high performance computing on graphics processing units: hgpu.org

Current GPUs are massively parallel multicore processors optimised for workloads with a large degree of SIMD parallelism. Good performance requires highly idiomatic programs, whose development is work intensive and requires expert knowledge. To raise the level of abstraction, we propose a domain-specific high-level language of array computations that captures appropriate idioms in the form of […]

In this paper, we present a multi-level programming model for recent GPU-based high performance computing systems. Involving cooperative stream threads and symmetric multiprocessing threads our model gives a computational framework that scales through multi-GPU environments to GPU-cluster systems. Instead of hiding the execution environment from the programmer using compiler extensions or metaprogramming techniques we aim […]

A new compilation framework enables the execution of numerical-intensive applications, written in Python, on a hybrid execution environment formed by a CPU and a GPU. This compiler automatically computes the set of memory locations that need to be transferred to the GPU, and produces the correct mapping between the CPU and the GPU address spaces. […]

Recent trends in computer-graphics APIs and hardware have made it practical to use high-level functional languages for real-time graphics applications. Thus we have the opportunity to develop new approaches to computer graphics that take advantage of the high-level features of functional languages. This paper describes one such project that uses the techniques of functional programming […]

For systems that use hardware accelerators to combine multicore and multiprocess technology with libraries and computational kernels, the drawbacks are the complexity of the programming model and the corresponding verification of the software and validation of the system performance capabilities. In this paper, we describe a software approach to utilizing the compute power of the […]

This paper presents a revamping of a sparse linear algebra design pattern, targeting parallelization within scientific and engineering applications. A proof of concept implementation is developed to compare actual software practices and optimizations with those described in the original design pattern. The case study reveals that the design pattern did not tightly coincide with the […]

MOTIVATION: Hundreds of genome-wide association studies have been performed over the last decade, but as single nucleotide polymorphism (SNP) chip density has increased so has the computational burden to search for epistasis [for n SNPs the computational time resource is O(n(n-1)/2)]. While the theoretical contribution of epistasis toward phenotypes of medical and economic importance is […]

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 […]

Software Distributed Shared Memory (SDSM) systems offer a convenient way to run applications developed for shared memory systems on distributed systems with no changes to them. However, since SDSM systems add an extra layer of abstraction to the memory hierarchy, applications may suffer performance problems when running on top of them. Our main research interest […]

A frequently used method of clustering is a technique called k-means clustering. The k-means algorithm consists of two steps: A map step, which is simple to execute on a GPU, and a reduce step, which is more problematic. Previous researchers have used a hybrid approach in which the map step is computed on the GPU […]

Gaining knowledge out of vast datasets is a main challenge in data-driven applications nowadays. Sparse grids provide a numerical method for both classification and regression in data mining which scales only linearly in the number of data points and is thus well-suited for huge amounts of data. Due to the recursive nature of sparse grid […]

Graphics processing units (GPUs) provide a low cost platform for accelerating high performance computations. The introduction of new programming languages, such as CUDA and OpenCL, makes GPU programming attractive to a wide variety of programmers. However, programming GPUs is still a cumbersome task for two primary reasons: tedious performance optimizations and lack of portability. First, […]