Exploring performance and power properties of modern multicore chips via simple machine models

Modern multicore chips show complex behavior with respect to performance and power. Starting with the Intel Sandy Bridge processor, it has become possible to directly measure the power dissipation of a CPU chip and correlate this data with the performance properties of the running code. Going beyond a simple bottleneck analysis, we employ the recently published Execution-Cache-Memory (ECM) model to describe the single- and multi-core performance of streaming kernels. The model refines the well-known roofline model, since it can predict the scaling and the saturation behavior of bandwidth-limited loop kernels on a multicore chip. The saturation point is especially relevant for considerations of energy consumption. From power dissipation measurements of benchmark programs with vastly different requirements to the hardware, we derive a simple, phenomenological power model for the Sandy Bridge processor. Together with the ECM model, we are able to explain many peculiarities in the performance and power behavior of multicore processors, and derive guidelines for energy-efficient execution of parallel programs. Finally, we show that the ECM and power models can be successfully used to describe the scaling and power behavior of a lattice-Boltzmann flow solver code.Comment: 23 pages, 10 figures. Typos corrected, DOI adde

Performance Analysis of the Lattice Boltzmann Model Beyond Navier-Stokes

The lattice Boltzmann method is increasingly important in facilitating large-scale fluid dynamics simulations. To date, these simulations have been built on discretized velocity models of up to 27 neighbors. Recent work has shown that higher order approximations of the continuum Boltzmann equation enable not only recovery of the Navier-Stokes hydrodynamics, but also simulations for a wider range of Knudsen numbers, which is especially important in micro- and nanoscale flows. These higher-order models have significant impact on both the communication and computational complexity of the application. We present a performance study of the higherorder models as compared to the traditional ones, on both the IBM Blue Gene/P and Blue Gene/Q architectures. We study the tradeoffs of many optimizations methods such as the use of deep halo level ghost cells that, alongside hybrid programming models, reduce the impact of extended models and enable efficient modeling of extreme regimes of computational fluid dynamics.Physic

Multiscale Modeling of Biological Flow using Lattice Boltzmann Method

In this dissertation, we have developed a fluid-structure interaction code specifically designed to simulate soft microparticle deformation in biological flow. We have used this tool for two different applications. First, we study red blood cell deformation under shear flow to evaluate stress distribution on membrane and subsequently pore formation on RBC membrane. Second, we utilized this code to show a proof of concept for an idea where we can separate soft particles based on their biophysical properties. In the following, these applications are discussed in more details.Under high shear rates, pores form on RBC membrane through which hemoglobin leaks out and increases free hemoglobin content of plasma leading to hemolysis. We hypothesize that local flow dynamics such as flow rate and shear stress determines blood cell damage. In this dissertation, a novel model is presented to study red blood cell (RBC) hemolysis at cellular level. The goal of the proposed work is to establish multiscale computational techniques to predict the blood cell dynamics and damage in complex flow conditions, i.e., blood-wetting biomedical devices. The cell membrane damage model will be coupled with local fluid flow to study cell deformation and rupture and a generalized cellular level blood cell damage model will be developed based on these simulations. By coupling Lattice Boltzmann and spring connected network models through immersed boundary method, we estimate hemolysis of a single red blood cell under various shear rates. First, we use adaptive meshing to find local strain distribution and critical sites on RBC membrane, then we apply underlying molecular dynamic simulations to evaluate damage. Our approach is comprised of three sub-models: defining criteria of pore formation, calculating pore size, and measuring Hb diffusive flux out of pores. Our damage model uses information of different scales to predict cellular level hemolysis. Results are compared with experimental studies and other models in literature. The developed cellular damage model can be used as a predictive tool for hydrodynamic and hematologic design optimization of blood-wetting medical devices.Isolating cells of interest from a heterogeneous mixture has been of critical importance in biological studies and clinical applications. In this dissertation, we have proposed to use ciliary system in microfluidic devices to isolate target subpopulation of soft particles based on their biophysical properties. In this model, the bottom of microchannel is covered with an equally spaced cilia array which can be magnetically actuated. A series of simulations are performed to study cilia-particle interaction and isolation dynamic. It is shown that these elastic hair-like filaments can influence particle’s trajectories differently depending on their biophysical properties. This modeling study also uses immersed boundary (IB) method coupled with lattice Boltzmann method. Soft particles are simulated by connected network of nonlinear springs. Moreover, cilia is modeled by point-particle scheme. It is demonstrated that active ciliary system is able to continuously and non-destructively sort cells based on their size, shape and stiffness. Ultimately, a design map for fabrication of a programmable microfluidic device capable of isolating various subpopulation of cells is developed. This biocompatible, label-free design can separate cells/soft microparticles with high throughput which can greatly complement existing separation technologies

