Presented in pecha kucha sg, a follow up party of RedDotRubyConf 2011.

1. GPU Computing with Ruby
SpeedGo Computing
Chung Shin Yee
shinyee@speedgocomputing.com

2. CPU vs GPU Architecture
6 Core vs 1024 Core
6 GB/s vs 300 GB/s Memory Bandwidth
By CUDA C Programming Guide

3. CUDA Programming Model
.
.
.
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By CUDA C Programming Guide

4. Existing Programming Tools
● Cg
● BrookGPU
● GLSL (OpenGL Shading Language)
● Nvidia CUDA C/C++
● OpenCL
● PyCUDA Where is the Red Ruby ?

5. Bridging Ruby & CUDA C/C++
● Ruby C extension
– Hard to manipulate Ruby objects in C.
– Compilation problems.
● Ruby FFI
– Bridging purely in Ruby.
– Support multiple Ruby implementations.

6. Ruby Bridge Sample

7. Developing SGC Ruby CUDA
● Object-oriented API.
● Start with crucial operations.
– Memory allocation.
– Memory transfer.
– Kernel launch.
– Wrapper for structures.
● Documented with YARD.

8. Driver vs Runtime API
● CUDA Driver API
– For system developers.
– Supported by PyCUDA.
● CUDA Runtime API
– For computation centric developers.
We going to support both API !

9. Using SGC Ruby CUDA
● Kernel program in CUDA C.

10. Using SGC Ruby CUDA
● Compiling kernel into PTX.
– nvcc --ptx vadd.cu

11. Using SGC Ruby CUDA
● Setup
require 'rubycu'
include SGC::CU
CUInit.init
d = CUDevice.get(0)
c = CUContext.create(d)
m = CUModule.new.load(“vadd.ptx”)
f = m.function(“vadd”)

12. Using SGC Ruby CUDA
● Memory allocations
da = CUDevice.malloc(10*4)
db = CUDevice.malloc(10*4)
dc = CUDevice.malloc(10*4)
ha = Buffer.new(:int, 10)
hb = Buffer.new(:int, 10)
hc = Buffer.new(:int, 10)

13. Using SGC Ruby CUDA
● Initialization
(0...10).each { |i|
ha[i] = i
hb[i] = 1
hc[i] = ha[i] + hb[i]
hd[i] = 0
}

14. Using SGC Ruby CUDA
● Transfer inputs to the GPU
CUMemory.memcpy_htod(da, ha, 4*10)
CUMemory.memcpy_htod(db, hb, 4*10)
CUMemory.memcpy_htod(dc, hc, 4*10)

15. Using SGC Ruby CUDA
● Launch kernel on GPU
# Launch with 1x1x1 grid,
# 10x1x1 blocks,
params = [da, db, dc, 10]
f.launch_kernel(1, 1, 1, 10, 1, 1, 0, 0, params)
By CUDA C Programming Guide By CUDA C Programming Guide

16. Using SGC Ruby CUDA
● Transfer results back to system memory
CUMemory.memcpy_dtoh(hd, dc, 4*10)
● Verify results
(0...10).each { |i|
assert_equal(hc[i], hd[i])
}

17. Problematic CUDA Runtime API
● For use in a CUDA C/C++ program.
● Workaround
– CUDA C/C++ effectively uses C/C++
bindings.
– Create dynamic library for the kernel
programs.
– Load the library at runtime.

18. Current Limitations
● Support limited data types.
– Fixnum → int
– ?? → long
– Float → float
– ?? → double
● No supports for CUDA C++ templates.
● No Ruby in a kernel program.

19. To Support
● Texture memory.
● New features in CUDA 4.0
– Multi-GPU.
– Unified Virtual Memory.
● More C data types.
● Mac platform.

20. Try It Now! Thank You ~
git clone git://github.com/xman/sgc-ruby-cuda.git
cd sgc-ruby-cuda
gem install ffi yard
rake test
rake yard