Dell PowerEdge R940xa GPU Database Acceleration on - Page 7

Machine Learning and Deep Learning?

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2.2 2.3 2.4 2.5 What database operations can run on GPU? GPUs achieve their amazing performance by running things in parallel and this means the underlying code must take this parallel way of doing things into account. It also means the algorithms used must be parallelizable, and in many cases parallelizing an operation is not trivial. Relational operations like filtering, sorting, aggregating, grouping and even joining tables are all possible on GPU. How do GPUs accelerate analytic workloads? What makes the GPU in-memory database different to a CPU in-memory database is how it manages storing and processing data to delivering peak performance. Data usually resides in CPU memory in vectorized columns to optimize parallel processing across all available GPUs. The data is moved as needed to GPU memory for both mathematical and spatial calculations, and the results then returned to CPU. For smaller data sets and live streams, the data can reside entirely in the GPU's for even faster processing. What are some examples? a) The U.S. Army's Intelligence & Security Command (INSCOM) unit replaced a cluster of 42 servers with a single server running a database purpose-built to leverage the GPU's power. The application required ingesting over 200 sources of streaming data that together produced some 200 billion records per day. b) The U.S. Postal Service uses a GPU-accelerated database to track over 200,000 devices that send location coordinates once per minute. In total, the application ingests and analyzes more than a quarterbillion such events in real time every day. c) A retail company was able to replace a 300-node database cluster with a 30-node GPU-accelerated database cluster. Even with only one-tenth the number of nodes, the new cluster delivers 100-200 times increase in performance for the company's top 10 most complicated queries. How can GPU database acceleration help other workloads like Machine Learning and Deep Learning? Artificial Intelligence is one of the fastest growing segments in the technology space. While only 4% of companies have AI in production right now, over 52% of companies are either starting AI projects or would like to learn more about how AI can be applied to their business. In 2016, the AI market was worth $644 million and the value of that market is expected to grow rapidly, reaching$38.6 billion by 2025. Artificial intelligence will transform the relationship between people and technology, accelerating our creativity and skills. 7 GPU Database Acceleration on PowerEdge R940xa

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7
GPU Database Acceleration on PowerEdge R940xa
2.2
What database operations can run on GPU?
GPUs achieve their amazing performance by running things in parallel and this means the underlying code
must take this parallel way of doing things into account. It also means the algorithms used must be
parallelizable, and in many cases parallelizing an operation is not trivial. Relational operations like filtering,
sorting, aggregating, grouping and even joining tables are all possible on GPU.
2.3
How do GPUs accelerate analytic workloads?
What makes the GPU in-memory database different to a CPU in-memory database is how it manages storing
and processing data to delivering peak performance. Data usually resides in CPU memory in vectorized
columns to optimize parallel processing across all available GPUs. The data is moved as needed to GPU
memory for both mathematical and spatial calculations, and the results then returned to CPU. For smaller
data sets and live streams, the data can reside entirely in the GPU’s for even faster processing.
2.4
What are some examples?
a)
The U.S. Army’s Intelligence & Security
Command (INSCOM) unit replaced a cluster of 42 servers with a
single server running a database purpose-
built to leverage the GPU’s power. The application required
ingesting over 200 sources of streaming data that together produced some 200 billion records per day.
b)
The U.S. Postal Service uses a GPU-accelerated database to track over 200,000 devices that send
location coordinates once per minute. In total, the application ingests and analyzes more than a quarter-
billion such events in real time every day.
c)
A retail company was able to replace a 300-node database cluster with a 30-node GPU-accelerated
database cluster. Even with only one-tenth the number of nodes, the new cluster delivers 100-200 times
increase in performance for the company’s top 10 most compl
icated queries.
2.5
How can GPU database acceleration help other workloads like
Machine Learning and Deep Learning?
Artificial Intelligence is one of the fastest growing segments in the technology space. While only 4% of
companies have AI in production right now, over 52% of companies are either starting AI projects or would
like to learn more about how AI can be applied to their business. In 2016, the AI market was worth $644
million and the value of that market is expected to grow rapidly, reaching$38.6 billion by 2025. Artificial
intelligence will transform the relationship between people and technology, accelerating our creativity and
skills.