Introduction to CUDA C
Introduction to CUDA C Introduction to CUDA C
Parallel Programming in CUDA C � With add() running in parallel…let’s do vector addition � Terminology: Each parallel invocation of add() referred to as a block � Kernel can refer to its block’s index with the variable blockIdx.x � Each block adds a value from a[] and b[], storing the result in c[]: __global__ void add( int *a, int *b, int *c ) { c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x]; } � By using blockIdx.x to index arrays, each block handles different indices
Parallel Programming in CUDA C � We write this code: __global__ void add( int *a, int *b, int *c ) { } c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x]; � This is what runs in parallel on the device: Block 0 c[0] = a[0] + b[0]; Block 2 c[2] = a[2] + b[2]; Block 1 c[1] = a[1] + b[1]; Block 3 c[3] = a[3] + b[3];
- Page 1 and 2: Introduction to CUDA C San Jose Con
- Page 3 and 4: What is CUDA? � CUDA Architecture
- Page 5 and 6: CUDA C Prerequisites � You (proba
- Page 7 and 8: Hello, World! int main( void ) { pr
- Page 9 and 10: Hello, World! with Device Code __gl
- Page 11 and 12: A More Complex Example � A simple
- Page 13 and 14: Memory Management � Host and devi
- Page 15 and 16: A More Complex Example: main() int
- Page 17: Parallel Programming in CUDA C �
- Page 21 and 22: Parallel Addition: main() #define N
- Page 23 and 24: Review � Difference between “ho
- Page 25 and 26: Threads � Terminology: A block ca
- Page 27 and 28: Parallel Addition (Threads): main()
- Page 29 and 30: Indexing Arrays With Threads And Bl
- Page 31 and 32: Addition with Threads and Blocks
- Page 33 and 34: Parallel Addition (Blocks/Threads):
- Page 35 and 36: Dot Product � Unlike vector addit
- Page 37 and 38: Dot Product � But we need to shar
- Page 39 and 40: Parallel Dot Product: dot() � We
- Page 41 and 42: Faulty Dot Product Exposed! � Ste
- Page 43 and 44: Synchronization � We need threads
- Page 45 and 46: Parallel Dot Product: dot() __globa
- Page 47 and 48: Parallel Dot Product: main() } // c
- Page 49 and 50: Review (cont) � Using __shared__
- Page 51 and 52: Multiblock Dot Product: Algorithm
- Page 53 and 54: Multiblock Dot Product: dot() #defi
- Page 55 and 56: Global Memory Contention Block 0 su
- Page 57 and 58: Atomic Operations � Terminology:
- Page 59 and 60: Parallel Dot Product: main() #defin
- Page 61 and 62: Review � Race conditions — Beha
- Page 63: Questions � First my questions
Parallel Programming in <strong>CUDA</strong> C<br />
� With add() running in parallel…let’s do vec<strong>to</strong>r addition<br />
� Terminology: Each parallel invocation of add() referred <strong>to</strong> as a block<br />
� Kernel can refer <strong>to</strong> its block’s index with the variable blockIdx.x<br />
� Each block adds a value from a[] and b[], s<strong>to</strong>ring the result in c[]:<br />
__global__ void add( int *a, int *b, int *c ) {<br />
c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x];<br />
}<br />
� By using blockIdx.x <strong>to</strong> index arrays, each block handles different indices
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