Getting Started with CUDA

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Outline CUDA programming model Basics of CUDA programming Software stack Data management Executing code on the GPU CUDA libraries BLAS FFT © 2008 NVIDIA Corporation.
CUBLAS Implementation of BLAS (Basic Linear Algebra Subprograms) on top of CUDA driver Self contained at the API level, no direct interaction with CUDA driver Basic model for use Create matrix and vector objects in GPU memory space Fill objects with data Call CUBLAS functions Retrieve data CUBLAS library helper functions Creating and destroying data in GPU space Writing data to and retrieving data from objects © 2008 NVIDIA Corporation.
Page 1 and 2: Getting Started with CUDA Greg Ruet
Page 3 and 4: Outline CUDA programming model Basi
Page 5 and 6: CUDA Kernels and Threads Parallel p
Page 7 and 8: Thread Cooperation The Missing Piec
Page 9 and 10: Transparent Scalability Hardware is
Page 11 and 12: 10-Series Architecture 240 thread p
Page 13 and 14: Physical Memory Layout Host “Loca
Page 15 and 16: Key Parallel Abstractions in CUDA T
Page 17 and 18: CUDA Installation CUDA installation
Page 19 and 20: Compiling CUDA Code C/C++ CUDA Appl
Page 23 and 24: GPU Memory Allocation / Release cud
Page 25 and 26: Data Movement Example int main(void
Page 35 and 36: Executing Code on the GPU Kernels a
Page 37 and 38: Launching Kernels Modified C functi
Page 39 and 40: CUDA Built-in Device Variables All
Page 41 and 42: Minimal Kernels __global__ void min
Page 43 and 44: Host Synchronization All kernel lau
Page 45 and 46: Variable Qualifiers (GPU code) __de
Page 47 and 48: GPU Thread Synchronization void __s
Page 49 and 50: Built-in Vector Types Can be used i
Page 51: CUDA Programming Resources We only
Page 55 and 56: Using CUBLAS Interface to CUBLAS li
Page 57 and 58: sgemmExample.c #include #include
Page 61 and 62: More on CUFFT For 2D and 3D transfo
Page 63 and 64: CUFFT Example #include #include #
Page 65: Getting Started with CUDA Greg Ruet

Getting Started with CUDA

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