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GPU Computing with Ruby

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

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GPU Computing with Ruby



      SpeedGo Computing

         Chung Shin Yee
 shinyee@speedgocomputing.com
CPU vs GPU Architecture
        6 Core vs 1024 Core
6 GB/s vs 300 GB/s Memory Bandwidth




       By CUDA C Programming Guide
CUDA Programming Model



                              .
                              .
                              .
                              .




By CUDA C Programming Guide
Existing Programming Tools
●   Cg
●   BrookGPU
●   GLSL (OpenGL Shading Language)
●   Nvidia CUDA C/C++
●   OpenCL
●   PyCUDA     Where is the Red Ruby ?
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.
Ruby Bridge Sample
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GPU Computing with Ruby

  • 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 . . . . 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.
  • 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