One of the difficult requirements imposed on high-quality CFD mesh generation has been the ability to evaluate the mesh quality efficiently. Due to the lack of a general and effective evaluating criterion, the current mesh quality evaluation task mainly relies on various quality metrics for the shape of mesh elements, such as angle, radius, edge and contextual information collected by pre-processing software. However, this line of methods greatly increases the pre-processing cost and may not guarantee a precise quality result. In this paper, we provide a solution to solve the mentioned issues, resulting in a CNN model GridNet and the first mesh dataset NACA-Market. GridNet takes the mesh file as input and then automatically evaluates the mesh quality. Experiment results show that Grid-Net is capable of performing automatic mesh quality evaluation and outperforms the widely used classifiers. We hope that the proposed large benchmark collection and network could fill in the gaps in the fields of CNN-based mesh quality evaluation and provide potential future research directions in this field.
Summary
Sparse matrix‐vector multiplication (SpMV) is an essential kernel in sparse linear algebra and has been studied extensively on all modern processor and accelerator architectures. Compressed Sparse Row (CSR) is a frequently used format for sparse matrices storage. However, CSR‐based SpMV has poor performance on processors with vector units. In order to take full advantage of SIMD acceleration technology in SpMV, we proposed a new matrix storage format called CSR‐SIMD. The new storage format compresses the non‐zero elements into many variable‐length data fragments with consecutive memory access addresses. Thus, the data locality of sparse matrix A and dense vector x expands and the floating‐point operations for each fragment can be completely calculated by vectorized implementation on wide SIMD units. Our experimental results indicate that CSR‐SIMD has better storage efficiency and low‐overhead for format conversion. Besides, the new format achieves high scalability on wide SIMD units. In comparison with the CSR‐based and BCSR‐based SpMV, CSR‐SIMD obtains better performance on FT1500A, Intel Xeon, and Intel Xeon Phi.
Protein secondary structure prediction is very important for its molecular structure. GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the running time is unbearable with sharp growth in protein database. Fortunately, CUDA (Compute Unified Device Architecture) provides a promising approach to accelerate secondary structure prediction. Therefore, we propose a fine-grained parallel implementation to parallelize GOR-IV package for accelerating protein secondary structure prediction, in which each amino acid would be assigned to one single CUDA thread, hence protein secondary structure prediction would be parallelized by many CUDA threads simultaneously, and constant cache is resorted to cache parameter table. Experimental results show a speedup factor is more than 173X over original GOR-IV version.
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