high performance computing on graphics processing units: hgpu.org

Detecting higher-order epistatic interactions in Genome-Wide Association Studies (GWAS) remains a challenging task in the fields of genetic epidemiology and computer science. A number of algorithms have recently been proposed for epistasis discovery. However, they suffer from a high computational cost since statistical measures have to be evaluated for each possible combination of markers. Hence, […]

Soft shadows, depth of field, and diffuse global illumination are common distribution effects, usually rendered by Monte Carlo ray tracing. Physically correct, noise-free images can require hundreds or thousands of ray samples per pixel, and take a long time to compute. Recent approaches have exploited sparse sampling and filtering; the filtering is either fast (axisaligned), […]

This technical report introduces DeepPy – a deep learning framework built on top of NumPy with GPU acceleration. DeepPy bridges the gap between highperformance neural networks and the ease of development from Python/NumPy. Users with a background in scientific computing in Python will quickly be able to understand and change the DeepPy codebase as it […]

The recent successes of deep learning are largely attributed to the advancement of hardware acceleration technologies, which can accommodate the incredible growth of data sizes and model complexity. The current solution involves using clusters of graphics processing units (GPU) to achieve performance beyond that of general purpose processors (GPP), but the use of field programmable […]

Using GPUs as general-purpose processors has revolutionized parallel computing by providing, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to their widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper proposes a programming approach, […]

In this work we describe and consider some features of implementing RAID erasure coding algorithms for Intel Xeon Phi coprocessor. We propose some algorithmic and technical improvements of encoding and decoding performance both in native and offload modes. Proposed approaches are designed to maximize the efficiency of Intel MIC architecture. We suggest new approach to […]

We show the flux tubes produced by static quark-antiquark, quark-quark and quark-gluon charges at finite temperature. The sources are placed in the lattice with fundamental and adjoint Polyakov loops. We compute the square densities of the chromomagnetic and chromoelectric fields above and below the phase transition. Our results are gauge invariant and produced in pure […]

Stencil computations are a class of algorithms operating on multi-dimensional arrays also called grid functions (GFs), which update array elements using their nearest-neighbors. This type of computation forms the basis for computer simulations across almost every field of science, such as computational fluid dynamics. Its mostly regular data access patterns potentially enable it to take […]

Current trends in multicore platform design indicate that heterogeneous systems are here to stay. Such systems include processors with specialized accelerators supporting different instruction sets and different types of memory spaces among several other features. Unfortunately, these features increase the effort for programming and porting applications to different target platforms. To solve this problem, effective […]

Dedicated graphics processing chips have become a standard component in most modern systems, making their powerful parallel computing capabilities more accessible to developers. Amongst the tools created to aid programmers in the task of parallelizing applications, directive-based standards are some of the most widely used. These standards, such as OpenACC and OpenMP, facilitate the conversion […]

Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools coming to public. Training a deep network is usually a very time-consuming process. To address the huge computational challenge in deep learning, many tools exploit hardware features such […]

Efficient ordinary differential equation solvers for chemical kinetics must take into account the available thread and instruction-level parallelism of the underlying hardware, especially on many-core coprocessors, as well as the numerical efficiency. A stiff Rosenbrock and nonstiff Runge-Kutta solver are implemented using the single instruction, multiple thread (SIMT) and single instruction, multiple data (SIMD) paradigms […]