PostgreSQL with OpenCL
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PgOpenCL is a new PostgreSQL procedural language that allows developers to write OpenCL kernels to harness the parallel processing power of GPUs. It introduces a new execution model where tables can be copied to arrays, passed to an OpenCL kernel for parallel operations on the GPU, and results copied back to tables. This unlock the potential for dramatically improved performance on compute-intensive database operations like joins, aggregations, and sorting.
![PGOpenCL
Function Declaration
CREATE or REPLACE FUNCTION VectorAdd(IN a float[], IN B float[], OUT c float[])
AS $BODY$
#pragma PGOPENCL Platform : ATI Stream
#pragma PGOPENCL Device : CPU
__kernel __attribute__((reqd_work_group_size(64, 1, 1)))
void VectorAdd( __global const float *a, __global const float *b, __global float *c)
{
int i = get_global_id(0);
c[i] = a[i] + b[i];
}
$BODY$
Language PgOpenCL;](https://image.slidesharecdn.com/pgopencl-120108014658-phpapp02/85/PostgreSQL-with-OpenCL-18-320.jpg)
PostgreSQL with OpenCL
- 1. Introducing PgOpenCL A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child
- 2. Bio Tim Child • 35 years experience of software development • Formerly • VP Oracle Corporation • VP BEA Systems Inc. • VP Informix • Leader at Illustra, Autodesk, Navteq, Intuit, … • 30+ years experience in 3D, CAD, GIS and DBMS
- 3. Terminology Term Description Procedure Language Language for SQL Procedures (e.g. PgPLSQL, Perl, TCL, Java, … ) GPU Graphics Processing Unit (highly specialized CPU for graphics) GPGPU General Purpose GPU (non-graphics programming on a GPU) CUDA Nvidia’s GPU programming environment APU Accelerated Processing Unit (AMD’s Hybrid CPU & GPU chip) ISO C99 Modern standard version of the C language OpenCL Open Compute Language OpenMP Open Multi-Processing (parallelizing compilers) SIMD Single Instruction Multiple Data (Vector instructions ) SSE x86, x64 (Intel, AMD) Streaming SIMD Extensions xPU Any Processing Unit device (CPU, GPU, APU) Kernel Functions that execute on a OpenCL Device Work Item Instance of a Kernel Workgroup A group of Work Items FLOP Floating Point Operation (single = SQL real type ) MIC Many Integrated Cores (Intel’s 50+ x86 Core chip architecture)
- 4. Some Technology Trends Impacting DBMS • Solid State Storage – Reduced Access Time, Lower Power, Increasing in capacity • Virtualization – Server consolidation, Specialized VM’s, lowers direct costs • Cloud Computing – EC2, Azure, … lowers capital requirements • Multi-Core – 2,4,6,8, 12, …. Lots of benefits to multi-threaded applications • xPU (GPU/APU) – GPU >1000 Cores – > 1T FLOP /s @ €2500 – APU = CPU + GPU Chip Hybrids due in Mid 2011 – 2 T FLOP /s for $2.10 per hour (AWS EC2) – Intel MIC “Knights Corner “ > 50 x86 Cores
