete. This paper presents the acceleration of a Monte Carlo algorithm for simulating stellar cluster evolution using […]

With an increasing amount of data and demand for fast query processing, the efficiency of database operations continues to be a challenging task. A common approach is to leverage parallel hardware platforms. With the introduction of general-purpose GPU (Graphics Processing Unit) computing, massively parallel hardware has become available within commodity hardware. XML is based on […]

The goal of face detection is to determine the presence of faces in arbitrary images, along with their locations and dimensions. As it happens with any graphics workloads, these algorithms benefit from data-level parallelism. Existing parallelization efforts strictly focus on mapping different divide and conquer strategies into multicore CPUs and GPUs. However, even the most […]

We tried to accelerate the computational speed of TOUGH2 simulation by introducing a linear computation routine using a Graphics Processing Unit (GPU). Libraries for GPU computation were introduced, and new solvers for linear equations were developed. Out of those, CLLUSTB, an ILU preconditioned BiCGSTAB solver made with the CULA Sparse, demonstrated good performance both in […]

Graphics processing units (GPUs) have emerged as a strong candidate for high-performance computing. While regular data-parallel computations with little or no synchronization are easy to map on the GPU architectures, it is a challenge to scale up computations on dynamically changing pointer-linked data structures. The traditional lock-based implementations are known to offer poor scalability due […]

The use of accelerators in high-performance computing is increasing. The most commonly used accelerator is the graphics processing unit (GPU) because of its low cost and massively parallel performance. The two most common programming environments for GPU accelerators are CUDA and OpenCL. While CUDA runs natively only on NVIDIA GPUs, OpenCL is an open standard […]

Mapping algorithms in an efficient way to the target hardware poses a challenge for algorithm designers. This is particular true for heterogeneous systems hosting accelerators like graphics cards. While algorithm developers have profound knowledge of the application domain, they often lack detailed insight into the underlying hardware of accelerators in order to exploit the provided […]