high performance computing on graphics processing units: hgpu.org

We focus on the following computational chemistry problem: Given a subset of the exact distances between atoms, reconstruct the three-dimensional position of each atom in the given molecule. The distance matrix is generally sparse. This problem is both important and challenging. Our contribution is a novel combination of two known techniques (parallel breadth-first search and […]

OpenCL is an emerging platform for parallel computing that promises portability of applications across different architectures. This promise is seriously undermined, however, by the frequent use of floating-point arithmetic in scientific applications. Floating-point computations can yield vastly different results on different architectures – even IEEE 754-compliant ones -, potentially causing changes in control flow and […]

We present a new parallel code for computing the dynamical evolution of collisional N-body systems with up to N~10^7 particles. Our code is based on the the H’enon Monte Carlo method for solving the Fokker-Planck equation, and makes assumptions of spherical symmetry and dynamical equilibrium. The principal algorithmic developments involve optimizing data structures, and the […]

In recent years, GPU-CPU heterogeneous architectures have been increasingly adopted in high performance computing, because of their capabilities of providing high computational throughput. However, the energy consumption is a major concern due to the large scale of such kind of systems. There are a few existing efforts that try to lower the energy consumption of […]

This paper introduces a novel implementation of the genetic algorithm exploiting a multi-GPU cluster. The proposed implementation employs an island-based genetic algorithm where every GPU evolves a single island. The individuals are processed by CUDA warps, which enables the solution of large knapsack instances and eliminates undesirable thread divergence. The MPI interface is used to […]

Languages such as OpenCL and CUDA offer a standard interface for general-purpose programming of GPUs. However, with these languages, programmers must explicitly manage numerous low-level details involving communication and synchronization. This burden makes programming GPUs difficult and error-prone, rendering these powerful devices inaccessible to most programmers. We desire a higher-level programming model that makes GPUs […]

In this paper, we present a novel approach for using a GPU-based Cloud computing infrastructure to efficiently perform a structural comparison of protein binding sites. The original CPU-based Java version of a recent graph-based algorithm called SEGA has been rewritten in OpenCL to run on NVIDIA GPUs in parallel on a set of Amazon EC2 […]

We describe parallel implementations of an algorithm used to evaluate the likelihood function used in data analysis. The implementations run, respectively, on CPU and GPU, and both devices cooperatively (hybrid). CPU and GPU implementations are based on OpenMP and OpenCL, respectively. The hybrid implementation allows the application to run also on multi-GPU systems (not necessarily […]

Parallel computing has been in use for decades, and throughout many researchers have sought to define a model for algorithm design for such a platform. Valiant developed a model for parallel computing, which was later extended to later include multi-core processors, but it still may not be best suited for the unique GPU architecture. With […]

In this work we present further extensions and improvements of a Spiking Neural P system (for short, SNP systems) simulator on graphics processing units (for short, GPUs). Using previous results on representing SNP system computations using linear algebra, we analyze and implement a computation simulation algorithm on the GPU. A two-level parallelism is introduced for […]

In this paper, we propose an efficient implementation of the branch and bound method for knapsack problems on a CPU-GPU system via CUDA. Branch and bound computations can be carried out either on the CPU or on a GPU according to the size of the branch and bound list. A better management of GPUs memories, […]

We introduce a new spatial data structure for high dimensional data called the emph{approximate principal direction tree} (APD tree) that adapts to the intrinsic dimension of the data. Our algorithm ensures vector-quantization accuracy similar to that of computationally-expensive PCA trees with similar time-complexity to that of lower-accuracy RP trees. APD trees use a small number […]