high performance computing on graphics processing units: hgpu.org

The general-purpose computation on graphic processing units (GPGPU) becomes increasingly popular due to their high computational throughput for data parallel applications. Modern GPU architectures have limited capability for error detection and tolerance since they are originally designed for graphics processing. However, the rigorous execution correctness is required for general-purpose applications. This makes reliability a growing […]

Power and thermal constraints gained critical importance in the design of microprocessors over the past decade. Chipmakers failed to keep power at bay while sustaining the performance growth of serial computers at the rate expected by consumers. As an alternative, they turned to fitting an increasing number of simpler cores on a single die. While […]

The Finite-Difference Time-Domain (FDTD) solution of Maxwell’s equations, a grid-based differential time-domain numerical modeling method, is an approach for the direct modelling of the penetration of structures by continuous plane waves. Although FDTD techniques are considered to be relatively easy to understand and to implement in software, such modelling methods require a high level of […]

In this dissertation we provide new numerical algorithms for use in conjunction with simulation based design codes. These algorithms are designed and best suited to run on emerging heterogenous computing architectures which contain a combination of traditional multi-core processors and new programmable many-core graphics processing units (GPUs). We have developed the following numerical algorithms (i) […]

Graphics processing units (GPUs) have become increasingly popular over the last years as a cost-effective means of accelerating various computationally intensive tasks. We study the particular case of modular exponentiation, the crucial operation behind most modern public-key cryptography algorithms. We focus our attention on the NVIDIA GT200 architecture, currently one of the most popular for […]

Nowadays it is difficult to imagine an area of knowledge that can continue developing without the use of computers and informatics. It is not different with biology, that has seen an unpredictable growth in recent decades, with the rise of a new discipline, bioinformatics, bringing together molecular biology, biotechnology and information technology. More recently, the […]

In the context of massively parallel processors such as Graphics Processing Units (GPUs), an emerging non-volatile memory – STT-RAM – provides substantial power, area savings, and increased capacity compared to the conventionally used SRAM. The use of highly dense, low static power STT-RAM in processors that run just few threads of execution does not seem […]

Dataflow is a well known computational model and is widely used for expressing the functionality of digital signal processing (DSP) applications, such as audio and video data stream processing, digital communications, and image processing. These applications usually require real-time processing capabilities and have critical performance constraints. Dataflow provides a formal mechanism for describing specifications of […]

This dissertation presents research into the development of high performance dataflow middleware and applications on heterogeneous, distributed-memory supercomputers. We present coarse-grained state-of-the-art ad-hoc techniques for optimizing the performance of real-world, data-intensive applications in biomedical image analysis and radar signal analysis on clusters of computational nodes equipped with multi-core microprocessors and accelerator processors, such as the […]

The research conducted in this thesis provides a robust implementation of a preconditioned iterative linear solver on programmable graphic processing units (GPUs). Solving a large, sparse linear system is the most computationally demanding part of many widely used power system analysis. This thesis presents a detailed study of iterative linear solvers with a focus on […]

Compilation on modern architectures has become an increasingly difficult challenge with the evolution of computers and computing needs. In particular, programmers expect the compiler to produce optimized code for a variety of hardware, making the most of their theoretical performance. For years this was not a problem because hardware vendors consistently delivered increases in clock […]

As the computer industry retools to leverage massively parallel graphics processing units (GPUs), this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA Application Design and Development starts with an introduction to parallel computing concepts for readers with […]