The ever-increasing demand for high performance Big Data analytics and data processing,
has paved the way for heterogeneous hardware accelerators, such as Graphics Processing …

Due to its simplicity and scalability, MapReduce has become a de facto standard computing
model for big data processing. Since the original MapReduce model was only appropriate …

曹嵘晖, 唐卓, 左知微, 等. 面向机器学习的分布式并行计算关键技术及应用[J]. 智能系统学报,
2021, 16 (5): 919-930. DOI: 10.11992/tis. 202108010.CAO Ronghui, TANG Zhuo, ZUO …

Machine learning (ML) algorithms have garnered increased interest as they demonstrate
improved ability to extract meaningful trends from large, diverse, and noisy data sets. While …

With the growing need of big-data processing in diverse application domains, MapReduce
(eg, Hadoop) has become one of the standard computing paradigms for large-scale …

In this article, we propose new extensions to Hadoop to enable clusters of reconfigurable
active solid-state drives (RASSDs) to process streaming data from SSDs using FPGAs. We …

Recently, many large-scale data-intensive applications have emerged from the Internet and
science domains. They pose significant challenges on the performance, scalability and …

摘要针对智能配用电数据具有海量, 多源异构等特点, 提出一种基于Hadoop
的智能配用电数据存储处理的框架设计方案. 在对智能配用电数据组成进行梳理的基础上 …

With the advent of advances in imaging and computing technologies, large-scale data
acquisition and processing have become commonplace in many science and engineering …

Cluster analysis, such as k-means algorithm, plays a critical role in data mining area, but
now it is facing the computational challenge due to the continuously increasing data volume …