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Algorithm Engineering „ GPGPU“

Algorithm Engineering „ GPGPU“. Stefan Edelkamp. Graphics Processing Units. GPGPU = (GP)²U General Purpose Programming on the GPU „ Parallelism for the masses “ Application : Fourier-Transformation, Model Checking , Bio- Informatics , see CUDA-ZONE.

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Algorithm Engineering „ GPGPU“

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  1. Algorithm Engineering „GPGPU“ Stefan Edelkamp

  2. Graphics Processing Units • GPGPU = (GP)²U General PurposeProgramming on the GPU • „Parallelismforthemasses“ • Application: Fourier-Transformation, Model Checking, Bio-Informatics, see CUDA-ZONE

  3. Programming the Graphics Processing Unitwith Cuda

  4. Overview • Cluster / Multicore / GPU comparison • Computing on the GPU • GPGPU languages • CUDA • Small Example

  5. Overview • Cluster / Multicore / GPU comparison • Computing on the GPU • GPGPU languages • CUDA • Small Example

  6. Cluster / Multicore / GPU CPU RAM • Cluster system • many unique systems • each one • one (or more) processors • internal memory • often HDD • communication over network • slow compared to internal • no shared memory HDD CPU RAM Switch HDD CPU RAM HDD

  7. Cluster / Multicore / GPU • Multicore systems • multiple CPUs • RAM • external memory on HDD • communication over RAM CPU1 CPU2 CPU3 CPU4 RAM HDD

  8. Cluster / Multicore / GPU • System with a Graphic Processing Unit • Many (240) Parallel processing units • Hierarchical memory structure • RAM • VideoRAM • SharedRAM • Communication • PCI BUS Graphics Card CPU VRAM GPU RAM SRAM Hard Disk Drive

  9. Overview • Cluster / Multicore / GPU comparison • Computing on the GPU • GPGPU languages • CUDA • Small Example

  10. Computing on the GPU • Hierarchical execution • Groups • executed sequentially • Threads • executed parallel • lightweight (creation / switching nearly free)‏ • one Kernel function • executed by each thread Group 0

  11. Computing on the GPU • Hierarchical memory • Video RAM • 1 GB • Comparable to RAM • Shared RAM in the GPU • 16 KB • Comparable to registers • parallel access by threads Graphic Card VideoRAM GPU SRAM

  12. Beispielarchitektur G200 z.B. in 280GTX

  13. Beispielprobleme

  14. Ranking und Unranking mit Parity

  15. 2-Bit BFS

  16. 1-Bit BFS

  17. Schiebepuzzle

  18. SomeResults…

  19. Weitere Resultate …

  20. Overview • Cluster / Multicore / GPU comparison • Computing on the GPU • GPGPU languages • CUDA • Small Example

  21. GPGPU Languages • RapidMind • Supports MultiCore, ATI, NVIDIA and Cell • C++ analysed and compiled for target hardware • Accelerator (Microsoft)‏ • Library for .NET language • BrookGPU (Stanford University)‏ • Supports ATI, NVIDIA • Own Language, variant of ANSI C

  22. Overview • Cluster / Multicore / GPU comparison • Computing on the GPU • Programming languages • CUDA • Small Example

  23. CUDA • Programming language • Similar to C • File suffix .cu • Own compiler called nvcc • Can be linked to C

  24. CUDA C++ code CUDA Code Compile with GCC Compile with nvcc Link with ld Executable

  25. CUDA • Additional variable types • Dim3 • Int3 • Char3

  26. CUDA • Different types of functions • __global__ invoked from host • __device__ called from device • Different types of variables • __device__ located in VRAM • __shared__ located in SRAM

  27. CUDA • Calling the kernel function • name<<<dim3 grid, dim3 block>>>(...)‏ • Grid dimensions (groups)‏ • Block dimensions (threads)‏

  28. CUDA • Memory handling • CudaMalloc(...) - allocating VRAM • CudaMemcpy(...) - copying Memory • CudaFree(...) - free VRAM

  29. CUDA • Distinguish threads • blockDim – Number of all groups • blockIdx – Id of Group (starting with 0)‏ • threadIdx – Id of Thread (starting with 0)‏ • Id = blockDim.x*blockIdx.x+threadIdx.x

  30. Overview • Cluster / Multicore / GPU comparison • Computing on the GPU • Programming languages • CUDA • Small Example

  31. __global__ void inc(int *a, int b, int N)‏ { int id = blockDim.x*blockIdx.x+threadIdx.x; if (id<N) a[id] = a[id] + b; } void main()‏ { ... int * a_d = CudaAlloc(N); CudaMemCpy(a_d,a,N,HostToDevice); dim3 dimBlock ( blocksize, 0, 0 ); dim3 dimGrid ( N / blocksize, 0, 0 ); inc<<<dimGrid,dimBlock>>>(a_d,b,N); } CUDA void inc(int *a, int b, int N) { for (inti = 0; i<N; i++) a[i] = a[i] + b; } void main()‏ { ... inc(a,b,N); }

  32. Realworld Example • LTL Model checking • Traversing an implicit Graph G=(V,E)‏ • Vertices called states • Edges represented by transitions • Duplicate removal needed

  33. Realworld Example • External Model checking • Generate Graph with external BFS • Each BFS layer needs to be sorted • GPU proven to be fast in sorting

  34. Realworld Example • Challenges • Millions of states in one layer • Huge state size • Fast access only in SRAM • Elements needs to be moved

  35. Realworld Example • Solutions: • Gpuqsort • Qsort optimized for GPUs • Intensive swapping in VRAM • Bitonic based sorting • Fast for subgroups • Concatenating Groups slow

  36. SRAM VRAM Realworld Example • Our solution • States S presorted by Hash H(S) • Bucket sorted in SRAM by a Group

  37. Realworld Example • Our solution • Order given by H(S),S

  38. Realworld Example • Results

  39. Programming the GPU • Questions???

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