[en] Monte Carlo (MC) neutron transport simulations are widely used in the nuclear community to perform reference calculations with minimal approximations. The conventional MC method has a slow convergence according to the law of large numbers, which makes simulations computationally expensive. Cross section computation has been identified as the major performance bottleneck for MC neutron code. Typically, cross section data are precalculated and stored into memory before simulations for each nuclide, thus during the simulation, only table lookups are required to retrieve data from memory and the compute cost is trivial. We implemented and optimized a large collection of lookup algorithms in order to accelerate this data retrieving process. Results show that significant speedup can be achieved over the conventional binary search on both CPU and MIC in unit tests other than real case simulations. Using vectorization instructions has been proved effective on many-core architecture due to its 512-bit vector units; on CPU this improvement is limited by a smaller register size. Further optimization like memory reduction turns out to be very important since it largely improves computing performance. As can be imagined, all proposals of energy lookup are totally memory-bound where computing units does little things but only waiting for data. In another word, computing capability of modern architectures are largely wasted. Another major issue of energy lookup is that the memory requirement is huge: cross section data in one temperature for up to 400 nuclides involved in a real case simulation requires nearly 1 GB memory space, which makes simulations with several thousand temperatures infeasible to carry out with current computer systems. In order to solve the problem relevant to energy lookup, we begin to investigate another on-the-fly cross section proposal called reconstruction. The basic idea behind the reconstruction, is to do the Doppler broadening (performing a convolution integral) computation of cross sections on-the-fly, each time a cross section is needed, with a formulation close to standard neutron cross section libraries, and based on the same amount of data. The reconstruction converts the problem from memory-bound to compute-bound: only several variables for each resonance are required instead of the conventional pointwise table covering the entire resolved resonance region. Though memory space is largely reduced, this method is really time-consuming. After a series of optimizations, results show that the reconstruction kernel benefits well from vectorization and can achieve 1806 GFLOPS (single precision) on a Knights Landing 7250, which represents 67% of its effective peak performance. Even if optimization efforts on reconstruction significantly improve the FLOP usage, this on-the-fly calculation is still slower than the conventional lookup method. Under this situation, we begin to port the code on GPGPU to exploit potential higher performance as well as higher FLOP usage. On the other hand, another evaluation has been planned to compare lookup and reconstruction in terms of power consumption: with the help of hardware and software energy measurement support, we expect to find a compromising solution between performance and energy consumption in order to face the 'power wall' challenge along with hardware evolution. (author)

[en] The authors introduce a three step method to optimally bias a photomultiplier obtaining the best performance in single photo-electron counting. Instead of employing an activated spectroscopy which is not easy to repeat condition, the single electron emitting noise was observed to characterize photo multipliers behavior relating to single photo-electron counting. Three parameters were used to detail single electron spectrum shape being advanced than other reported way. This method test the influence of three settings that would affect the spectrum shape

[en] We have developed a balanced homodyne detector capable of working with femtosecond pulsed light at a 76MHz repetition rate and 800nm wavelength. It exhibits a common mode rejection ratio of 55dB, and can achieve the shot-noise limit. Provided with good performance, our detector can be used in high speed quantum information applications. (paper)

[en] The ability to tailor and enhance photoluminescence (PL) behavior in two-dimensional (2D) transition metal dichalcogenides (TMDCs) such as molybdenum disulfide (MoS₂) is significant for pursuing optoelectronic applications. To achieve this, it has been essential to obtain high-quality single-layer MoS₂ and fully explore its intrinsic PL performance. Here, we fabricate single-layer MoS₂ by a thermal vapor sulfurization method in which a pre-deposited molybdenum trioxide (MoO₃) thin film is sulfurized over a short period (for several minutes) to turn into MoS₂. These as-grown MoS₂ crystals show quite strong PL, which is about one order of magnitude higher than that of chemical-vapor-deposited MoS₂. Temperature- and power-dependent spectroscopy measurements disclose the apparent influence of sulfur (S) vacancies on the PL behavior and the noticeable free-to-bound exciton recombinations in the luminescence process. The fact that PL intensity of the sample in vacuum sharply lowered down relative to in air reveals that the high PL is facilitated by molecular adsorption on S vacancies in air. And multi-channel decay processes coupled with S vacancies are revealed in the time-resolved PL spectroscopy. In our work, single-layer MoS₂ with high PL is synthesized and its defect-induced PL features are analyzed, which is of great importance for developing advanced nano-electronics and optoelectronics based on 2D structures. .

[en] The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hot-spot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and Many Integrated Core (MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and MIC. Using vectorization instructions has been proved efficient on many-core architecture due to its 512-bit Vector Processing Unit (VPU); on CPU this improvement is limited by the smaller VPU width. Further optimization like memory reduction turns out to be very important since it largely improves computing performance. (authors)