high performance computing on graphics processing units: hgpu.org

The proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph’s minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper […]

We consider flexible decoder implementation of low density parity check (LDPC) codes via compute-unified-devicearchitecture (CUDA) programming on graphics processing unit (GPU), a research subject of considerable recent interest. An important issue in LDPC decoder design based on CUDA-GPU is realizing coalesced memory access, a technique that reduces memory transaction time considerably. In previous works along […]

For many finite element problems, when represented as sparse matrices, iterative solvers are found to be unreliable because they can impose computational bottlenecks. Early pioneering work by Duff et al, explored an alternative strategy called multifrontal sparse matrix factorization. This approach, by representing the sparse problem as a tree of dense systems, maps well to […]

Combining CPUs and GPUs on the same chip has become a popular architectural trend. However, these heterogeneous architectures put more pressure on shared resource management. In particular, managing the lastlevel cache (LLC) is very critical to performance. Lately, many researchers have proposed several shared cache management mechanisms, including dynamic cache partitioning and promotion-based cache management, […]

Recently, interest in the Graphics Processing Unit (GPU) for general purpose parallel applications development and research has grown. Much of the current research on the GPU focuses on the acceleration of regular problems, as irregular problems typically do not provide the same level of performance on the hardware. We explore the potential of the GPU […]

There are a number of different phenomena in the early universe that have to be studied numerically with lattice simulations. This paper presents a graphics processing unit (GPU) accelerated Python program called PyCOOL that solves the evolution of scalar fields in a lattice with very precise symplectic integrators. The program has been written with the […]

This paper will report our evaluation to use openCL as a platform for hard realtime scheduling. Specifically, we have evaluated which types of tasks are faster on GPGPU than on CPU. We have investigated computational tasks, memory intensive tasks (especially tasks using low latency GDDR memory) and disk intensive tasks. This study is the first […]

Sorting and merging are two important kernels which are used as subroutines in numerous algorithms, whose performance depends on the efficiency of these primitives. Databases use sort and merge primitives extensively. Computational biology, search engines, realtime rendering and geographical information systems are other fields where sorting and merging large amounts of data is indispensable. Even […]

The main aim of the Computational Fluid Dynamics (CFD) simulations is to reconstruct the reality of fluid motion and behaviour as accurately as possible in order to better understand the natural phenomena under specified conditions. Ideally, general solutions can also cover different scales and geometric configurations. Unfortunately, due to expensive algorithms, classic CFD codes most […]

We present a CUDA implementation of dense multivariate polynomial arithmetic based on Fast Fourier Transforms over finite fields. Our core routine computes on the device (GPU) the subresultant chain of two polynomials with respect to a given variable. This subresultant chain is encoded by values on a FFT grid and is manipulated from the host […]

Analyzing and clustering large scale data set is a complex problem. One explored method of solving this problem borrows from nature, imitating the flocking behavior of birds. One limitation of this method of data clustering is its complexity O(n^2). As the number of data and feature dimensions grows, it becomes increasingly difficult to generate results […]

In this paper we describe GPA priori, a GPU-accelerated implementation of Frequent Item set Mining (FIM). We tested our implementation with an Nvidia Tesla T10 graphic processor and demonstrate up to 100x speedup as compared with several state-of-the-art FIM algorithms on a CPU. In order to map the Apriori algorithm onto the SIMD execution model, […]