high performance computing on graphics processing units: hgpu.org

In the last decade, there has been a dramatic growth in research and development of massively parallel commodity graphics hardware both in academia and industry. Graphics card architectures provide an optimal platform for parallel execution of many number crunching loop programs from fields like image processing or linear algebra. However, it is hard to efficiently […]

Data parallel programming provides an accessible model for exploiting the power of parallel computing elements without resorting to the explicit use of low level programming techniques based on locks, threads and monitors. The emergence of Graphics Processing Units (GPUs) with hundreds or thousands of processing cores has made data parallel computing available to a wider […]

General-purpose graphical processing units (GPGPUs) have transformed high-performance computing over the past decade. Making great computational power available with reduced cost and power consumption overheads, heterogeneous CPU-GPU-equipped systems have helped to make possible the emerging class of exascale data-intensive applications. Although the theoretical performance achieved by these hybrid systems is impressive, taking practical advantage of […]

As the computational power of GPUs continues to scale with Moore’s Law, an increasing number of applications are becoming limited by memory bandwidth. We propose an approach for programming GPUs with tightly-coupled specialized DMA warps for performing memory transfers between on-chip and off-chip memories. Separate DMA warps improve memory bandwidth utilization by better exploiting available […]

As the size of datasets continues to grow, machine learning applications are becoming increasingly limited by the amount of available computational power. Taking advantage of modern hardware requires using multiple parallel programming models targeted at different devices (e.g. CPUs and GPUs). However, programming these devices to run efficiently and correctly is difficult, error-prone, and results […]

Heterogeneous computers with processors and accelerators are becoming widespread in scientific computing. However, it is difficult to program hybrid architectures and there is no commonly accepted programming model. Ideally, applications should be written in a way that is portable to many platforms, but providing this portability for general programs is a hard problem. By restricting […]

This is the second volume of Morgan Kaufmann’s GPU Computing Gems, offering an all-new set of insights, ideas, and practical ";hands-on"; skills from researchers and developers worldwide. Each chapter gives you a window into the work being performed across a variety of application domains, and the opportunity to witness the impact of parallel GPU computing […]

An important class of compute accelerators are graphics processing units (GPUs). Popular programming models for non-graphics computation on GPUs, such as CUDA and OpenCL, provide an abstraction of many parallel scalar threads. Contemporary GPU hardware groups 32 to 64 scalar threads as a single warp or wavefront and executes this group of scalar threads in […]

Computing systems are becoming increasingly parallel and heterogeneous, and therefore new applications must be capable of exploiting parallelism in order to continue achieving high performance. However, targeting these emerging devices often requires using multiple disparate programming models and making decisions that can limit forward scalability. In previous work we proposed the use of domain-specific languages […]

This paper presents calculations of homogeneous isotropic turbulence at Re_{lambda} = 100 using both a pseudo-spectral method and a fast multipole vortex method on a 256^3 grid. For the vortex method, both algorithmic and hardware acceleration are applied using a highly parallel fast multipole method (FMM) on GPUs. The spectral methods uses the FFTW library […]

Using a popular Monte Carlo workload which implements European option pricing, we tested a variety of architectures including NVIDIA and AMD GPUs, ClearSpeed accelerator and multi-core processors and different programming approaches. We conclude that this particular workload seems most suitable for running on GPU type of architecture compared to other alternatives such as CPU or […]

Recent advances have enabled GPUs to be used as general-purpose parallel processors on commodity hardware for little cost. However, the ability to program these devices has not kept up with their performance. The programming model for GPUs has a number of restrictions that make it dif?cult to program. For example, software running on the GPU […]