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Vectorization of programs is crucial for achieving high performance on modern processors with SIMD (Single Instruction Multiple Data) extensions. Programs with IF-statements suffer from control flow divergence that seriously complicates automatic vectorization. Therefore, contemporary compilers employ the IF-conversion approach to convert control flow to data flow, which relies on using predicated execution techniques (i.e., masked or select SIMD instructions). In this paper, we enhance the compiler's capabilities to generate efficiently vectorized code for processors without masked instructions. We improve the state of the art in program vectorization by developing a novel approach—IF-select transformation—which is applicable to arbitrarily nested IF-statements. We implement our approach in the open-source Open64 compiler and evaluate its performance on the SW26010 processor used in the Sunway TaihuLight supercomputer (currently #3 in the TOP500 list) that does not support masked instructions. We extend our vectorization approach by providing an additional LLVM optimization pass to reduce the amount of masked memory accesses on processors without masked instructions, e.g., IBM Power8 and ARMCortex-A8. Experimental results demonstrate the performance advantages of the suggested vectorization techniques. [ABSTRACT FROM AUTHOR]

Iterative compilation based on machine learning can automatically predict the best optimization for the new programs. However, the efficient prediction models often require repetitive training, which leads to a higher training time overheads, and greatly affects the widespread utilization of the technology. The existing approaches in the prediction model construction often use random sample search strategy, which easily lead to data redundancy. In addition, due to the effect of run-time noises, the sample program is subjected to a fixed number of repetitive observations. However, in the case there is very little noises, the repetitive observations will result in a serious waste of iterative compilation time overheads. Therefore, how to effectively collect the optimal prediction model samples and choose the appropriate sample observations number are the key problem of reducing the iterative compilation overheads. We propose a low overheads iterative compilation optimization parameters prediction model ALIC. First, we describe the target programs by static-dynamic features representation based on feature-class relevance, and construct an initial optimization prediction model by the classifier. Then we use a dynamic number of sample observations strategy for each sample. The most profitable sample from the candidate samples set is selected and marked, each mark is equivalent to increase the number of sample observations. Finally, the optimization prediction model is constructed based on the intermediate prediction model that learns candidate samples actively. The experimental results show that when predicting optimization parameters for the new programs on Intel Xeon E5520 and Chinese Shenwei 26010 platforms, the ALIC model generates 1.38× (by ICC14.0 compiler), 1.35× (by GCC5.4 compiler) average performance improvement on the Xeon platform, and 1.42× (by SW compiler) on the Shenwei Platform. In addition, the ALIC model can significantly reduce the iterative compilation training time overheads than the existing approaches. [ABSTRACT FROM AUTHOR]

The power of data dependence testing techniques of a parallelizing compiler is its essence to transform and optimize programs. Numerous techniques were proposed in the past, and it is, however, still a challenging problem to evaluate the relative power of these techniques to better understand the data dependence testing problem. In the past, either empirical studies or experimental evaluation results are published to compare these data dependence testing techniques, being not able to convince the research community completely. In this paper, we show a theoretical study on this issue, comparing the power on disproving dependences of existing techniques by proving theorems in a proposed formal system K-DT. Besides, we also present the upper bounds of these techniques and introduce their minimum complete sets. To the best of our knowledge, K-DT is the first formal system used to compare the power of data dependence testing techniques, and this paper is the first work to show the upper bounds and minimum complete sets of data dependence testing techniques. [ABSTRACT FROM AUTHOR]

Semantic gap is a major challenge for virtualization security applications. Many researches have been carried out represented by virtual machine introspection around bridging the semantic gap. This paper analyzes the causes of semantic gap problem, discusses the differences of current semantic gap bridging method in the aspects of extracting and view generation in Guest status based on three typical model of virtual machine introspection, including Out-of band, In-band and Derivative, and evaluates these three models.It also counts on 34 kinds of mainstream methods features of virtual machine introspection on the basis of factors of data source, threat model and VMM security assumption. According to the analysis of defects in data reliability and status recognition of the current methods, this paper also put forward a view generation model which can be used to transfer secret information between Guest and VMM. [ABSTRACT FROM AUTHOR]

To further parallelize large-scale nonlinear scientific computing applications, some data dependence techniques for nonlinear subscripts, especially for quadratic subscripts, were proposed in the past. The quadratic programming (QP) test and polynomial variable interval (PVI) test are two representative techniques. The QP test, which serves as an exact but time-consuming technique, always gives conservative results when the coefficient matrix of the quadratic terms is not positive semi-definite, while the PVI test will lose efficiency when there exist mixed polynomials in the dependence equation. Focusing on the dependences caused by quadratic subscripts in nonlinear and irregular programs, we propose an improved nonlinear data dependence test in this paper. We first normalize a quadratic equation which is written in a general form, and determine whether the canonical equation is integer solvable in the region of interest based on the interval equation theory. Experimental results show that, compared with the QP test, our method maintains a much lower time complexity. Furthermore, it can detect more general dependences than other dependence testing methods like the PVI test in terms of quadratic subscripts. [ABSTRACT FROM AUTHOR]

Power is an important design constraint in embedded computing systems. To meet the power constraint, microarchitecture and hardware designed to achieve high performance need to be revisited, from both performance and power angles. This paper studies one of them: branch predictor. As well known, branch prediction is critical to exploit instruction level parallelism effectively, but may incur additional power consumption due to the hardware resource dedicated for branch prediction and the extra power consumed on mispredicted branches. This paper explores the design space of branch prediction mechanisms and tries to find the most beneficial one to realize low-power embedded processor. The sample processor studied is Godson-like processor, which is a dual-issue, out-of-order processor with deep pipeline, supporting MIPS instruction set. [ABSTRACT FROM AUTHOR]