No abstract

A general geometry gyro-kinetic model for particle simulation of plasma turbulence in tokamak experiments is described. It incorporates the comprehensive influence of noncircular cross section, realistic plasma profiles, plasma rotation, neoclassical (equilibrium) electric fields, and Coulomb collisions. An interesting result of global turbulence development in a shaped tokamak plasma is presented with regard to nonlinear turbulence spreading into the linearly stable region. The mutual interaction between turbulence and zonal flows in collisionless plasmas is studied with a focus on identifying possible nonlinear saturation mechanisms for zonal flows. A bursting temporal behavior with a period longer than the geodesic acoustic oscillation period is observed even in a collisionless system. Our simulation results suggest that the zonal flows can drive turbulence. However, this process is too weak to be an effective zonal flow saturation mechanism.

Modern large-scale scientific simulations running on HPC systems generate data in the order of terabytes during a single run. To lessen the I/O load during a simulation run, scientists are forced to capture data infrequently, thereby making data collection an inherently lossy process. Yet, lossless compression techniques are hardly suitable for scientific data due to its inherently random nature; for the applications used here, they offer less than 10% compression rate. They also impose significant overhead during decompression, making them unsuitable for data analysis and visualization that require repeated data access.To address this problem, we propose an effective method for In-situ Sort-And-B-spline Error-bounded Lossy Abatement (ISABELA) of scientific data that is widely regarded as effectively incompressible. With IS-ABELA, we apply a preconditioner to seemingly random and noisy data along spatial resolution to achieve an accurate fitting model that guarantees a ≥ 0.99 correlation with the original data. We further take advantage of temporal patterns in scientific data to compress data by ≈ 85%, while introducing only a negligible overhead on simulations in terms of runtime. ISABELA significantly outperforms existing lossy compression methods, such as Wavelet compression. Moreover, besides being a communication-free and scalable compression technique, ISABELA is an inherently local decompression method, namely it does not decode the entire data, making it attractive for random access.