![Heterogeneous Computing
#include <iostream>
#include <algorithm>
using namespace std;
#define N 1024
#define RADIUS 3
#define BLOCK_SIZE 16
__global__ void stencil_1d(int *in, int *out) {
__shared__ int temp[BLOCK_SIZE + 2 * RADIUS];
int gindex = threadIdx.x + blockIdx.x * blockDim.x;
int lindex = threadIdx.x + RADIUS;
// Read input elements into shared memory
temp[lindex] = in[gindex];
if (threadIdx.x < RADIUS) {
temp[lindex - RADIUS] = in[gindex - RADIUS];
temp[lindex + BLOCK_SIZE] = in[gindex + BLOCK_SIZE];
}
// Synchronize (ensure all the data is available)
__syncthreads();
// Apply the stencil
int result = 0;
for (int offset = -RADIUS ; offset <= RADIUS ; offset++)
result += temp[lindex + offset];
// Store the result
out[gindex] = result;
}
void fill_ints(int *x, int n) {
fill_n(x, n, 1);
}
int main(void) {
int *in, *out; // host copies of a, b, c
int *d_in, *d_out; // device copies of a, b, c
int size = (N + 2*RADIUS) * sizeof(int);
// Alloc space for host copies and setup values
in = (int *)malloc(size); fill_ints(in, N + 2*RADIUS);
out = (int *)malloc(size); fill_ints(out, N + 2*RADIUS);
// Alloc space for device copies
cudaMalloc((void **)&d_in, size);
cudaMalloc((void **)&d_out, size);
// Copy to device
cudaMemcpy(d_in, in, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_out, out, size, cudaMemcpyHostToDevice);
// Launch stencil_1d() kernel on GPU
stencil_1d<<<N/BLOCK_SIZE,BLOCK_SIZE>>>(d_in + RADIUS,
d_out + RADIUS);
// Copy result back to host
cudaMemcpy(out, d_out, size, cudaMemcpyDeviceToHost);
// Cleanup
free(in); free(out);
cudaFree(d_in); cudaFree(d_out);
return 0;
}
serial code
parallel code
serial code
parallel fn
© NVIDIA 2013](https://image.slidesharecdn.com/introductiontocudac-240424154308-54479a34/85/introduction-to-CUDA_C-pptx-it-is-widely-used-8-320.jpg)
![Vector Addition on the Device
• With add() running in parallel we can do vector addition
• Terminology: each parallel invocation of add() is referred to
as a block
– The set of blocks is referred to as a grid
– Each invocation can refer to its block index using blockIdx.x
__global__ void add(int *a, int *b, int *c) {
c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x];
}
• By using blockIdx.x to index into the array, each block handles
a different index
© NVIDIA 2013](https://image.slidesharecdn.com/introductiontocudac-240424154308-54479a34/85/introduction-to-CUDA_C-pptx-it-is-widely-used-28-320.jpg)
![Vector Addition on the Device
__global__ void add(int *a, int *b, int *c) {
c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x];
}
• On the device, each block can execute in parallel:
c[0] = a[0] + b[0]; c[1] = a[1] + b[1]; c[2] = a[2] + b[2]; c[3] = a[3] + b[3];
Block 0 Block 1 Block 2 Block 3
© NVIDIA 2013](https://image.slidesharecdn.com/introductiontocudac-240424154308-54479a34/85/introduction-to-CUDA_C-pptx-it-is-widely-used-29-320.jpg)
![Vector Addition on the Device: add()
• Returning to our parallelized add() kernel
__global__ void add(int *a, int *b, int *c) {
c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x];
}
• Let’s take a look at main()…
© NVIDIA 2013](https://image.slidesharecdn.com/introductiontocudac-240424154308-54479a34/85/introduction-to-CUDA_C-pptx-it-is-widely-used-31-320.jpg)
![CUDA Threads
• Terminology: a block can be split into parallel threads
• Let’s change add() to use parallel threads instead of
parallel blocks
• We use threadIdx.x instead of blockIdx.x
• Need to make one change in main()…
__global__ void add(int *a, int *b, int *c) {
c[threadIdx.x] = a[threadIdx.x] + b[threadIdx.x];
}
© NVIDIA 2013](https://image.slidesharecdn.com/introductiontocudac-240424154308-54479a34/85/introduction-to-CUDA_C-pptx-it-is-widely-used-37-320.jpg)
![Vector Addition with Blocks and
Threads
• What changes need to be made in main()?
