Back-reaction instabilities of relativistic cosmic rays

We explore streaming instabilities of the electron-ion plasma with relativistic and ultra-relativistic cosmic rays in the background magnetic field in the multi-fluid approach. Cosmic rays can be both electrons and ions. The drift speed of cosmic rays is directed along the magnetic field. In equilibrium, the return current of the background plasma is taken into account. One-dimensional perturbations parallel to the magnetic field are considered. The dispersion relations are derived for transverse and longitudinal perturbations. It is shown that the back-reaction of magnetized cosmic rays generates new instabilities one of which has the growth rate that can approach the growth rate of the Bell instability. These new instabilities can be stronger than the cyclotron resonance instability. For unmagnetized cosmic rays, the growth rate is analogous to the Bell one. We compare two models of the plasma return current in equilibrium with three and four charged components. Some difference between these models is demonstrated. For longitudinal perturbations, an instability is found in the case of ultra-relativistic cosmic rays. The results obtained can be applied to investigation of astrophysical objects such as the shocks by supernova remnants, galaxy clusters, intracluster medium and so on, where interaction of cosmic rays with turbulence of the electron-ion plasma produced by them is of a great importance for the cosmic-ray evolution.Comment: Accepted for publication in Plasma Physics and Controlled Fusio

A Hardware Redundancy and Recovery Mechanism for Reliable Scientific Computation on Graphics Processors Abstract

General purpose computation on graphics processors (GPGPU) has rapidly evolved since the introduction of commodity programmable graphics hardware. With the appearance of GPGPU computation-oriented APIs such as AMD’s Close to the Metal (CTM) and NVIDIA’s Compute Unified Device Architecture (CUDA), we begin to see GPU vendors putting financial stakes into this non-graphics, one-time niche market. Major supercomputing installations are building GPGPU clusters to take advantage of massively parallel floating point capabilities, and Folding@Home has even released a GPU port of its protein folding distributed computation client. But in order for GPGPU to truly become important to the supercomputing community, vendors will have to address the heretofore unimportant reliability concerns of graphics processors. We present a hardware redundancy-based approach to reliability for general purpose computation on GPUs that requires minimal change to existing GPU architectures. Upon detecting an error, the system invokes an automatic recovery mechanism that only recomputes erroneous results. Our results show that our technique imposes less than a 1.5 × performance penalty and saves energy for GPGPU but is completely transparent to general graphics and does not affect the performance of the games that drive the market. 1