and Parallel Algorithms Programming with CUDA, WS09 Waqar Saleem, Jens Müller

Transcription

1 Programming with CUDA and Parallel Algorithms Waqar Saleem Jens Müller

2 Organization People Waqar Saleem, Jens Mueller, Room 3335, Ernst-Abbe-Platz 2 The course will be conducted in English 6 points Wahl/Wahlpflicht Theoretical/Practical

3 Organization Meetings, before winter break Tue 12-14, CZ 129 Thu 16-18, CZ 129 Every second week Starting next week Exercises: Wed 8-10, CZ 125 Starting tomorrow in the pool

4 The course 2 parts Before winter break: Lectures and assignments Need at least 50% in assignments to qualify for... After the break: Group projects Project chosen by or assigned to each group Regular meetings Presentation of each project on semester end

5 Assignments Build up a minimal ray tracer on GPU Implement basic ray tracer on CPU Port to GPU Make ray tracer more interesting/efficient Utilize CUDA concepts Basic framework will be provided Scene format and scenes Introduction to ray tracing concepts

6 Requirements Strong background in C programming Familiarity with your OS Modifying default settings Writing/understanding Makefiles Compiler flags and options

7 Course content Parallel programming models and platforms GPGPU GPGPU on NVIDIA cards: CUDA Architecture and programming model OpenCL

8 Today Organization Brief introduction to parallel programming and CUDA Short introduction to Ray tracing

9 Growth of Compute Capability Moore s law: the number of transistors that can be placed... on an integrated circuit [doubles] approximately every two years source: wikipedia

10 Growth of Compute Capability Moore s law source: wikipedia

11 Need for increasing compute capability Problems are getting more complex e.g. Text editing to Image editing to Video editing Current hardware complexity is never enough Impractical to stop development at current state of the art

12 Barriers to growth Natural limit on transistor size: the size of an atom More transistors per unit area lead to higher power consumption and heat dissipation

13 Solution: Parallel architectures

14 Parallel architectures Multiple Instructions Multiple Data (MIMD) multi-threaded, multi-core architectures, clusters, grids Single Instruction Multiple Data (SIMD) Cell processor, GPUs, clusters, grids GPU: Graphics Processing Unit Parallel programming allows to program for parallel architectures

15 GPU architecture Simpler architecture than MIMD Little overhead for instruction scheduling, branch prediction etc. Subsequent figures from NVIDIA CUDA Programming Guide unless mentioned otherwise

16 GPU architecture Simpler architecture leads to higher performance (compared to CPUs)

17 General Purpose computing on GPU, GPGPU Attractive because of raw GPU power Traditionally hard because GPU programming was closely associated to graphics Simplicity of GPU architecture limits the kind of problems suitable for GPGPU or at least requires some problems to be reformulated

18 GPGPU for the masses* Freeing the GPU from graphics: Nvidia CUDA, ATI Stream C-like programming interface to the GPU * - knowledge of underlying architecture required to achieve peak performance

19 Freeing Parallel Programming OpenCL: code once, run anywhere single core, multi core, GPU,... platform details transparent to the user supported by major vendors: Apple, Intel, AMD, Nvidia,... OpenCL drivers made available by ATI and Nvidia for their cards

20 This course chiefly CUDA: Nvidia specific, mature, well documented, easily available literature some OpenCL: open standard, very new, limited documentation available, very similar concepts to CUDA no ATI Stream

21 CUDA, Compute Unified Device Architecture Software: C like programming interface to the GPU Hardware: the hardware that supports the above programming model

22 CUDA hardware model

23 CUDA programming model CPU=host, GPU=device, work unit=thread

24

25 Ray tracing A method to render a given scene Cast rays from a camera into the scene Compute ray intersections with scene geometry Render pixel image source: wikipedia

26 Ray tracer complexity A ray tracer can be arbitrarily complex Recursively compute intersections for reflected, refracted and shadow rays Account for diffuse lighting Consider multiple light sources Consider light sources other than point lights Account for textures: object materials

27 Coding a ray tracer Relatively easy to code on the CPU Call the same intersection function recursively on secondary rays CPU code is not so complex Tricky to code on the GPU as recursion is not yet supported in GPGPU models

28 This course Build a trivial ray tracer on the CPU compute view rays only part of tomorrow s exercise Port to GPU Add complexity to your GPU ray tracer

29 Reminders Exercise session tomorrow Register on CAJ

30 See you next time!