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A Study on Parallel Optimization of Regional Ocean Model Using KISTI 5th Supercomputer Nurion System
Dong-Hoon Kim, Chaewook Lim, Min-Su Joh, Jooneun An, Il-Ju Moon, Seung-Buhm Woo
http://doi.org/10.5626/JOK.2020.47.1.1
A parrallel optimiztion of the Regional Ocean Modeling system (ROMS) was conducted by analyzing the computational characteristics of the Knights Landing (KNL) system of Nurion, which is the fifth supercomputer at the Korea Institute of Science and Technology Information (KISTI). Although the KNL system comprises more than twice as many cores per node as the Skylake system, the performance of the KNL system is known to be about three times slower than that of Skylake system, when using the same number of cores. However, the KNL performance optimized for the ROMS model in Nurion is only about twice that of the Skylake system suggesting that the KNL system of Nurion is approximately 1.3-fold faster than the normal KNL system. In this study, two types of numerical experiments (ideal & real cases) were conducted to compare the computational capabilities of the KNL and SKL systems; The KNL system has more computational efficiency. The KNL system shows continuous improvement in performance even in maximum core experiments under both ideal and real case simulation, which is an advantage for the simulation of super parallel numerical calculation.
Grammatical Error Detection for L2 Learners Based on Attention Mechanism
Chanhee Park, Jinuk Park, Minsoo Cho, Sanghyun Park
http://doi.org/10.5626/JOK.2019.46.6.554
Grammar Error Detection refers to the work of discovering the presence and location of grammatical errors in a given sentence, and is considered to be useful for L2 learners to learn and evaluate the language. Systems for grammatical error correction have been actively studied, but there still exist limitations such as lack of training corpus and limited error type correction. Therefore, this paper proposes a model for generalized grammatical error detection through the sequence labeling problem which does not require the determination of error type. The proposed model dynamically decides character-level and word-level representation to deal with unexpected words in L2 learners" writing. Also, based on the proposed model the bias which can occur during the learning process with imbalanced data can be avoided through multi-task learning. Additionally, attention mechanism is applied to efficiently predict errors by concentrating on words for judging errors. To validate the proposed model, three test data were used and the effectiveness of the model was verified through the ablation experiment.
Biomedical Named Entity Recognition using Multi-head Attention with Highway Network
Minsoo Cho, Jinuk Park, Jihwan Ha, Chanhee Park, Sanghyun Park
http://doi.org/10.5626/JOK.2019.46.6.544
Biomedical named entity recognition(BioNER) is the process of extracting biomedical entities such as diseases, genes, proteins, and chemicals from biomedical literature. BioNER is an indispensable technique for the extraction of meaningful data from biomedical domains. The proposed model employs deep learning based Bi-LSTM-CRF model which eliminates the need for hand-crafted feature engineering. Additionally, the model contains multi-head attention to capture the relevance between words, which is used when predicting the label of each input token. Also, in the input embedding layer, the model integrates character-level embedding with word-level embedding and applies the combined word embedding into the highway network to adaptively carry each embedding to the input of the Bi-LSTM model. Two English biomedical benchmark datasets were employed in the present research to evaluate the level of performance. The proposed model resulted in higher f1-score compared to other previously studied models. The results demonstrate the effectiveness of the proposed methods in biomedical named entity recognition study.
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