- 5. Compute Intensive xPU Database Applications • Bioinformatics • Signal/Audio/Image Processing/Video • Data Mining & Analytics • Searching • Sorting • Spatial Selections and Joins • Map/Reduce • Scientific Computing • Many Others …
- 6. GPU vs CPU Vendor NVidia ATI Radeon Intel Architecture Fermi Evergreen Nehalem Cores 448 1600 4 Simple Simple Complex Transistors 3.1 B 2.15 B 731 M Clock 1.5 G Hz 851 M Hz 3 G Hz Peak Float 1500 G 2720 G 96 G Performance FLOP / s FLOP / s FLOP / s Peak Double 750 G 544 G 48 G Performance FLOP / s FLOP / s FLOP / s Memory ~ 190 G / s ~ 153 G / s ~ 30 G / s Bandwidth Power 250 W > 250 W 80 W Consumption SIMD / Vector Many Many SSE4+ Instructions
- 8. Future (Mid 2011) APU Based PC APU (Accelerated Processing Unit) APU Chip CPU CPU ~20 GB/s System RAM North Bridge ~20 GB/s APU’s PCIE ~12 GB/s PCIE ~12 GB/s Adds an Embedded Embedded GPU GPU Discrete 150 GB/s Graphic RAM GPU Source AMD
- 9. Scalar vs. SIMD Scalar Instruction C=A+B 1 + 2 = 3 SIMD Instruction 1 3 5 7 + Vector C = Vector A + Vector B 2 4 6 8 = 3 7 11 15 OpenCL Vector lengths 2,4,8,16 for char, short, int, float, double
- 10. Summarizing xPU Trends • Many more xPU Cores in our Future • Compute Environment becoming Hybrid – CPU and GPU’s – Need CPU to give access to GPU power • GPU Capabilities – Lots of cores – Vector/SIMD Instructions – Fast Memory • GPU Futures – Virtual Memory – Multi-tasking / Pre-emption
- 11. Scaling PostgreSQL Queries on xPU’s Multi-Core CPU Many Core GPU PgOpenCL PgOpenCL PgOpenCL PgOpenCL PgOpenCL PgOpenCL PgOpenCL PgOpenCL PgOpenCL Threads Threads Threads Threads Threads Threads Threads Thread Thread PgOpenCL PgOpenCL PgOpenCL PgOpenCL PgOpenCL Postgres Threads Threads Threads Thread Thread Process PgOpenCL PgOpenCL PgOpenCL PgOpenCL PgOpenCL Threads Threads Thread Thread Threads Using More Transistors
- 12. Parallel Programming Systems Category CUDA OpenMP OpenCL Language C C, Fortran C Cross Platform X √ √ Standard Vendor OpenMP Khronos CPU X √ √ GPU √ X √ Clusters X √ X Compilation / Link Static Static Dynamic
- 13. What is OpenCL? • OpenCL - Open Compute Language – Subset of C 99 – Open Specification – Proposed by Apple – Many Companies Collaborated on the Specification – Portable, Device Agnostic – Specification maintained by Khronos Group • PgOpenCL – OpenCL as a PostgreSQL Procedural Language
- 14. System Overview DBMS Server PgOpenCL PgOpenCL Web HTTP Web SQL SQL SQL Browser Server Statement Procedure Procedure PCIe X2 Bus TCP/IP App PostgreSQL GPGPU Server Disk I/O Tables TCP/IP PostgreSQL Client
- 15. OpenCL Language • A subset of ISO C99 – - But without some C99 features such as standard C99 headers, – function pointers, recursion, variable length arrays, and bit fields • A superset of ISO C99 with additions for: – - Work-items and Workgroups – - Vector types – - Synchronization – - Address space qualifiers • Also includes a large set of built-in functions – - Image manipulation – - Work-item manipulation, – - Specialized math routines, etc.
