swTVM: Exploring the Automated Compilation for Deep Learning on Sunway Architecture

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the exiting deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific and deep learning applications. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient code for deep learning application on Sunway. The experimental results show the ability of swTVM to automatically generate code for various deep neural network models on Sunway. The performance of automatically generated code for AlexNet and VGG-19 by swTVM achieves 6.71x and 2.45x speedup on average than hand-optimized OpenACC implementations on convolution and fully connected layers respectively. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and high performance architecture particularly with productivity and efficiency in mind. We would like to open source the implementation so that more people can embrace the power of deep learning compiler and Sunway many-core processor

Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference

There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods for temporal relation extraction.Comment: 10 pages, 4 figures, 7 tables, accepted by AAAI 202

Molecular detection of Cylindrocarpon destructans in infected Chinese ginseng roots and soil

Ginseng (<i>Panax ginseng</i> C.A. Meyer) is one of the most important medicinal plants in China, but its yields are often reduced by a variety of root pathogens. The root rot of ginseng is a destructive soil-borne disease caused by Cylindrocarpon destructans (teleomorph: Neonectria radicicola). A species-specific polymerase chain reaction (PCR) assay was developed for rapid detection of C. destructans in diseased ginseng roots and artificially inoculated soil. One pair of specific primers was designed from comparisons of nucleotide sequences of the nuclear ribosomal internal transcribed spacer (ITS) regions of 22 fungal isolates from northeast of China. Under stringent PCR conditions, primers CD-F and CD-R amplified only a 450 bp fragment from C. destructans DNA but not from other pathogens or negative control. The sensitivity of detection was 1 pg genomic DNA per 25 ìl PCR reaction volume, and C. destructans could be specifically detected with CD-F/CD-R from infected ginseng roots and soil. The approach outlined here could be further utilized as a rapid and reliable tool for the diagnosis and monitoring of the root rot of ginseng.Key words: Panax ginseng, Cylindrocarpon destructans, internal transcribed spacer (ITS), polymerase chain reaction (PCR), molecular detection

Various limit theorems for ratios from the uniform distribution

In this paper, we consider the ratios of order statistics in samples from uniform distribution and establish strong and weak laws for these ratios

Ultralow field magnetization reversal of two-body magnetic nanoparticles

Field induced magnetization reversal was investigated in a system of two magnetic nanoparticles with uniaxial anisotropies and magnetostatic interaction. By using the micromagnetic simulation, ultralow switching field strength was found when the separation distance between the two particles reaches a critical small value (on nanometer scale) in the perpendicular configuration where the anisotropic axes of the two particles are perpendicular to the separation line. The switching field increases sharply when the separation is away from the critical distance. The ultralow field switching phenomenon was missed in the parallel configuration where both the anisotropic axes are aligned along the separation line of the two particles. The micromagnetic results are consistent with the previous theoretical prediction [J. Appl. Phys. 109, 104303 (2011)] where dipolar interaction between two single-domain magnetic particles was considered. Our present simulations offered further proofs and possibilities for the low-power applications of information storage as the two-body magnetic nanoparticles might be implemented as a composite information bit