Abstract:

We fundamentally reconsider implementation of the Fast Multipole Method (FMM) on a computing node with a heterogeneous CPU-GPU architecture with multicore CPU(s) and one or more GPU accelerators, as well as on an interconnected cluster of such nodes. The FMM is a divide-and-conquer algorithm that performs a fast N-body sum using a spatial decomposition and is often used in a time-stepping or iterative loop. Using the observation that the local summation and the analysis-based translation parts of the FMM are independent, we map these respectively to the GPUs and CPUs. Careful analysis of the FMM is performed to distribute work optimally between the multicore CPUs and the GPU accelerators. We first develop a single node version where the CPU part is parallelized using OpenMP and the GPU version via CUDA. New parallel algorithms for creating FMM data structures are presented together with load balancing strategies for the single node and distributed multiple-node versions. Our 8 GPU performance
is comparable with performance of a 256 GPU version of the FMM that won the 2009 Bell prize.

(Qi Hu, Nail A. Gumerov and Ramani Duraswami: “Scalable fast multipole methods on distributed heterogeneous architectures”, accepted for SC’11. [PDF])

Abstract

It is increasingly easy to develop software that exploits Graphics Processing Units (GPUs). The molecular dynamics simulation community has embraced this recent opportunity. Herein, we outline the current approaches that exploit this technology. In the context of biomolecular simulations, we discuss some of the algorithms that have been implemented and some of the aspects that distinguish the GPU from previous parallel environments. The ubiquity of GPUs and the ingenuity of the simulation community augur well for the scale and scope of future computational studies of biomolecules.

(Baker, J. A. and Hirst, J. D.: “Molecular Dynamics Simulations Using Graphics Processing Units”. Molecular Informatics, 30:498–504. [DOI])

Implementing flexible software solutions, such as rendering and ray tracing, is still challenging for GPU programs. The amount of available memory on modern GPUs is relatively small.  Scenes for feature film rendering and visualization have large geometric complexity and can easily contain millions of polygons and a large number of texture maps and other data attributes. CentiLeo presents an interactive out-of-core ray tracing engine running on the single desktop GPU. The system is built around a virtual memory manager. A novel ray intersection algorithm built around an acceleration structure, cached on the GPU, loads needed data on-demand using page swapping. The ray tracing engine is used to implement a variety of rendering and light transport algorithms. The system is implemented using CUDA and runs on a single NVIDIA GTX 480.

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Abstract:

Algebraic multigrid methods for large, sparse linear systems are a necessity in many computational simulations, yet parallel algorithms for such solvers are generally decomposed into coarse-grained tasks suitable for distributed computers with traditional processing cores. However, accelerating multigrid on massively parallel throughput-oriented processors, such as the GPU, demands algorithms with abundant fine-grained parallelism. In this paper, we develop a parallel algebraic multigrid method which exposes substantial fine-grained parallelism in both the construction of the multigrid hierarchy as well as the cycling or solve stage. Our algorithms are expressed in terms of scalable parallel primitives that are efficiently implemented on the GPU. The resulting solver achieves an average speedup of over 2x in the setup phase and around 6x in the cycling phase when compared to a representative CPU implementation.

(Nathan Bell, Steven Dalton and Luke Olson: “Exposing Fine-Grained Parallelism in Algebraic Multigrid Methods”, NVIDIA Technical Report NVR-2011-002, June 2011 [PDF and Sources])

Abstract:

Multigrid methods are efficient and fast solvers for problems typically modeled by partial differential equations of elliptic type. For problems with complex geometries and local singularities stencil-type discrete operators on equidistant Cartesian grids need to be replaced by more flexible concepts for unstructured meshes in order to properly resolve all problem-inherent specifics and for maintaining a moderate number of unknowns. However, flexibility in the meshes goes along with severe drawbacks with respect to parallel execution – especially with respect to the definition of adequate smoothers. This point becomes in particular pronounced in the framework of fine-grained parallelism on GPUs with hundreds of execution units. We use the approach of matrix-based multigrid that has high flexibility and adapts well to the exigences of modern computing platforms.

In this work we investigate multi-colored Gauss-Seidel type smoothers, the power(q)-pattern enhanced multi-colored ILU(p) smoothers with fill-ins, and factorized sparse approximate inverse (FSAI) smoothers. These approaches provide efficient smoothers with a high degree of parallelism. In combination with matrix-based multigrid methods on unstructured meshes our smoothers provide powerful solvers that are applicable across a wide range of parallel computing platforms and almost arbitrary geometries. We describe the configuration of our smoothers in the context of the portable lmpLAtoolbox and the HiFlow3 parallel finite element package. In our approach, a single source code can be used across diverse platforms including multicore CPUs and GPUs. Highly optimized implementations are hidden behind a unified user interface. Efficiency and scalability of our multigrid solvers are demonstrated by means of a comprehensive performance analysis on multicore CPUs and GPUs.

