Abstract-Most of today's processors include vector units that have been designed to speedup single threaded programs. Although vector instructions can deliver high performance, writing vector code in assembly language or using intrinsics in high level languages is a time consuming and error-prone task. The alternative is to automate the process of vectorization by using vectorizing compilers.This paper evaluates how well compilers vectorize a synthetic benchmark consisting of 151 loops, two application from Petascale Application Collaboration Teams (PACT), and eight applications from Media Bench II. We evaluated three compilers: GCC (version 4.7.0), ICC (version 12.0) and XLC (version 11.01). Our results show that despite all the work done in vectorization in the last 40 years 45-71% of the loops in the synthetic benchmark and only a few loops from the real applications are vectorized by the compilers we evaluated.

The continuing importance of game applications and other numerically intensive workloads has generated an upsurge in novel computer architectures tailored for such functionality. Game applications feature highly parallel code for functions such as game physics, which have high computation and memory requirements, and scalar code for functions such as game artificial intelligence, for which fast response times and a full-featured programming environment are critical. The Cell Broadband Enginee architecture targets such applications, providing both flexibility and high performance by utilizing a 64-bit multithreaded PowerPCt processor element (PPE) with two levels of globally coherent cache and eight synergistic processor elements (SPEs), each consisting of a processor designed for streaming workloads, a local memory, and a globally coherent DMA (direct memory access) engine. Growth in processor complexity is driving a parallel need for sophisticated compiler technology. In this paper, we present a variety of compiler techniques designed to exploit the performance potential of the SPEs and to enable the multilevel heterogeneous parallelism found in the Cell Broadband Engine architecture. Our goal in developing this compiler has been to enhance programmability while continuing to provide high performance. We review the Cell Broadband Engine architecture and present the results of our compiler techniques, including SPE optimization, automatic code generation, single source parallelization, and partitioning.

This study investigated the effects of different supplementation ways of lycopene during pre-hatch (from the diet of hens) and post-hatch (from the diet of progeny) on production performance, antioxidant capacity and biochemical parameters in chicks. In total, 360 hens were fed diets supplemented with 0 (control group) or 40 mg lycopene/kg diet. From 28 to 34 days after the start of supplementation (30 weeks old), 650 qualified eggs were collected to artificial incubation. In this trial, 2 × 2 factorial designs were used. Male chicks hatched from hens fed with 0 or 40 mg lycopene/kg diet were fed a diet containing either 0 or 40 mg lycopene/kg diet. The results showed that, relative to control, in ovo-deposited lycopene significantly increased chick birth body weight, improved liver total antioxidant capacity (T-AOC), glutathione peroxidase (GSH-Px) and glutathione to oxidized glutathione ratio (GSH: GSSG), and significantly declined liver malondialdehyde (MDA) level and increased liver lycopene content during 0-14 days after hatching. On days 14 after hatching, dietary lycopene in diet began to take over gradually. Both supplementation ways of lycopene increased immune organ index, serum high-density lipoprotein (HDL) cholesterol, villus length and villus/crypt in duodenum, jejunum and ileum. Data in this study suggested lycopene supplementation could improve antioxidant capacity and immune function, and regulate lipid metabolism in chicks.

Object categories inherently form a hierarchy with di erent levels of concept abstraction, especially for ne-grained categories. For example, birds (Aves) can be categorized according to a four-level hierarchy of order, family, genus, and species. This hierarchy encodes rich correlations among various categories across di erent levels, which can e ectively regularize the semantic space and thus make prediction less ambiguous. However, previous studies of negrained image recognition primarily focus on categories of one certain level and usually overlook this correlation information. In this work, we investigate simultaneously predicting categories of di erent levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework. Specically, the HSE framework sequentially predicts the category score vector of each level in the hierarchy, from highest to lowest. At each level, it incorporates the predicted score vector of the higher level as prior knowledge to learn ner-grained feature representation. During training, the predicted score vector of the higher level is also employed to regularize label prediction by using it as soft targets of corresponding sub-categories. To evaluate the proposed framework, we organize the 200 bird species of the Caltech-UCSD birds dataset with the four-level category hierarchy and construct a large-scale butter y dataset that also covers four level categories. Extensive experiments on these two and the newly-released VegFru datasets demonstrate the superiority of our HSE framework over the baseline methods and existing competitors.

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