high performance computing on graphics processing units: hgpu.org

In recent years, Graphics Processing Units (GPUs) have emerged as a powerful accelerator for general-purpose computations. Current approaches to program GPUs are still relatively low-level programming models such as Compute Unified Device Architecture (CUDA), a programming model from NVIDIA, and Open Compute Language (OpenCL), created by Apple in cooperation with others. These two programming models […]

GPUs constitute nowadays a solid alternative for high performance computing, and the advent of CUDA/OpenCL allow programmers a friendly model to accelerate a broad range of applications. The way GPUs exploit parallelism differ from multi-core CPUs, which raises new challenges to take advantage of its tremendous computing power. In this respect, P systems or Membrane […]

The performance of data-parallel processing can be highly sensitive to any contention in memory. In contrast to multi-core CPUs which employ a number of memory latency minimization techniques such as multi-level caching and prefetching, Graphics Processing Units (GPUs) require that the data-parallel computations reference memory in a deterministic pattern in order to reap the benefits […]

Recent technological and economic developments have led to widespread availability of multi-core CPUs and specialist accelerator processors such as graphical processing units (GPUs). The accelerated computational performance possible from these devices can be very high for some applications paradigms. Software languages and systems such as NVIDIA’s CUDA and Khronos consortium’s open compute language (OpenCL) support […]

The paper compares three APIs for the implementation of cloth modelling on modern graphics processor units (GPU): OpenGL plus GLSL, NVIDIA CUDA and OpenCL. They are compared by programming features, platform and device portability, and performance for the purpose of dynamic cloth simulation. Results about performance are given and conclusions are drawn about use cases.

Computational Fluid Dynamics, an important branch in HPC field, has a history of seeking and requiring higher computational performance. The traditional way to satisfy this quest is to use faster machines or supercomputers. Yet these approaches seem inconvenient and costly to many individual researchers. We investigated the use of GPU to accelerate CFD codes and […]

Advances in GPU technology have propelled the GPU into arenas far afield from the traditional, isolated roles they have previously played. With hundreds of processing units in a single GPU, substantial speedups can be achieved by harnessing their power to augment the performance of the traditional single- or multi-core CPU on certain compute-intensive applications. However, […]

This work presents the Open Reconfigurable Computing Language (OpenRCL) system designed to enable low-power high-performance reconfigurable computing with imperative programming language such as C/C++. The key idea is to expose the FPGA platform as a compiler target for applications expressed in the OpenCL paradigm. To this end, we present a combination of low-level virtual machine […]

The use of accelerators such as graphics processing units (GPUs) has become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high-performance computers, machines with more than one type of floating-point processor, are now becoming more prevalent due to these advantages. In […]

Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization in cancer diagnosis. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper we present a pipeline that enables interactive visual exploration and semi-automatic segmentation and classification of CEUS data. For […]

Multi-core processors and a variety of accelerators have allowed scientific applications to scale to larger problem sizes. We present a performance, design methodology, platform, and architectural comparison of several application accelerators executing a Quantum Monte Carlo application. We compare the application’s performance and programmability on a variety of platforms including CUDA with Nvidia GPUs, Brook+ […]

Modern GPUs and CPUs are massively parallel, many-core processors. While application developers for these many-core chips are reporting 10X-100X speedup over sequential code on traditional microprocessors, the current practice of many-core programming based on OpenCL, CUDA, and OpenMP puts strain on software development, testing and support teams. According to the semiconductor industry roadmap, these processors […]