The document introduces SGC Ruby CUDA, a Ruby library that provides an object-oriented API for CUDA programming to bridge Ruby and CUDA C/C++. It allows performing operations like memory allocation and transfer as well as kernel launching from Ruby. The library aims to make CUDA programming accessible from Ruby while hiding complexity of the low-level CUDA driver and runtime APIs.

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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.
.
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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