high performance computing on graphics processing units: hgpu.org

The release of the CUDA architecture made massively parallel computing possible on ordinary desktops and opened a door to enormous computing power of graphics adapters. The trajectory planning for aerial vehicles is one of the tasks that can benefit from it. The sought path must respect all physical limitations of the airplane and avoid all […]

The irruption in the HPC scene of hardware accelerators, like GPUs, has made available unprecedented performance to developers. However, even expert developers may not be ready to exploit the new complex processor hierarchies. We need to find a way to leverage the programming effort in these devices at programming language level, otherwise, developers will spend […]

In this paper there are considered several approaches for the increasing performance of software implementation of integer multiplication algorithm for the 32-bit & 64-bit platforms via parallelization. The main idea of algorithm parallelization consists in delayed carry mechanism using which authors have proposed earlier [11]. The delayed carry allows to get rid of connectivity in […]

The geometric multigrid method (GMG) is one of the most efficient solving techniques for discrete algebraic systems arising from many types of partial differential equations. GMG utilizes a hierarchy of grids or discretizations and reduces the error at a number of frequencies simultaneously. Graphics processing units (GPUs) have recently burst onto the scientific computing scene […]

GPGPUs (General Purpose Graphic Processing Units) provide massive computational power. However, applying GPGPU technology to real-time computing is challenging due to the non-preemptive nature of GPGPUs. Especially, a job running in a GPGPU or a data copy between a GPGPU and CPU is non-preemptive. As a result, a high priority job arriving in the middle […]

Since its introduction the watershed transform became a popular method for volume data segmentation. A range of various algorithms for its computation were developed, including parallel algorithms for computation on different architectures. Recently also algorithms for consumer graphical accelerators were developed. Neither of these, however, are able to process data larger than the available memory […]

Given the extraordinary computational power of modern graphics processing units (GPUs), general purpose computation on GPUs (GPGPU) has become an increasingly important platform for high performance computing. To better understand how well the GPU resource has been utilized by application developers and then to facilitate them to develop high performance GPGPU code, we conduct an […]

On-chip shared memory (a.k.a. local data share) is a critical resource to many GPGPU applications. In current GPUs, the shared memory is allocated when a thread block (also called a workgroup) is dispatched to a streaming multiprocessor (SM) and is released when the thread block is completed. As a result, the limited capacity of shared […]

Preprocessing of data is a vital aspect in information retrieval. Stemming is a major preprocessing task. The goal of stemming is to reduce the inflectional and some of the derivational forms of a word to its base form. Dealing with the massive amounts of data on the web, preprocessing generally consumes a major portion of […]

CONTEXT: Cosmological measurements require the calculation of nontrivial quantities over large datasets. The next generation of survey telescopes (such as DES, PanSTARRS, and LSST) will yield measurements of billions of galaxies. The scale of these datasets, and the nature of the calculations involved, make cosmological calculations ideal models for implementation on graphics processing units (GPUs). […]

BACKGROUND: Protein-DNA docking is a very challenging problem in structural bioinformatics and has important implications in a number of applications, such as structure-based prediction of transcription factor binding sites and rational drug design. Protein-DNA docking is very computational demanding due to the high cost of energy calculation and the statistical nature of conformational sampling algorithms. […]

The detection of (moving or static) targets in remotely sensed hyperspectral images often requires real-time responses for swift decisions that depend upon high computing performance of algorithm analysis. The automatic target detection and classification algorithm (ATDCA) has been widely used for this purpose. In this letter, we develop several optimizations for accelerating the computational performance […]