1,493 research outputs found
Systolic and Hyper-Systolic Algorithms for the Gravitational N-Body Problem, with an Application to Brownian Motion
A systolic algorithm rhythmically computes and passes data through a network
of processors. We investigate the performance of systolic algorithms for
implementing the gravitational N-body problem on distributed-memory computers.
Systolic algorithms minimize memory requirements by distributing the particles
between processors. We show that the performance of systolic routines can be
greatly enhanced by the use of non-blocking communication, which allows
particle coordinates to be communicated at the same time that force
calculations are being carried out. Hyper-systolic algorithms reduce the
communication complexity at the expense of increased memory demands. As an
example of an application requiring large N, we use the systolic algorithm to
carry out direct-summation simulations using 10^6 particles of the Brownian
motion of the supermassive black hole at the center of the Milky Way galaxy. We
predict a 3D random velocity of 0.4 km/s for the black hole.Comment: 33 pages, 10 postscript figure
A pilgrimage to gravity on GPUs
In this short review we present the developments over the last 5 decades that
have led to the use of Graphics Processing Units (GPUs) for astrophysical
simulations. Since the introduction of NVIDIA's Compute Unified Device
Architecture (CUDA) in 2007 the GPU has become a valuable tool for N-body
simulations and is so popular these days that almost all papers about high
precision N-body simulations use methods that are accelerated by GPUs. With the
GPU hardware becoming more advanced and being used for more advanced algorithms
like gravitational tree-codes we see a bright future for GPU like hardware in
computational astrophysics.Comment: To appear in: European Physical Journal "Special Topics" : "Computer
Simulations on Graphics Processing Units" . 18 pages, 8 figure
Performance Analysis of Direct N-Body Algorithms on Special-Purpose Supercomputers
Direct-summation N-body algorithms compute the gravitational interaction
between stars in an exact way and have a computational complexity of O(N^2).
Performance can be greatly enhanced via the use of special-purpose accelerator
boards like the GRAPE-6A. However the memory of the GRAPE boards is limited.
Here, we present a performance analysis of direct N-body codes on two parallel
supercomputers that incorporate special-purpose boards, allowing as many as
four million particles to be integrated. Both computers employ high-speed,
Infiniband interconnects to minimize communication overhead, which can
otherwise become significant due to the small number of "active" particles at
each time step. We find that the computation time scales well with processor
number; for 2*10^6 particles, efficiencies greater than 50% and speeds in
excess of 2 TFlops are reached.Comment: 34 pages, 15 figures, submitted to New Astronom
Proteomic-biostatistic integrated approach for finding the underlying molecular determinants of hypertension in human plasma
Despite advancements in lowering blood pressure, the best approach to lower it remains controversial because of the lack of information on the molecular basis of hypertension. We, therefore, performed plasma proteomics of plasma from patients with hypertension to identify molecular determinants detectable in these subjects but not in controls and vice versa. Plasma samples from hypertensive subjects (cases; n=118) and controls (n=85) from the InGenious HyperCare cohort were used for this study and performed mass spectrometric analysis. Using biostatistical methods, plasma peptides specific for hypertension were identified, and a model was developed using least absolute shrinkage and selection operator logistic regression. The underlying peptides were identified and sequenced off-line using matrix-assisted laser desorption ionization orbitrap mass spectrometry. By comparison of the molecular composition of the plasma samples, 27 molecular determinants were identified differently expressed in cases from controls. Seventy percent of the molecular determinants selected were found to occur less likely in hypertensive patients. In cross-validation, the overall R(2) was 0.434, and the area under the curve was 0.891 with 95% confidence interval 0.8482 to 0.9349, P<0.0001. The mean values of the cross-validated proteomic score of normotensive and hypertensive patients were found to be -2.007±0.3568 and 3.383±0.2643, respectively, P<0.0001. The molecular determinants were successfully identified, and the proteomic model developed shows an excellent discriminatory ability between hypertensives and normotensives. The identified molecular determinants may be the starting point for further studies to clarify the molecular causes of hypertension
A bibliography on parallel and vector numerical algorithms
This is a bibliography of numerical methods. It also includes a number of other references on machine architecture, programming language, and other topics of interest to scientific computing. Certain conference proceedings and anthologies which have been published in book form are listed also
4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics
4D flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invasive examination. This enables qualitative and quantitative analysis of hemodynamic flow parameters of the heart and great vessels. An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. However, this must be balanced with increasing imaging time. The recent success of deep learning in generating super resolution images shows promise for implementation in medical images. We utilized computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2. We also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal volunteer data, and demonstrated comparable results with the actual flow rate measurements giving an absolute relative error of 0.6–5.8% and 1.1–3.8% in the phantom data and normal volunteer data, respectively
- …