GraphR: Accelerating Graph Processing Using ReRAM

This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suit- able for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x) speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes 4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is 3.67x to 10.96x more energy efficiency compared to PIM-based architecture.Comment: Accepted to HPCA 201

Plant invasions in China : an emerging hot topic in invasion science

China has shown a rapid economic development in recent decades, and several drivers of this change are known to enhance biological invasions, a major cause of biodiversity loss. Here we review the current state of research on plant invasions in China by analyzing papers referenced in the ISI Web of Knowledge. Since 2001, the number of papers has increased exponentially, indicating that plant invasions in China are an emerging hot topic in invasion science. The analyzed papers cover a broad range of methodological approaches and research topics. While more that 250 invasive plant species with negative impacts have been reported from China, only a few species have been considered in more than a handful of papers (in order of decreasing number of references: Spartina alterniflora, Ageratina adenophora, Mikania micrantha, Alternanthera philoxeroides, Solidago canadensis, Eichhornia crassipes). Yet this selection might rather reflect the location of research teams than the most invasive plant species in China. Considering the previous achievements in China found in our analysis research in plant invasions could be expanded by (1) compiling comprehensive lists of non-native plant species at the provincial and national scales and to include species that are native to one part of China but non-native to others in these lists; (2) strengthening pathways studies (primary introduction to the country, secondary releases within the country) to enhance prevention and management; and (3) assessing impacts of invasive species at different spatial scales (habitats, regions) and in relation to conservation resources

Adults regularize variation when linguistic cues suggest low input reliability

Children regularize inconsistent probabilistic patterns in linguistic input, yet they also acquire and match probabilistic sociolinguistic variation. What factors in the language input contribute to whether children will regularize or match the probabilistic patterns they are exposed to? Here, we test the hypothesis that low input reliability facilitates regularization. As a first step, we asked adult participants to acquire a variable plural marking pattern from a written (Exp 1) and a spoken (Exp 2) artificial language under different conditions, where they were led to believe input was more, or less, reliable. In both experiments, input reliability was manipulated through both information about the speaker (e.g., whether the speaker was likely to make mistakes) and linguistic cues (e.g., typos or pronunciation errors). Results showed that adults regularized the written language more only when they were told the speaker would make mistakes and the plural variants resembled typos (Exp 1), whereas they regularized the spoken language more when the plural variants resembled pronunciation errors regardless of the speaker’s said reliability in the spoken language. We conclude that input reliability is an important factor that can modulate learners’ regularization of probabilistic linguistic input, and that linguistic cues may play a more critical role than top-down knowledge about the speaker. The current study lays down an important foundation for future work exploring whether children are able to incorporate input reliability cues when learning probabilistic linguistic variation