Modern graphs are large, often containing billions of nodes and edges that demand huge amount of processing for analysis purposes. The algorithms processing these graphs often run for long time and consume substantial amount of energy. However, not all edges in the graphs are equally important. Some edges play critical role in maintaining the community and other interesting structures in the graph, while the rest are less important for analysis. Identifying edges as important and unimportant allows one to apply elastic fidelity computing when processing edges of low importance, hence saving significant amount of energy while processing large graphs. In this paper we propose a novel technique for identifying important edges in a graph using a fast method that exploits locality sensitive hashing. We then propose a framework for energy-efficient computing that applies elastic fidelity computing when processing edges of low importance and applies full fidelity computing when processing important edges. This allows the framework to deliver good results while saving energy when processing a large number of lowimportance edges. Our proposed technique reduces the power consumption by 3-30% while still producing results that are within acceptable range of the full-accuracy results.
Widespread use of stochastic flow based graph clustering algorithms, e.g. Markov Clustering (MCL), has been hampered by their lack of scalability and fragmentation of output. Multi-Level Regularized Markov Clustering (MLR-MCL) is an improvement over Markov Clustering (MCL), providing faster performance and better quality of clusters for large graphs. However, a closer look at MLR-MCL's performance reveals potential for further improvement. In this paper we present a fast parallel implementation of MLR-MCL algorithm via static work partitioning based on analysis of memory footprints. By parallelizing the most time consuming region of the sequential MLR-MCL algorithm, we report up to 10.43x (5.22x in average) speedup on CPU, using 8 datasets from SNAP and 3 PPI datasets. In addition, our algorithm can be adapted to perform general sparse matrix-matrix multiplication (SpGEMM), and our experimental evaluation shows up to 3.50x (1.92x in average) speedup on CPU, and up to 5.12x (2.20x in average) speedup on MIC, comparing to the SpGEMM kernel provided by Intel Math Kernel Library (MKL).
Kepuasan pelanggan merupakan salah satu tujuan dari perusahan dalam memberikan pelayanan kepada pelanggannya baik perusahaan jasa maupun non jasa. Salah satu perusahaan jasa penyedia sewa kamera yang berkomitmen untuk kepuasan pelanggannya adalah Sewa Kamera Cikarang. Penelitian ini bertujuan untuk menganalisa kepuasan pelanggan sewa kamera cikarang dengan menggunakan algoritma K Nearest Neighbour (KNN). Atribut masukan kepuasan pelanggan dalam penelitian ini mencangkup harga, fasilitas, pelayanan dam loyalitas. Keluaran hasil dari atribut masukan diatas adalah puas dan tidak puas. Penelitian ini diharapkan untuk membantu Sewa Kamera Cikarang untuk meningkatkan kepuasan pelanggan dan meningkatkan laba pada Sewa Kamera Cikarang. Hasil penelitian yang dapat dicapai menggunakan algoritma C 4.5 adalah acuracy = 95%, classification recall =100%, Classification precision = 92.31% dan AUC = 0.948. Hasil penelitian ini bisa menjadikan referensi untuk membangun sebuah aplikasi yang dapat memudahkan perusahaan dalam memperoleh informasi mengenai kepuasan pelanggannya.
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