Gpu databases

A presentation for GPU databases by Mahmoud Eskandari.
Ershad Damavand University, Tehran.

1. An introduction to the GPU databases By Mahmoud eskandari Ershad Damavand university – April 2019

2. What is A GPU ? A graphics processing unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. GPUs are used in embedded systems, mobile phones,  personal computers, workstations, and game consoles. [1]

3. THE Difference between A CPU & gpu CPU GPU

4. A CPU consists of a few cores (up to 24) optimized for sequential serial processing. It is designed to maximize the performance of a single task within a job; however, the range of tasks is wide. On the other hand, a GPU uses thousands of smaller and more efficient cores for a massively parallel architecture aimed at handling multiple functions at the same time. A CPU can work on a variety of different calculations, while a GPU is best at focusing all the computing abilities on a specific task.

5. What is A GPGPU ? General-purpose computing on graphics processing units  is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). [1] OpenCL is the dominant open general-purpose GPU computing language that is a GPGPU implementation.

6. GPU Server

7. What are gpu databases? A GPU database is a database, relational or non-relational, that uses a GPU (graphical processing unit) to perform some database operations. For example, GPU databases are typically fast, and geared towards analytics. The use of high-throughput devices like NVIDIA Tesla GPUs mean that most GPU databases are more flexible in processing many different types of data, or much larger amounts of data.

8. A list of famous gpu databases Kinetica SqreamDB BlazingDB IBM DB2 PG-Storm Omnisci (MAPD) Uber AresDB

9. Why We call it “Database”?

10. How are GPU Databases Different? With thousands of processing cores available on a single card, it is possible to perform operations in parallel, using brute force to solve complex analytics operations that traditional databases struggle with. Aggregations, sorts, and grouping operations are workload intensive for a CPU, but can work effectively in parallel on a GPU. NVIDIA’s CUDA API made it possible to use these GPU cards for high performance computing on standard hardware.

11. Usages Fast data processing Stream data analytics Extreme data analytics Graph processing

12. Gpu database Types In-memory For small data-sets Fast response Non-in-memory for very large data-sets > 10TB Relational SQL databases Non-relational Can be graph databases or other NoSQL databases For using graph analytics

13. References   Fung, et al., "Mediated Reality Using Computer Graphics Hardware for Computer Vision"   Archived  2 April 2012 at the  Wayback Machine , Proceedings of the International Symposium on Wearable Computing 2002 (ISWC2002), Seattle, Washington, USA, 7–10 October 2002, pp. 83–89. https://sqream.com/different-gpu-databases/