high performance computing on graphics processing units: hgpu.org

In spatial agent-based models (SABMs) each entity of the system being modeled is uniquely represented as an independent agent. Large scale emergent behavior in SABMs is population sensitive. Thus, the number of agents should reflect the system being modeled, which can be in the order of billions. Models can be decomposed such that each component […]

Nowadays, the ideas of continuous integration and continuous delivery are under heavy usage in order to achieve rapid software development speed and quick product delivery to the customers with good quality. During the process ofmodern software development, the testing stage has always been with great significance so that the delivered software is meeting all the […]

While OpenMP is the de facto standard of shared memory parallel programming models, a number of alternative programming models and runtime systems have arisen in recent years. Fairly evaluating these programming systems can be challenging and can require significant manual effort on the part of researchers. However, it is important to facilitate these comparisons as […]

In this paper we deal with the extension of the Fast Kinetic Scheme (FKS) [J. Comput. Phys., Vol. 255, 2013, pp 680-698] originally constructed for solving the BGK equation, to the more challenging case of the Boltzmann equation. The scheme combines a robust and fast method for treating the transport part based on an innovative […]

In this paper, we consider task-based dense linear algebra applications on a single heterogeneous node which contains regular CPU cores and a set of GPU devices. Efficient scheduling strategies are crucial in this context in order to achieve good and portable performance. HeteroPrio, a resource-centric dynamic scheduling strategy has been introduced in a previous work […]

We propose a new architecture for optimization modeling frameworks in which solvers are expressed as computation graphs in a framework like TensorFlow rather than as standalone programs built on a low-level linear algebra interface. Our new architecture makes it easy for modeling frameworks to support high performance computational platforms like GPUs and distributed clusters, as […]

This paper investigates the feasibility of a backend design for real-time, multiple-channel processing digital phased array system, particularly for high-performance embedded computing platforms constructed of general purpose digital signal processors. First, we obtained the lab-scale backend performance benchmark from simulating beamforming, pulse compression, and Doppler filtering based on a Micro Telecom Computing Architecture (MTCA) chassis […]

In this work, we propose the implementation of a 3D object recognition system using Convolutional Neural Networks. For that purpose, we first analyzed the theoretical foundations of that kind of neural networks. Next, we discussed ways of representing 3D data in a compact and structured manner to feed the neural network. Those representations consist of […]

Modern day analytics deals with big datasets from diverse fields. For many application the data is in the form of an array which consists of large number of smaller arrays. Existing techniques focus on sorting a single large array and cannot be used for sorting large number of smaller arrays in an efficient manner. Currently […]

Accelerator-based systems are making rapid inroads into becoming platforms of choice for both high end cloud services and processing irregular applications like real-world graph analytics due to their high scalability and low dollar to FLOPS ratios. Yet GPUs are not first class schedulable entities causing substantial hardware resource underutilization, including their computational and data movement […]

Exascale computing will feature novel and potentially disruptive hardware architectures. Exploiting these to their full potential is non-trivial. Numerical modelling frameworks involving finite difference methods are currently limited by the ‘static’ nature of the hand-coded discretisation schemes and repeatedly may have to be re-written to run efficiently on new hardware. In contrast, OpenSBLI uses code […]

With the recent advancement of multilayer convolutional neural networks (CNN), deep learning has achieved amazing success in many areas, especially in visual content understanding and classification. To improve the performance and energy-efficiency of the computation-demanding CNN, the FPGA-based acceleration emerges as one of the most attractive alternatives. In this paper we design and implement Caffeine, […]