V. Heuveline, D. Lukarski, N. Trost and J.-P. Weiss. Parallel Smoothers for Matrix-based Multigrid Methods on Unstructured Meshes Using Multicore CPUs and GPUs. EMCL Preprint Series No. 9. 2011.

Abstract:

We present a highly parallel implementation of the cross-correlation of time-series data using graphics processing units (GPUs), which is scalable to hundreds of independent inputs and suitable for the processing of signals from “Large-N” arrays of many radio antennas. The computational part of the algorithm, the X-engine, is implementated efficiently on Nvidia’s Fermi architecture, sustaining up to 79% of the peak single precision floating-point throughput. We compare performance obtained for hardware- and software-managed caches, observing significantly better performance for the latter. The high performance reported involves use of a multi-level data tiling strategy in memory and use of a pipelined algorithm with simultaneous computation and transfer of data from host to device memory. The speed of code development, flexibility, and low cost of the GPU implementations compared to ASIC and FPGA implementations have the potential to greatly shorten the cycle of correlator development and deployment, for cases where some power consumption penalty can be tolerated.

(M. A. Clark, P. C. La Plante, L. J. Greenhill: “Accelerating Radio Astronomy Cross-Correlation with Graphics Processing Units”, July 2011. [Preprint on ARXIV] [Sources on GITHUB])

Abstract:

Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution. There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needs to be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motion compensation, are normally applied. The high computational power of modern graphic cards has already successfully been used for MRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing steps and two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implemented on a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on the GPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping (SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s if three GPUs are used, compared to 0.5 – 2.5 h with an optimized CPU implementation. The presented work will save time for researchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, both for conventional fMRI and for real-time fMRI.

(Anders Eklund, Mats Andersson, Hans Knutsson: “fMRI Analysis on the GPU – Possibilities and Challenges”, Computer Methods and Programs in Biomedicine, 2011 [DOI])

Abstract:

Parametric statistical methods, such as Z-, t-, and F-values are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With non-parametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient Graphics Processing Units (GPUs) can be used to speed up random permutation tests. A test with 10 000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation based approach, brain activity maps generated by the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level. During the development of the routines and writing of the paper, 3-4 years of processing time has been saved by using the GPU.

(Anders Eklund, Mats Andersson, Hans Knutsson: “Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single Subject fMRI Analysis”, International Journal of Biomedical Imaging, Article ID 627947, 2011 [Youtube Video] [PDF])

Abstract:

The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose, i.e. noisy, computed tomography (CT) data. The medical imaging domain has seen a tremendous development during the last decades. It is now possible to collect time resolved volumes, i.e. 4D data, with a number of modalities (e.g. ultrasound (US), CT, magnetic resonance imaging (MRI)). While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time (and not a single volume at a time). By using all the dimensions, it is for example possible to remove some of the time varying reconstruction artefacts that exist in CT volumes. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 x 512 x 445 x 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT based filtering. Fast spatial filtering makes it possible to apply the denoising algorithm to larger datasets (compared to if FFT based filtering is used). The short processing time increases the clinical value of true 4D image denoising significantly.

(Anders Eklund, Mats Andersson, Hans Knutsson: “True 4D Image Denoising on the GPU”, International Journal of Biomedical Imaging, Article ID 952819, 2011 [Youtube Video] [PDF])

Abstract:

In this work, we present an interactive visual clustering approach for the exploration and analysis of vast volumes of data. Our proposed approach is a bio-inspired collective behavioral model to be used in a 3D graphics environment. Our paper illustrates an extension of the behavioral model for clustering and a parallel implementation, using Compute Unified Device Architecture to exploit the computational power of Graphics Processor Units (GPUs). The advantage of our approach is that, as data enters the environment, the user is directly involved in the data mining process. Our experiments illustrate the effectiveness and efficiency provided by our approach when applied to a number of real and synthetic data sets.

(U. Erra, B. Frola, and V. Scarano: “A GPU-based Interactive Bio-inspired Visual Clustering”, Proceedings of the 2011 IEEE Symposium on Computational Intelligence and Data Mining. Paris, France. April 11-15, 2011 [PDF] [Video])