high performance computing on graphics processing units: hgpu.org

The proliferation of a diverse set of heterogeneous computing platforms in conjunction with the plethora of programming languages and optimization techniques on each language for each underlying architecture exacerbate widespread adoption of such platforms. This is especially true for novice programmers and the non-technical-savvy masses that are largely precluded from enjoying the advantages of high-performance […]

We study strategy improvement algorithms for solving parity games. While these algorithms are known to solve parity games using a very small number of iterations, experimental studies have found that a high step complexity causes them to perform poorly in practice. In this paper we seek to address this situation. Every iteration of the algorithm […]

This paper describes fast sorting techniques using the recent AVX-512 instruction set. Our implementations benefit from the latest possibilities offered by AVX-512 to vectorize a two-parts hybrid algorithm: we sort the small arrays using a branch- free Bitonic variant, and we provide a vectorized partitioning kernel which is the main component of the well-known Quicksort. […]

In recent years, deep convolutional neural networks (ConvNet) have shown their popularity in various real world applications. To provide more accurate results, the state-of-the-art ConvNet requires millions of parameters and billions of operations to process a single image, which represents a computational challenge for general purpose processors. As a result, hardware accelerators such as Graphic […]

We present a new method for numerical hydrodynamics which uses a multidimensional generalisation of the Roe solver and operates on an unstructured triangular mesh. The main advantage over traditional methods based on Riemann solvers, which commonly use one-dimensional flux estimates as building blocks for a multidimensional integration, is its inherently multidimensional nature, and as a […]

Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory. Prior work tries to address this restriction by virtualizing the memory usage of DNNs, enabling both CPU and GPU memory to be utilized for memory allocations. Despite […]

We introduce the CUDA Tensor Transpose (cuTT) library that implements high-performance tensor transposes for NVIDIA GPUs with Kepler and above architectures. cuTT achieves high performance by (a) utilizing two GPU-optimized transpose algorithms that both use a shared memory buffer in order to reduce global memory access scatter, and by (b) computing memory positions of tensor […]

In the paper we discuss the main features of the software package for numerical simulations of the surface water dynamics. We consider an approximation of the shallow water equations together with the parallel technologies for NVIDIA CUDA graphics processors. The numerical hydrodynamic code is based on the combined Lagrangian-Euler method~(CSPH-TVD). We focused on the features […]

We present GPUQT, a quantum transport code fully implemented on graphics processing units. Using this code, one can obtain intrinsic electronic transport properties of large systems described by a real-space tight-binding Hamiltonian together with one or more types of disorder. The DC Kubo conductivity is represented as a time integral of the velocity auto-correlation or […]

The AFiD code, an open source solver for the incompressible Navier-Stokes equations ({color{blue}burl{this http URL}}), has been ported to GPU clusters to tackle large-scale wall-bounded turbulent flow simulations. The GPU porting has been carried out in CUDA Fortran with the extensive use of kernel loop directives (CUF kernels) in order to have a source code […]

This work proposes TinyDL, an automated end-to-end framework that aims to integrate the state-of-the-art Deep Learning (DL) models into embedded systems. TinyDL enables efficient training and execution of DL models as data is collected over time while adhering to the underlying physical resources and constraints. The constraints can be characterized in terms of memory bandwidth, […]

This paper presents an application of GPU accelerators in Earth system modelling. We focus on atmospheric chemical kinetics, one of the most computationally intensive tasks in climate-chemistry model simulations. We developed a software package that automatically generates CUDA kernels to numerically integrate atmospheric chemical kinetics in the global climate model ECHAM/MESSy Atmospheric Chemistry (EMAC), used […]