MapReduce [3, 4] is a programming model for implementation of distributed computations of high volume or big data and an execution framework for large-scale data processing on clusters of servers.
From that necessity ERMA was born, one of the first large scale data processing machines for business.
MapReduce-like frameworks have achieved tremendous success for large-scale data processing in data centers.
Large-scale data processing with phred, SeqClean, cross_match, PartiGene and BLAST programs was carried out at command line level on a Linux platform, while analysis with LUCY2 was performed on a Windows XP platform.
For instance, each large-scale data processing system (e.g., relational databases, Spark, TensorFlow, etc) specializes on different workloads and exposes different domain-specific language or programming interface for applications to utilize.
To support large-scale data processing, we design a multi-factor real-time monitoring fault tolerance (MRMFT) model based on a GPU cluster.
It also includes the development of large-scale data processing software systems.
Large-scale data processing applications often coordinate effort among multiple computers.
Within the past few years, organizations in diverse industries have adopted MapReduce-based systems for large-scale data processing.
CEO and President, A.R.O., Inc. Andy Hickl is an innovator in machine learning, large-scale data processing, ubiquitous computing, and natural language processing.
These considerations led us to use cloud computing to meet large-scale data processing requirements.
