high performance computing on graphics processing units: hgpu.org

Clusters of seemingly homogeneous compute nodes are increasingly heterogeneous within each node due to replication and distribution of node-level subsystems. This intra-node heterogeneity can adversely affect program execution performance by inflicting additional data-access costs when accessing non-local data. In this work-in-progress paper, we present extensions to the Cbench Scalable Testing Framework for analyzing main memory […]

In the past, evaluating the architectural innovation of parallel computing devices relied on a benchmark suite based on existing programs, e.g., EEMBC or SPEC. However, with the growing ubiquity of parallel computing devices, we argue that it is unclear how best to express parallel computation, and hence, a need exists to identify a higher level […]

The design of graphics processing unit (GPU) audio signals processing extensions to Pure Data (Pd) is discussed with attention to future growth in GPU computing and the complexity of programming a general solution. An implementation named PdCUDA is presented for use of GPU general programming capability for audio signals processing with Pd and the CUDA […]

We develop a highly optimized code for simulating the Edwards-Anderson Heisenberg model on graphics processing units (GPUs). Using a number of computational tricks such as tiling, data compression and appropriate memory layouts, the simulation code combining over-relaxation, heat bath and parallel tempering moves achieves a peak performance of 0.29 ns per spin update on realistic […]

High-performance streams of (pseudo) random numbers are crucial for the efficient implementation for countless stochastic algorithms, most importantly, Monte Carlo simulations and molecular dynamics simulations with stochastic thermostats. A number of implementations of random number generators has been discussed for GPU platforms before and some generators are even included in the CUDA supporting libraries. Nevertheless, […]

In this work, we have explored the advantages and drawbacks of using GPUs instead of CPUs in the calculation of a standard 2-point correlation function algorithm, which is useful for the analysis of Large Scale Structure of galaxies. Taking into account the huge volume of data foreseen in upcoming surveys, our main goal has been […]

Stochastic Differential Equations are important in many models of various physical or artificial phenomena. To get meaningful results it is desirable to solve the initial value numerical integration problem for a sufficiently large ensemble of realizations. Each element of the ensemble has the same form, thus exposing inherent data-parallelism. We implemented a cross-platform library written […]

We recast the Cosegmentation problem using Random Walker (RW) segmentation as the core segmentation algorithm, rather than the traditional MRF approach adopted in the literature so far. Our formulation is similar to previous approaches in the sense that it also permits Cosegmentation constraints (which impose consistency between the extracted objects from >= 2 images) using […]

Stencil computations arise in many scientific computing domains, and often represent time-critical portions of applications. There is significant interest in offloading these computations to high-performance devices such as GPU accelerators, but these architectures offer challenges for developers and compilers alike. Stencil computations in particular require careful attention to off-chip memory access and the balancing of […]

Fast image reconstruction is a critical requirement for an imaging modality to be adopted in the field of clinical and pre-clinical sciences. While programs become faster due to more powerful hardware, at the same time data size increases and the need for advanced—and often computational more demanding—reconstruction algorithms arises. A cheap way to achieve a […]

Initially introduced as special-purpose accelerators for games and graphics code, graphics processing units (GPUs) have emerged as widely-used high-performance parallel computing platforms. GPUs traditionally provided only softwaremanaged local memories (or scratchpads) instead of demandfetched caches. Increasingly, however, GPUs are being used in broader application domains where memory access patterns are both harder to analyze and […]

Now-a-days various computing paradigms are present in IT industry. Cloud, Grid, Cluster and General Purpose-Graphical Processing Unit (GP-GPU) computing are High Performance Computing (HPC) technologies and are growing very quickly. These are undoubtedly today’s most enticing technology areas due to the various benefits offered by them such as virtualization, high performance and less managerial overhead […]