A Block-Based Union-Find Algorithm to Label Connected Components on GPUs

In this paper, we introduce a novel GPU-based Connected Components Labeling algorithm: the Block-based Union Find. The proposed strategy significantly improves an existing GPU algorithm, taking advantage of a block-based approach. Experimental results on real cases and synthetically generated datasets demonstrate the superiority of the new proposal with respect to state-of-the-art

Preferensi dan Keputusan Petani terhadap Pilihan Varietas Unggul Ubijalar di Lahan Pasang Surut

Penelitian dilakukan di Desa  Sidomulyo dan , Barito Koala pada tahun 2016. Tujuan penelitan untuk mengidentifikasi karakteristik tanaman ubijalar yang disenangi dan mengetahui faktor-faktor yang mempengaruhi keputusan petani dalam memilih varietas unggul ubijalar. Pengambilan sampel dilakukan secara “purposive sampling” di ke dua lokasi penelitian. Masing-masing sebanyak 30 (tiga puluh) responden, sehingga total responden sebanyak 60 responden. Analisis data yang digunakan terdiri atas analisis tabulasi, dan analisis komponen utama (principal component analysis). Hasil penelitian menunjukkan bahwa usahatani ubijalar mempunyai keuntungan yang tinggi.Petani responden Desa Sidomulyo menyatakan suka warna ubijalar varietas Kidal (100%/V2), namun pilihan petani (100%) sangat suka pada varietas V3 (Ayamurasakhi). Sedangkan petani responden Desa Simpangjaya suka warna kulit ubijalar pada varietas Shiroyutaka (V1), V2 (varietas Kidal), dan V3 (varietas Ayamurasaki), masing-masing 73,33%, 60%, dan 46,67%. Dalam menyikapi pemilihan varietas unggu ubijalar, petani dipengarugi beberapa faktor. Faktor utama atau yang sangat dominan dalam pemilihan varietas unggul sebagai prferensi adalah mudah memperoleh bibit, mempunyai daya tumbuh tinggi, produksi tinggi, mudah pemasarannya, warna kulit ubi dan warna daging. Faktor yang dominan sebagai faktor pelengkap adalah pengendalian hama penyakit, umur panen yang genjah, tekstur daging ubi yang sedikit berair dan jumlah ubi yang dihasilka

Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution