Computing autocorrelation coefficient can effectively reduce the influence of additive white noise, thus estimation precision will be improved. In this paper, an autocorrelation-like function, different from the ordinary one, is defined, and is proven to own better linear predictive performance. Two algorithms for signal model are developed to achieve frequency estimates. We analyze the theoretical properties of the algorithms in the additive white Gaussian noise. The simulation results match with the theoretical values well in the sense of mean square error. The proposed algorithms compare with existing estimators, are closer to the Cramer-Rao bound (CRLB). In addition, computer simulations demonstrate that the proposed algorithms provide high accuracy and good anti-noise capability.

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.

The nondeterminism of message-passing communication brings challenges to program debugging, testing and fault-tolerance. This paper proposes a novel deterministic message-passing implementation (DMPI) for parallel programs in the distributed environment. DMPI is compatible with the standard MPI in user interface, and it guarantees the reproducibility of message with high performance. The basic idea of DMPI is to use logical time to solve message races and control asynchronous transmissions, and thus we could eliminate the nondeterministic behaviors of the existing message-passing mechanism. We apply a buffering strategy to alleviate the performance slowdown caused by mismatch of logical time and physical time. To avoid deadlocks introduced by deterministic mechanisms, we also integrate DMPI with a lightweight deadlock checker to dynamically detect and solve these deadlocks. We have implemented DMPI and evaluated it using NPB benchmarks. The results show that DMPI could guarantee determinism with incurring modest runtime overhead (14% on average).

This paper creates a new hybrid Space-Time Adaptive Processing (STAP) algorithm that combines Direct Data Domain (D3) method and Space-Time Multiple-Beam (STMB) algorithm, which can effectively suppress discrete interference in the nonhomogeneous clutter environment. In the proposed hybrid algorithm, the D3 method is applied to process the discrete interference in the primary range cell, and the residual clutter is suppressed by the STMB algorithm. The performance of the proposed hybrid STAP algorithm is demonstrated in a simulation.

Existing large-scale systems suffer from various hardware/software failures, motivating the research of fault-tolerance techniques. Checkpoint-restart techniques are widely applied fault-tolerance approaches, especially in scientific computing systems. However, the overhead of checkpoint largely influences the overall system performance. Recently, the emerging byte-addressable, persistent memory technologies, such as phase change memory (PCM), make it possible to implement checkpointing in arbitrary data granularity. However, the impact of data granularity on the checkpointing cost has not been fully addressed. In this paper, we investigate how data granularity influences the performance of a checkpoint system. Further, we design and implement a high-performance checkpoint system named AG-ckpt. AG-ckpt is a hybrid-granularity incremental checkpointing scheme through: (1) low-cost modified-memory detection and (2) fine-grained memory duplication. Moreover, we also formulize the performance-granularity relationship of checkpointing systems through a mathematical model, and further obtain the optimum solutions. We conduct the experiments through several typical benchmarks to verify the performance gain of our design. Compared to conventional incremental checkpoint, our results show that AG-ckpt can reduce checkpoint data amount up to 50% and provide a speedup of 1.2x-1.3x on checkpoint efficiency.