Programming with CUDA and Parallel Algorithms Waqar Saleem Jens Müller
Organization People Waqar Saleem, waqar.saleem@uni-jena.de Jens Mueller, jkm@informatik.uni-jena.de Room 3335, Ernst-Abbe-Platz 2 The course will be conducted in English 6 points Wahl/Wahlpflicht Theoretical/Practical
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
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
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
Requirements Strong background in C programming Familiarity with your OS Modifying default settings Writing/understanding Makefiles Compiler flags and options
Course content Parallel programming models and platforms GPGPU GPGPU on NVIDIA cards: CUDA Architecture and programming model OpenCL
Today Organization Brief introduction to parallel programming and CUDA Short introduction to Ray tracing
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
Growth of Compute Capability Moore s law source: wikipedia
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
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
Solution: Parallel architectures
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
GPU architecture Simpler architecture than MIMD Little overhead for instruction scheduling, branch prediction etc. Subsequent figures from NVIDIA CUDA Programming Guide 2.3.1 unless mentioned otherwise
GPU architecture Simpler architecture leads to higher performance (compared to CPUs)
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
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
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
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
CUDA, Compute Unified Device Architecture Software: C like programming interface to the GPU Hardware: the hardware that supports the above programming model
CUDA hardware model
CUDA programming model CPU=host, GPU=device, work unit=thread
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
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
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
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
Reminders Exercise session tomorrow Register on CAJ
See you next time!