high performance computing on graphics processing units: hgpu.org

Distributed applications combine the computational capabilities of heterogeneous nodes. As such, they offer challenges regarding data transfer and synchronization. HPX is a library for concurrent, parallel applications. It strives not only to address challenges regarding distributed systems, but also to conform to current and upcoming C++ standards. One of the solutions found in heterogeneous systems […]

General sparse matrix-matrix multiplication (SpGEMM) is an integral part of many scientific computing, high-performance computing (HPC), and graph analytic applications. This paper presents a new compressed sparse vector (CSV) format for representing sparse matrices and FSpGEMM, an OpenCL-based HPC framework for accelerating general sparse matrix-matrix multiplication on FPGAs. The proposed FSpGEMM framework includes an FPGA […]

We reported initial work towards a new fast and scalable scientific visualization technology that leverages the Vulkan API to achieve unprecedented performance through GPUs. This technology is implemented in a C/C++ library called Datoviz that offers an intermediate-level API for scientific visualization libraries and software. Datoviz provides a unified graphics stack for 2-D, 3-D, graphical […]

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework for the Python programming language. The most significant […]

This work explores the viability of end-to-end convolutional neural network inference using OpenCL HLS kernels generated from TVM on Intel FPGAs. We explore layer-pipelined execution for small networks and time-multiplexed kernels for larger CNNs. Naively generated kernels do not produce efficient hardware. We propose a set of optimizations to increase parallelism, resource utilization, and more […]

The emergence of novel hardware accelerators has powered the tremendous growth of machine learning in recent years. These accelerators deliver incomparable performance gains in processing high-volume matrix operators, particularly matrix multiplication, a core component of neural network training and inference. In this work, we explored opportunities of accelerating database systems using NVIDIA’s Tensor Core Units […]

We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topology. We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non-linearity in the mesh space. Our […]

Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudorandom number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and […]

Fluid dynamics simulations with the lattice Boltzmann method (LBM) are very memory-intensive. Alongside reduction in memory footprint, significant performance benefits can be achieved by using FP32 (single) precision compared to FP64 (double) precision, especially on GPUs. Here, we evaluate the possibility to use even FP16 and Posit16 (half) precision for storing fluid populations, while still […]

As embedded systems, such as smartphones with limited resources, have become increasingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that […]

Despite the potential of neural scene representations to effectively compress 3D scalar fields at high reconstruction quality, the computational complexity of the training and data reconstruction step using scene representation networks limits their use in practical applications. In this paper, we analyze whether scene representation networks can be modified to reduce these limitations and whether […]

Today deep learning is widely used for building software. A software engineering problem with deep learning is that finding an appropriate convolutional neural network (CNN) model for the task can be a challenge for developers. Recent work on AutoML, more precisely neural architecture search (NAS), embodied by tools like Auto-Keras aims to solve this problem […]