Simultaneous multiple path measurements of temperature and H2O concentration will be presented for the AIMHYE test entries in the NASA Ames 16-Inch Shock Tunnel. Monitoring the progress of high temperature chemical reactions that define scramjet combustor efficiencies is a task uniquely suited to nonintrusive optical diagnostics. One application strategy to overcome the many challenges and limitations of nonintrusive measurements is to use laser absorption spectroscopy coupled with optical fibers. Absorption spectroscopic techniques with rapidly tunable lasers are capable of making simultaneous measurements of mole fraction, temperature, pressure, and velocity. The scramjet water absorption diagnostic was used to measure combustor efficiency and was compared to thrust measurements using a nozzle force balance and integrated nozzle pressures to develop a direct technique for evaluating integrated scramjet performance. Tests were initially performed with a diode laser tuning over a water absorption feature at 1391.7 nm. A second diode laser later became available at a wavelength near 1343.3 nm covering an additional water absorption feature and was incorporated in the system for a two-wavelength technique. Both temperature and mole fraction can be inferred from the lineshape analysis using this approach. Additional high temperature spectroscopy research was conducted to reduce uncertainties in the scramjet application. The lasers are optical fiber coupled to ports at the combustor exit and in the nozzle region. The output from the two diode lasers were combined in a single fiber, and the resultant two-wavelength beam was subsequently split into four legs. Each leg was directed through 60 meters of optical fiber to four combustor exit locations for measurement of beam intensity after absorption by the water within the flow. Absorption results will be compared to 1D combustor analysis using RJPA and nozzle CFD computations as well as to data from a nozzle metric
Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9 performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and _tar_gz/3205249. PMID:27420300
A Graham Bell 4 Stroke Performance Tuning
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Computational modeling of drug binding to proteins is an integral component of direct drug design. Particularly, structure-based virtual screening is often used to perform large-scale modeling of putative associations between small organic molecules and their pharmacologically relevant protein targets. Because of a large number of drug candidates to be evaluated, an accurate and fast docking engine is a critical element of virtual screening. Consequently, highly optimized docking codes are of paramount importance for the effectiveness of virtual screening methods. In this communication, we describe the implementation, tuning and performance characteristics of GeauxDock, a recently developed molecular docking program. GeauxDock is built upon the Monte Carlo algorithm and features a novel scoring function combining physics-based energy terms with statistical and knowledge-based potentials. Developed specifically for heterogeneous computing platforms, the current version of GeauxDock can be deployed on modern, multi-core Central Processing Units (CPUs) as well as massively parallel accelerators, Intel Xeon Phi and NVIDIA Graphics Processing Unit (GPU). First, we carried out a thorough performance tuning of the high-level framework and the docking kernel to produce a fast serial code, which was then ported to shared-memory multi-core CPUs yielding a near-ideal scaling. Further, using Xeon Phi gives 1.9 performance improvement over a dual 10-core Xeon CPU, whereas the best GPU accelerator, GeForce GTX 980, achieves a speedup as high as 3.5. On that account, GeauxDock can take advantage of modern heterogeneous architectures to considerably accelerate structure-based virtual screening applications. GeauxDock is open-sourced and publicly available at www.brylinski.org/geauxdock and _tar_gz/3205249.
GPUs, with their high bandwidths and computational capabilities are an increasingly popular target for scientific computing. Unfortunately, to date, harnessing the power of the GPU has required use of a GPU-specific programming model like CUDA, OpenCL, or OpenACC. Thus, in order to deliver portability across CPU-based and GPU-accelerated supercomputers, programmers are forced to write and maintain two versions of their applications or frameworks. In this paper, we explore the use of a compiler-based autotuning framework based on CUDA-CHiLL to deliver not only portability, but also performance portability across CPU- and GPU-accelerated platforms for the geometric multigrid linear solvers found inmore many scientific applications. We also show that with autotuning we can attain near Roofline (a performance bound for a computation and target architecture) performance across the key operations in the miniGMG benchmark for both CPU- and GPU-based architectures as well as for a multiple stencil discretizations and smoothers. We show that our technology is readily interoperable with MPI resulting in performance at scale equal to that obtained via hand-optimized MPI+CUDA implementation. less
A Low-level radio-frequency (LLRF) control systems is required to regulate the rf field in the rf cavity used for beam acceleration. As the LLRF system is usually complex, testing of the basic functions or control algorithms of this system in real time and in advance of beam commissioning is strongly recommended. However, the equipment necessary to test the LLRF system, such as superconducting cavities and high-power rf sources, is very expensive; therefore, we have developed a field-programmable gate array (FPGA)-based cavity simulator as a substitute for real rf cavities. Digital models of the cavity and other rf systems are implemented in the FPGA. The main components include cavity baseband models for the fundamental and parasitic modes, a mechanical model of the Lorentz force detuning, and a model of the beam current. Furthermore, in our simulator, the disturbance model used to simulate the power-supply ripples and microphonics is also carefully considered. Based on the presented cavity simulator, we have established an LLRF system test bench that can be applied to different cavity operational conditions. The simulator performance has been verified by comparison with real cavities in KEK accelerators. In this paper, the development and implementation of this cavity simulator is presented first, and the LLRF test bench based on the presented simulator is constructed. The results are then compared with those for KEK accelerators. Finally, several LLRF applications of the cavity simulator are illustrated.
The aerodynamic performance of hovering insects is largely explained by the presence of a stably attached leading edge vortex (LEV) on top of their wings. Although LEVs have been visualized on real, physically modeled, and simulated insects, the physical mechanisms responsible for their stability are poorly understood. To gain fundamental insight into LEV stability on flapping fly wings we expressed the Navier-Stokes equations in a rotating frame of reference attached to the wing's surface. Using these equations we show that LEV dynamics on flapping wings are governed by three terms: angular, centripetal and Coriolis acceleration. Our analysis for hovering conditions shows that angular acceleration is proportional to the inverse of dimensionless stroke amplitude, whereas Coriolis and centripetal acceleration are proportional to the inverse of the Rossby number. Using a dynamically scaled robot model of a flapping fruit fly wing to systematically vary these dimensionless numbers, we determined which of the three accelerations mediate LEV stability. Our force measurements and flow visualizations indicate that the LEV is stabilized by the ;quasi-steady' centripetal and Coriolis accelerations that are present at low Rossby number and result from the propeller-like sweep of the wing. In contrast, the unsteady angular acceleration that results from the back and forth motion of a flapping wing does not appear to play a role in the stable attachment of the LEV. Angular acceleration is, however, critical for LEV integrity as we found it can mediate LEV spiral bursting, a high Reynolds number effect. Our analysis and experiments further suggest that the mechanism responsible for LEV stability is not dependent on Reynolds number, at least over the range most relevant for insect flight (100 2ff7e9595c
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