This document discusses using OpenCL to accelerate database operations by offloading computation to GPUs. It presents PostgreSQL as an example database that can be extended with OpenCL using PgOpenCL. This allows SQL queries to launch OpenCL kernels on GPUs and retrieve results. Challenges include data transfer between CPU and GPU, runtime partitioning for large datasets, and concurrency without preemptive multitasking. Example use cases include spatial selections in GIS, DNA/protein matching, and accelerating database internal operations like joins and sorting.

1. Database Driven
OpenCL Programming
HPC & GPU
Supercomputing Group
of Silicon Valley
by
Tim Child

2. OpenCL Data I/O Problem
• Write a OpenCL Applications that :-
– Read data from DBMS or File
– Publishes Results as Web Pages
• Possible Solutions
– C/C++ Binding using Web CGI
– Java/Perl/Python Bindings in App Server
– ???
or
Database Driven
OpenCL

3. PostgeSQL
Swiss Army Knife of Databases
Extensible
Types
Extensible
Indices
Extensible
Procedural
Languages
(Java, Perl, …)
Rules
System
SQL
(Declarative Language,
Set Operations)
Remote
Open
Source
Native
API’s
Vibrant
Community
OpenCLPostGIS

4. Database Driven OpenCL
PostgreSQL
Shared
Memory
Cache
O/S
I/O Buffers
Disk
Storage
IO
OpenCL
Kernels
Web
Page
SQL Query
Results Set
Two Levels of Data Caching
Web Client
Application
HTTP Request ►
HTTP Response ◄
Log
Parameterized
Queries ►
Results Sets ◄
PostgreSQL
Sever
Process
PostgreSQL
Sever
Process
IO
Memory Access
Row
Row
Row
Row
Row
Row
Row
Row
Row
Row
Row
Row
Row
Row
Row
Row

5. OpenCL SQL Binding
CREATE or REPLACE FUNCTION VectorAdd(IN Id int[], IN a real[], IN B real[], OUT C
real[] )
AS $BODY$
$BODY$
Language PgOpenCL;
__kernel void VectorAdd( __global int * id,
__global float *a,
__global float *b,
__global float *c)
{
int i = get_global_id(0); /* Query OpenCL for the Array Subscript **/
c[i] = a[i] + b[i];
}

6. Database Driven OpenCL
A
B
A B C
Select Table
to Array
Unnest Array
To Table
Copy
Copy
100’s - 1000’s of
Threads (Kernels)
Table
Table
VectorAdd(A, B)
Returns C+ =
xPU
C C C C C C CC C C C C C

7. Challenges
• Type Mapping
– Extend SQL Types with
• OpenCL Vectors Types
• OpenCL Image Types
• Setup –> Runtime
• Caching kernel info
• Data Transfer
– CPU ↔ GPU
• Still present
– SQL Queries
• + ∆ Overhead
• Map – Increase
– Bulk Data Loaders
• New Task
• Problem Size
– DBMS Table Size >> GPU RAM
– # Work Groups / # Work Items
• Runtime Partitioning
• Device Management
– CPU vs. GPU
• Runtime Selection
• Concurrency
– No Pre-emptive Multi-Tasking
• Time-out Long Queries
• Partitioning / Scheduling
X
√
√
X
√
X
X
X
√
X
√

8. Some Example Uses Cases
• 3D Content Management / GIS
– Spatial Selections
– Coordinate Transformations
– Image Processing
• Bioinformatics
– DNA & Protein Sequence Matching
• Database Internal Operations
– Joins
– Sorting
– Query Planning

9. Benefits of the Approach
Database
Internal
Operations
Open
Source
Release
OpenCL
PostgreSQL

10. Questions?
More Information
• PgOpenCL
• Twitter @3DMashUp
• Blog www.scribd.com/3dmashup
• OpenCL
• www.khronos.org/opencl/
• www.amd.com/us/products/technologies/stream-technology/opencl/
• http://software.intel.com/en-us/articles/intel-opencl-sdk
• http://www.nvidia.com/object/cuda_opencl_new.html