Green manure is a biological fertilizer source with complete nutrients, which provides reference for the selection of green manure resistant to low temperature germination. Six green manure crops including Brassica napus L., Orychophragmus violaceus (L.) O. E. Schulz, Medicago sativa L., Lolium perenne L., Melilotus officinalis (Linn.) Pall. and Vicia villosaRoth were selected as experimental materials to study the effects of low temperature stress on germination and seedling growth of different green manure through low temperature germination experiments. The results showed that there were significant differences in cold tolerance of the seeds of 6 medium green manure crops during germination. The germination potential, germination rate, germination index, vitality index, seedling height, root length, seedling fresh weight, soluble protein content and chlorophyll content of Brassica napus L. and Vicia villosaRoth seeds were all better than those of other green manure crops, which provided theoretical basis for the actual production of green manure.

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GEMM with the small size of input matrices is becoming widely used in many fields like HPC and machine learning. Although many famous BLAS libraries already supported small GEMM, they cannot achieve near-optimal performance. This is because the costs of pack operations are high and frequent boundary processing cannot be neglected. This paper proposes an input-aware adaptive tuning framework(IAAT) for small GEMM to overcome the performance bottlenecks in stateof-the-art implementations. IAAT consists of two stages, the install-time stage and the run-time stage. In the run-time stage, IAAT tiles matrices into blocks to alleviate boundary processing. This stage utilizes an input-aware adaptive tile algorithm and plays the role of runtime tuning. In the install-time stage, IAAT auto-generates hundreds of kernels of different sizes to remove pack operations. Finally, IAAT finishes the computation of small GEMM by invoking different kernels, which corresponds to the size of blocks. The experimental results show that IAAT gains better performance than other BLAS libraries on ARMv8 platform.