- 16. PgOpenCL Components • New PostgreSQL Procedural Language – Language handler • Maps arguments • Calls function • Returns results – Language validator • Creates Function with parameter & syntax checking • Compiles Function to a Binary format • New data types – cl_double4, cl_double8, …. • System Admin Pseudo-Tables – Platform, Device, Run-Time, …
- 17. PgOpenCL Admin
- 18. PGOpenCL Function Declaration CREATE or REPLACE FUNCTION VectorAdd(IN a float[], IN B float[], OUT c float[]) AS $BODY$ #pragma PGOPENCL Platform : ATI Stream #pragma PGOPENCL Device : CPU __kernel __attribute__((reqd_work_group_size(64, 1, 1))) void VectorAdd( __global const float *a, __global const float *b, __global float *c) { int i = get_global_id(0); c[i] = a[i] + b[i]; } $BODY$ Language PgOpenCL;
- 19. PgOpenCL Execution Model A Table B Select Table 100’s - 1000’s of to Array Threads (Kernels) xPU VectorAdd(A, B) A + B Returns C = C Copy Unnest Array Copy To Table Table C C C C C C C C C C C C C
- 20. Using Re-Shaped Tables 100’s - 1000’s of Table of Threads (Kernels) Table of Arrays Arrays A + B = C A C C C C B xPU VectorAdd(A, B) Returns C A C C C C B Copy Copy
- 21. Today’s GPGPU Challenges • No Pre-emptive Multi-Tasking • No Virtual Memory • Limited Bandwidth to discrete GPGPU – 1 – 8 G/s over PCIe Bus • Hard to Program – New Parallel Algorithms and constructs – “New” C language dialect • Immature Tools – Compilers, IDE, Debuggers, Profilers - early years • Data organization really matters – Types, Structure, and Alignment – SQL needs to Shape the Data • Profiling and Debugging is not easy Solves Well for Problem Sets with the Right Shape!
- 22. Making a Problem Work for You • Determine % Parallelism Possible for ( i = 0, i < ∞, i++) for ( j = 0; j < ∞; j++ ) for ( k = 0; k < ∞; k++ ) • Arrange data to fit available GPU RAM • Ensure calculation time >> I/O transfer overhead • Learn about Parallel Algorithms and the OpenCL language • Learn new tools • Carefully choose Data Types, Organization and Alignments • Profile and Measure at Every Stage
- 23. PgOpenCL System Requirements • PostgreSQL 9.x • For GPU’s – AMD ATI OpenCL Stream SDK 2.x – NVidia CUDA 3.x SDK – Recent Macs with O/S 11.6 • For CPU’s (Pentium M or more recent) – AMD ATI OpenCL Stream SDK 2.x – Intel OpenCL SDK Alpha Release (x86) – Recent Macs with O/S 11.6
- 24. PGOpenCL Status Today 1Q 2011 Prototype Beta 2010 2011 • Wish List • Beta Testers – Existing OpenCL App? – Have a GPU App? • Contributors – Code server side functions? • Sponsors & Supporters – AMD Fusion Fund? – Khronos?
- 25. PgOpenCL Future Plans • Increase Platform Support • Scatter/Gather Functions • Additional Type Support – Image Types – Sparse Matrices • Run-Time – Asynchronous – Events – Profiling – Debugging
- 26. Using the Whole Brain APU Chip PgOpenCl PgOpenCl PgOpenCL PgOpenCL CPU CPU CPU Postgres You can’t be in a parallel universe with a single brain! North Bridge ~20 GB/s • Heterogeneous Compute Environments PgOpenCl PgOpenCl • CPU’s, GPU’s, APU’s Embedded PgOpenCl • Expect 100’s – 1000’s of cores PgOpenCl GPU PgOpenCL The Future Is Parallel: What's a Programmer to Do?
- 27. Summarizing PgOpenCL • Supports Heterogeneous Parallel Compute Environments • CPU’s, GPU’s, APU’s • OpenCL • Portable and high-performance framework –Ideal for computationally intensive algorithms –Access to all compute resources (CPU, APU, GPU) –Well-defined computation/memory model •Efficient parallel programming language –C99 with extensions for task and data parallelism –Rich set of built-in functions •Open standard for heterogeneous parallel computing • PgOpenCL • Integrates PostgreSQL with OpenCL • Provides Easy SQL Access to xPU’s • APU, CPU, GPGPU • Integrates OpenCL • SQL + Web Apps(PHP, Ruby, … )
- 28. More Information • PGOpenCL • Twitter @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 • http://developer.apple.com/technologies/mac/snowleopard/opencl.html
- 29. Q&A • Using Parallel Applications? • Benefits of OpenCL / PgOpenCL? • Want to Collaborate on PgOpenCL?