Apache Hadoop is a framework for storing large Data in distributed mode and distributed processing on that large datasets. It scales from a single server to thousands of servers. Hadoop detects the failures at the application layer and handles that failure. Hadoop 3.0 is major release after Hadoop 2 with new features like HDFS erasure coding, improves the performance and scalability, multiple NameNodes and many more.
A platform or framework in which Big Data is stored in Distributed Environment and processing of this data is done parallelly. Click to explore about, Apache Hadoop on Kubernetes
How Hadoop with GPU Works?
GPU's are becoming a vital tool for many Big Data apps. There are many apps which rely on GPU Deep Learning, Machine Learning, Data Analytics, Genome Sequencing, etc. In many cases, GPU's speed up to 10x and its speed increases by 300x. While starting Apache Hadoop 3.0, there is support for operators as well as the admins to configure YARN clusters and to schedule, use GPU resources. Without native and more comprehensive applications require GPU support, there is no isolation of GPU resource also.
Recognize GPU as a resource type while doing scheduling. With GPU scheduling support, containers with GPU request placed to machines or local with enough available GPU resources. To solve the isolation problem, GPU uses multiple machines at the same time without affecting each other. WEB UI of YARN includes GPU information. It shows the total used and available resources across the cluster among other resources like CPU & Memory.
What are the benefits of Hadoop with GPU?
The benefits of Hadoop with Graphics Processing Unit:
Minimum required Java version increased from Java 7 to Java 8.
Support for erasure encoding in HDFS.
YARN Timeline Service v.2.
Shell script rewrite.
Shaded client jars.
Support for Opportunistic Containers and Distributed Scheduling.
How to adopt Apache Hadoop with Graphics Processing Unit?
Adopt Hadoop with GPU by using Hadoop 3.0 Installation on the server and also add GPU on the Server. Hadoop runs on Unix/Linux based Operating Systems. However, it works with Windows-based machines, but it is not recommended. There are three different modes of Hadoop Installation -
Fully Distributed Mode.
Hadoop default mode.
HDFS not utilized.
The local file system used for input and output.
Used for debugging purpose.
No Configuration required in 3 Hadoop(mapred-site.xml,core-site.xml, hdfs-site.xml) files.
Faster than the Pseudo-distributed mode.
This configuration requires three files -
HDFS needed only one Replication factory.
In this one node used as Master Node / Data Node / Job Tracker / Task Tracker.
Real Code to test in HDFS.
Pseudo-distributed cluster to run all Daemons on one node itself.
Fully Distributed Mode
This mode is a Production Phase mode.
In this, data used and distributed across many nodes.
In this mode, different Nodes used as Master Node / Data Node / Job Tracker / Task Tracker.
It Introduced GPU into MapReduce cluster and obtained up to 20 times speedup and reduced up to 19/20 power consumption with the current preliminary solution and workload. It also compared with upgrading CPUs and adding more nodes, deploying GPU on Hadoop has the high cost-to-benefit ratio and provided practical implementations for people wanting to construct MapReduce clusters with GPUs. To know more about Hadoop with GPU, go through with below steps: