Electric field control of nonvolatile four-state magnetization at room temperature

We find the realization of large converse magnetoelectric (ME) effects at room temperature in a multiferroic hexaferrite Ba$_{0.52}$Sr$_{2.48}$Co$_{2}$Fe$_{24}$O$_{41}$ single crystal, in which rapid change of electric polarization in low magnetic fields (about 5 mT) is coined to a large ME susceptibility of 3200 ps/m. The modulation of magnetization then reaches up to 0.62 $\mu$$_{B}$/f.u. in an electric field of 1.14 MV/m. We find further that four ME states induced by different ME poling exhibit unique, nonvolatile magnetization versus electric field curves, which can be approximately described by an effective free energy with a distinct set of ME coefficients

A case study for reversible computing: Reversible debugging of concurrent programs

Reversible computing allows one to run programs not only in the usual forward direction, but also backward. A main application area for reversible computing is debugging, where one can use reversibility to go backward from a visible misbehaviour towards the bug causing it. While reversible debugging of sequential systems is well understood, reversible debugging of concurrent and distributed systems is less settled. We present here two approaches for debugging concurrent programs, one based on backtracking, which undoes actions in reverse order of execution, and one based on causal consistency, which allows one to undo any action provided that its consequences, if any, are undone beforehand. The first approach tackles an imperative language with shared memory, while the second one considers a core of the functional message-passing language Erlang. Both the approaches are based on solid formal foundations

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

The most popular multithreaded languages based on the fork-join concurrency model (CIlkPlus, OpenMP) are currently being extended to support other forms of parallelism (vectorization, pipelining and single-instruction-multiple-data (SIMD)). In the SIMD case, the objective is to execute the corresponding code on a many-core device, like a GPGPU, for which the CUDA language is a natural choice. Since the programming concepts of CilkPlus and OpenMP are very different from those of CUDA, it is desirable to automatically generate optimized CUDA-like code from CilkPlus or OpenMP. In this thesis, we propose an accelerator model for annotated C/C++ code together with an implementation that allows the automatic generation of CUDA code. One of the key features of this CUDA code generator is that it supports the generation of CUDA kernel code where program parameters (like number of threads per block) and machine parameters (like shared memory size) are treated as unknown symbols. Hence, these parameters need not to be known at code-generation-time: machine parameters and program parameters can be respectively determined when the generated code is installed on the target machine. In addition, we show how these parametric CUDA programs can be optimized at compile-time in the form of a case discussion, where cases depend on the values of machine parameters (e.g. hardware resource limits) and program parameters (e.g. dimension sizes of thread-blocks). This generation of parametric CUDA kernels requires to deal with non-linear polynomial expressions during the dependence analysis and tiling phase. To achieve these algebraic calculations, we take advantage of techniques from computer algebra, in particular in the RegularChains library of Maple. Various illustrative examples are provided together with performance evaluation