Applications of CFD Simulations on Microfluidic Systems for Nanoparticle Synthesis

Microfluidics has been extensively investigated as a unique platform to synthesize nanoparticles with desired properties, e.g., size and morphology. Compared to the conventional batch reactors, wet-chemical synthesis using continuous flow microfluidics provides better control over addition of reagents, heat and mass transfer, and reproducibility. Recently, millifluidics has emerged as an alternative since it offers similar control as microfluidics. With its dimensions scaled up to millimeter size, millifluidics saves fabrication efforts and potentially paves the way for industrial applications. Good designs and manipulations of microfluidic and millifluidic devices rely on solid understanding of fluid dynamics. Fluid flow plays an important role in heat and mass transfer; thereby, it determines the quality of the synthesized nanoparticles. Computational fluid dynamics (CFD) simulations provide an effective approach to understand various effects on fluid flows without carrying out complicated experiments. The goal of this project is to utilize CFD simulations to study flow behaviors inside microfluidic and millifluidic. Residence time distribution (RTD) analysis coupled with TEM characterization was applied to investigate the effect of reagent flow rates on particle sizes distribution. Droplet-based microfluidics, as a solution to intrinsic drawbacks associated with single-phase microfluidics, depends on proper manipulation of the flow to generate steady droplet flow. The droplet / slug formation process inside a millifluidic reactor was investigated by both experiments and numerical simulations to understand the hydrodynamics of slug breaking. Geometric optimization was carried out to analyze the dependency of slug sizes on geometric dimensions. Numerical simulations were also performed to quantify the mixing efficiency inside slugs. This work provides insight to understand fluid flow inside microfluidic and millifluidic systems. It may benefit the design and operations of novel microfluidic and millifluidic systems

The effect of stochastic nano-scale surface roughness on microfluidic flow in computational microchannels

Microfluidics is a promising technology that is used extensively in biomedical devices, so called lab-on-a-chip devices. These devices harness a network of microchannels to mix, react, and conduct fluid flow. Most microchannel fabrication methods produce a stochastic surface roughness with heights ranging in the micro- to nano- scale. This inherent, stochastic roughness can potentially be harnessed to enhance microfluidic operations. Previous research on rough surfaces in microfluidics has focused on periodic, micro-scale obstructions, not of any stochastic nature. The purpose of this research is to characterize the effect of stochastic nano-scale surface roughness on microfluidic flow using very large-scale direct numerical simulations (DNS) and micro- particle image velocimetry (micro-PIV). The two studies are focused on a microchannel with one of the walls, the bottom surface, which has a manufactured surface roughness using a hydrofluoric-acid (HF) etching process. The rough surface is scanned by an optical profilometer, and the exact topography is imported as the bottom surface of the computational microchannel. HF-acid etched glass and un-etched glass surfaces are directly compared to each other. In the first study, the DNS simulations are compared to micro-PIV experiments for a Newtonian fluid (water). The flow regime was laminar, diffusion dominated and limited to Re \u3c 10. The second study used a longer microchannel relative to the first study that was made possible by stitching together consecutive profilometer surface scans. This study only used simulations to study the effect of nano-scale roughness on microfluidic flow (with the previous study forming a basis for model validation). In the future, the study will be extended to Newtonian as well as non-Newtonian (shear-thinning) fluids in the same flow regime as the first study. Overall, we have shown that an experimentally validated and experimentally driven three-dimensional computational study for microfluidic stochastic surface roughness is possible. Additionally, we have shown that the stochastic nature of the surface roughness and its effect on fluid flow can be characterized with numerous tools including velocity-perturbation contours, autocorrelation length (ACL), and energy spectra analysis. The different analyses illustrated the effect of the rough surface in different ways. Velocity-perturbation contours showed that both the etched and un-etched rough surfaces produced very small velocity structures (eddies) very near the rough surface that merge to form larger structures as the height above the rough surface increases. The velocity-perturbation contours revealed an increase in the magnitude of the velocity perturbations by an order of magnitude by using the etched glass, which was directly caused by the increase in roughness height from HF etching. The ACL analyses also showed how the surface roughness produces small perturbation structures that merge and persist well into the midplane of the microchannel. Energy spectra analyses revealed a transfer of energy caused by the structures of the rough surfaces. Notably for the same Reynolds number, the etched surface produced velocity-perturbation structures that contained more energy and persisted higher into the microchannel compared to the un-etched surface. This research has shown that a chemical etching surface treatment and other stochastic rough surfaces, even at the nano-scale, have an effect on microfluidic flow that can be characterized and potentially be harnessed across a range of fluid flow rates. Devices that use microchannels such as lab-on-a-chip medical devices can therefore be tuned and optimized for their respective applications such as reagent mixing, bubble creation and transport, fluid transport, and cell manipulation using stochastic surface roughness