Active and passive microrheology with large tracers in hard colloids.

The dynamics of a tracer particle in a bath of quasi-hard colloidal spheres is studied by Langevin dynamics simulations and mode coupling theory (MCT); the tracer radius is varied from equal to up to seven times larger than the bath particles radius. In the simulations, two cases are considered: freely diffusing tracer (passive microrheology) and tracer pulled with a constant force (active microrheology). Both cases are connected by linear response theory for all tracer sizes. It links both the stationary and transient regimes of the pulled tracer (for low forces) with the equilibrium correlation functions; the velocity of the pulled tracer and its displacement are obtained from the velocity auto-correlation function and the mean squared displacement, respectively. The MCT calculations give insight into the physical mechanisms: At short times, the tracer rattles in its cage of neighbours, with the frequency increasing linearly with the tracer radius asymptotically. The long-time tracer diffusion coefficient from passive microrheology, which agrees with the inverse friction coefficient from the active case, arises from the transport of transverse momentum around the tracer. It can be described with the Brinkman equation for the transverse flow field obtained in extension of MCT, but cannot be recovered from the MCT kernel coupling to densities only. The dynamics of the bath particles is also studied; for the unforced tracer the dynamics is unaffected. When the tracer is pulled, the velocity field in the bath follows the prediction of the Brinkman model, but different from the case of a Newtonian fluid.

Dynamics and friction of a large colloidal particle in a bath of hard spheres: Langevin dynamics simulations and hydrodynamic description.

The analysis of the dynamics of tracer particles in a complex bath can provide valuable information about the microscopic behavior of the bath. In this work, we study the dynamics of a forced tracer in a colloidal bath by means of Langevin dynamics simulations and a theory model within continuum mechanics. In the simulations, the bath is comprised of quasihard spheres with a volume fraction of 50% immersed in a featureless quiescent solvent, and the tracer is pulled with a constant small force (within the linear regime). The theoretical analysis is based on the Navier-Stokes equation, where a term proportional to the velocity arises from coarse-graining the friction of the colloidal particles with the solvent. As a result, the final equation is similar to the Brinkman model, although the interpretation is different. A length scale appears in the model, k_{0}^{-1}, where the transverse momentum transport crosses over to friction with the solvent. The effective friction coefficient experienced by the tracer grows with the tracer size faster than the prediction from Stokes's law. Additionally, the velocity profiles in the bath decay faster than in a Newtonian fluid. The comparison between simulations and theory points to a boundary condition of effective partial slip at the tracer surface. We also study the fluctuations in the tracer position, showing that it reaches diffusion at long times, with a subdiffusive regime at intermediate times. The diffusion coefficient, obtained from the long-time slope of the mean-squared displacement, fulfills the Stokes-Einstein relation with the friction coefficient calculated from the steady tracer velocity, confirming the validity of the linear response formalism.

TomoEED: fast edge-enhancing denoising of tomographic volumes.

Summary: TomoEED is an optimized software tool for fast feature-preserving noise filtering of large 3D tomographic volumes on CPUs and GPUs. The tool is based on the anisotropic nonlinear diffusion method. It has been developed with special emphasis in the reduction of the computational demands by using different strategies, from the algorithmic to the high performance computing perspectives. TomoEED manages to filter large volumes in a matter of minutes in standard computers. Availability and implementation: TomoEED has been developed in C. It is available for Linux platforms at http://www.cnb.csic.es/%7ejjfernandez/tomoeed. Supplementary information: Supplementary data are available at Bioinformatics online.

Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction.

Modern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far are purely focused on either GPUs or CPUs. This work presents a hybrid approach that collaboratively combines the GPUs and CPUs available in a computer and applies it to the problem of tomographic reconstruction. Proper orchestration of workload in such a heterogeneous system is an issue. Here we use an on-demand strategy whereby the computing devices request a new piece of work to do when idle. Our hybrid approach thus takes advantage of the whole computing power available in modern computers and further reduces the processing time. This CPU+GPU co-processing can be readily extended to other image processing tasks in 3DEM.

A matrix approach to tomographic reconstruction and its implementation on GPUs.

Electron tomography allows elucidation of the molecular architecture of complex biological specimens. Weighted backprojection (WBP) is the standard reconstruction method in the field. In this work, three-dimensional reconstruction with WBP is addressed from a matrix perspective by formulating the problem as a set of sparse matrix-vector products, with the matrix being constant and shared by all the products. This matrix approach allows efficient implementations of reconstruction algorithms. Although WBP is computationally simple, the resolution requirements may turn the tomographic reconstruction into a computationally intensive problem. Parallel systems have traditionally been used to cope with such demands. Recently, graphics processor units (GPUs) have emerged as powerful platforms for scientific computing and they are getting increasing interest. In combination with GPU computing, the matrix approach for WBP exhibits a significant acceleration factor compared to the standard implementation.

Vectorization with SIMD extensions speeds up reconstruction in electron tomography.

Electron tomography allows structural studies of cellular structures at molecular detail. Large 3D reconstructions are needed to meet the resolution requirements. The processing time to compute these large volumes may be considerable and so, high performance computing techniques have been used traditionally. This work presents a vector approach to tomographic reconstruction that relies on the exploitation of the SIMD extensions available in modern processors in combination to other single processor optimization techniques. This approach succeeds in producing full resolution tomograms with an important reduction in processing time, as evaluated with the most common reconstruction algorithms, namely WBP and SIRT. The main advantage stems from the fact that this approach is to be run on standard computers without the need of specialized hardware, which facilitates the development, use and management of programs. Future trends in processor design open excellent opportunities for vector processing with processor's SIMD extensions in the field of 3D electron microscopy.