Graphics processing unit or GPU (also occasionally called visual processing unit or VPU) is a specialized microprocessor that offloads and accelerates graphics rendering from the central (micro) processor. Modern GPUs are very efficient at manipulating computer graphics, and their highly parallel structure makes them more effective than general-purpose CPUs for a range of complex algorithms. In CPU, only a fraction of the chip does computations where as the GPU devotes more transistors to data processing. GPGPU is a programming methodology based on modifying algorithms to run on existing GPU hardware for increased performance. Unfortunately, GPGPU programming is significantly more complex than traditional programming for several reasons.

1. GPU programming with Java
Pramuditha Aravinda.
Kelum Senanayake.

2. Outline
 What is GPU.
 CPU vs. GPU Architecture.
 What is Stream processing.
 General Purpose GPU.
 CUDA.
 OpenCL
 Demo

3. What is GPU
 Graphics processing unit.
 Specialized microprocessor.
 Very efficient at manipulating computer graphics.
 Offloads and accelerates graphics rendering from the
CPU.
 Dedicated to calculating floating point operations.
 Highly parallel structure.
 More effective for a range of complex algorithms.
 GPU can be present on,
 Video card.
 Motherboard
 CPU die (certain Core Intel CPUs)

4. CPU vs. GPU Architecture
The GPU devotes more transistors to data processing.
GPU : A Highly Parallel, Multithreaded, Manycore Processor

5. CPU vs. GPU contd…

6. What is Stream processing
 Is a computer programming paradigm, related to SIMD.
 Allows applications to easily exploit a limited form of
parallel processing.
 Terminology
 Stream :- A set of data
 Kernel functions :- A series of operations
 Uniform streaming :- One kernel function is applied to all
elements in the stream.
 Stream processing is driven by a data-centric model
 Image, video and digital signal processing
 Less efficient in general purpose processing with more
randomized data access (such as databases)

7. General Purpose GPU
 The GPU is, by design, a stream processing system.
 GPGPU is a programming methodology.
 Modifying algorithms to run on existing GPU hardware
 Capable of performing simple operations on a stream of
input data with amazing speed.
 Allows software developers to use stream processing on
non-graphics data.

8. How hard is it?
 The languages are not very easy to use. Most GPU cards
still operate on assembly language.
 The process flow is unique. Typically simple branching
statement, such as if statements, offer such a performance
penalty that it is often faster to process both conditions.
 The unique Stream-In-Stream-Out design is not typically
used in CPU programs.
 The need to work with geometric primitives in order to
push mathematical inputs to the system.
 Rapidly growing community.

9. Programming GPUs!
 Plenty of interfaces
 Compute Unified Device Architecture (CUDA)
 OpenCL
 OpenGL Shader Language (GLSL)
 DirectX/DirectCompute/HLSL
 ATI Stream

10. CUDA
 Compute Unified Device Architecture.
 Parallel computing architecture developed by NVIDIA.
 Programmers use C for CUDA.
 C with NVIDIA extensions and certain restrictions.
 Third party wrappers are also available for Python, Perl,
Fortran, Java, Ruby, Lua, MATLAB and IDL.
 Currently used in,
 SETI@Home
 Distributed Calculations, such as predicting the native
conformation of proteins
 Accelerated inter conversion of video file formats
 Physical simulations, in particular in fluid dynamics

11. CUDA Processing Flow

12. OpenCL
 Open Computing Language.
 Managed by the non-profit technology consortium
Khronos Group
 Framework for writing programs that execute across
heterogeneous platforms consisting of CPUs, GPUs, and
other processors.
 Includes a language (based on C99) for writing kernels.
 APIs to define and then control the platforms.
 Supports both AMD/ATI and NVIDIA.

13. Programming GPU with Java and
OpenCL
 We need to program using a GPGPU driver
 However most GPGPU drivers are available as a native dll
only.
 We need another layer to interface to Java runtime.
 Called Java Binding

14. Java Binding
UserProgram.class
Jocl.jar
Jocl.dll
OpenCL.dll

15. Prerequisites - Hardware
 OpenCL capable graphic card.
 nVidea – All CUDA enabled GPU have OpenCL support.
 GeForce 8xxx or higher with 256MB minimum.
 http://www.nvidia.com/object/cuda_gpus.html
 AMD ATI Radeon™ HD 5400 or higher, AMD Radeon™
HD 6800 series or higher.
 AMD X86 CPU w/ SSE 2.x or later are also supported.
 http://developer.amd.com/gpu/AMDAPPSDK/pages/DriverCom
patibility.aspx

16. Prerequisites - Software
 OpenCL driver. – for nVidia GPUs, Usually OpenCL
drivers are distributed with graphic card drivers.
 http://developer.nvidia.com/object/opencl-download.html
 Java bindings for OpenCL. – Usually there are two parts
 Platform dependent dll. e.g. jocl-windows-x86.dll
 Platform independent jar file. Jocl.jar
 There are few Implementations.
http://jogamp.org/deployment/webstart/archive/jocl-0.9-b1-
20101213-windows-i586.zip
 JDK
 http://www.oracle.com/technetwork/java/javase/downloads/ind
ex.html

17. Demo Program
 Based on sample program available at
http://jogamp.org/wiki/index.php/JOCL_Tutorial