• Use the built-in variable blockDim.x for threads per
block
int index = threadIdx.x + blockIdx.x * blockDim.x;
• Combined version of add() to use parallel
threads and parallel blocks
__global__ void add(int *a, int *b, int *c) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
c[index] = a[index] + b[index];
}
© NVIDIA 2013](https://image.slidesharecdn.com/introductiontocudac-240424154308-54479a34/85/introduction-to-CUDA_C-pptx-it-is-widely-used-44-320.jpg)
![Handling Arbitrary Vector Sizes
• Update the kernel launch:
add<<<(N + M-1) / M,M>>>(d_a, d_b, d_c, N);
• Typical problems are not friendly multiples of
blockDim.x
• Avoid accessing beyond the end of the arrays:
__global__ void add(int *a, int *b, int *c, int n) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
if (index < n)
c[index] = a[index] + b[index];
}
© NVIDIA 2013](https://image.slidesharecdn.com/introductiontocudac-240424154308-54479a34/85/introduction-to-CUDA_C-pptx-it-is-widely-used-47-320.jpg)
introduction to CUDA_C.pptx it is widely used
- 1. CUDA C/C++ BASICS NVIDIA Corporation © NVIDIA 2013
- 2. What is CUDA? • CUDA Architecture – Expose GPU parallelism for general-purpose computing – Retain performance • CUDA C/C++ – Based on industry-standard C/C++ – Small set of extensions to enable heterogeneous programming – Straightforward APIs to manage devices, memory etc. • This session introduces CUDA C/C++ © NVIDIA 2013
- 3. Introduction to CUDA C/C++ • What will you learn in this session? – Start from “Hello World!” – Write and launch CUDA C/C++ kernels – Manage GPU memory – Manage communication and synchronization © NVIDIA 2013
- 4. Prerequisites • You (probably) need experience with C or C++ • You don’t need GPU experience • You don’t need parallel programming experience • You don’t need graphics experience © NVIDIA 2013
- 5. Heterogeneous Computing Blocks Threads Indexing Shared memory __syncthreads() Asynchronous operation Handling errors Managing devices CONCEPTS © NVIDIA 2013
- 6. HELLO WORLD! Heterogeneous Computing Blocks Threads Indexing Shared memory __syncthreads() Asynchronous operation Handling errors Managing devices CONCEPTS
- 7. Heterogeneous Computing Terminology: Host The CPU and its memory (host memory) Device The GPU and its memory (device memory) Host Device © NVIDIA 2013
- 8. Heterogeneous Computing #include <iostream> #include <algorithm> using namespace std; #define N 1024 #define RADIUS 3 #define BLOCK_SIZE 16 __global__ void stencil_1d(int *in, int *out) { __shared__ int temp[BLOCK_SIZE + 2 * RADIUS]; int gindex = threadIdx.x + blockIdx.x * blockDim.x; int lindex = threadIdx.x + RADIUS; // Read input elements into shared memory temp[lindex] = in[gindex]; if (threadIdx.x < RADIUS) { temp[lindex - RADIUS] = in[gindex - RADIUS]; temp[lindex + BLOCK_SIZE] = in[gindex + BLOCK_SIZE]; } // Synchronize (ensure all the data is available) __syncthreads(); // Apply the stencil int result = 0; for (int offset = -RADIUS ; offset <= RADIUS ; offset++) result += temp[lindex + offset]; // Store the result out[gindex] = result; } void fill_ints(int *x, int n) { fill_n(x, n, 1); } int main(void) { int *in, *out; // host copies of a, b, c int *d_in, *d_out; // device copies of a, b, c int size = (N + 2*RADIUS) * sizeof(int); // Alloc space for host copies and setup values in = (int *)malloc(size); fill_ints(in, N + 2*RADIUS); out = (int *)malloc(size); fill_ints(out, N + 2*RADIUS); // Alloc space for device copies cudaMalloc((void **)&d_in, size); cudaMalloc((void **)&d_out, size); // Copy to device cudaMemcpy(d_in, in, size, cudaMemcpyHostToDevice); cudaMemcpy(d_out, out, size, cudaMemcpyHostToDevice); // Launch stencil_1d() kernel on GPU stencil_1d<<<N/BLOCK_SIZE,BLOCK_SIZE>>>(d_in + RADIUS, d_out + RADIUS); // Copy result back to host cudaMemcpy(out, d_out, size, cudaMemcpyDeviceToHost); // Cleanup free(in); free(out); cudaFree(d_in); cudaFree(d_out); return 0; } serial code parallel code serial code parallel fn © NVIDIA 2013
- 9. Simple Processing Flow 1. Copy input data from CPU memory to GPU memory PCI Bus © NVIDIA 2013
- 10. Simple Processing Flow 1. Copy input data from CPU memory to GPU memory 2. Load GPU program and execute, caching data on chip for performance © NVIDIA 2013 PCI Bus
- 11. Simple Processing Flow 1. Copy input data from CPU memory to GPU memory 2. Load GPU program and execute, caching data on chip for performance 3. Copy results from GPU memory to CPU memory © NVIDIA 2013 PCI Bus
- 12. Hello World! int main(void) { printf("Hello World!n"); return 0; } Standard C that runs on the host NVIDIA compiler (nvcc) can be used to compile programs with no device code Output: $ nvcc hello_world. cu $ a.out Hello World! $ © NVIDIA 2013
- 13. Hello World! with Device Code __global__ void mykernel(void) { } int main(void) { mykernel<<<1,1>>>(); printf("Hello World!n"); return 0; } Two new syntactic elements… © NVIDIA 2013
- 14. Hello World! with Device Code __global__ void mykernel(void) { } • CUDA C/C++ keyword __global__ indicates a function that: – Runs on the device – Is called from host code • nvcc separates source code into host and device components – Device functions (e.g. mykernel()) processed by NVIDIA compiler – Host functions (e.g. main()) processed by standard host compiler • gcc, cl.exe © NVIDIA 2013
- 15. Hello World! with Device COde mykernel<<<1,1>>>(); • Triple angle brackets mark a call from host code to device code – Also called a “kernel launch” – We’ll return to the parameters (1,1) in a moment • That’s all that is required to execute a function on the GPU! © NVIDIA 2013
- 16. Hello World! with Device Code __global__ void mykernel(void){ } int main(void) { mykernel<<<1,1>>>(); printf("Hello World!n"); return 0; } • mykernel() does nothing, somewhat anticlimactic! Output: $ nvcc hello.cu $ a.out Hello World! $ © NVIDIA 2013
- 17. Parallel Programming in CUDA C/C++ • But wait… GPU computing is about massive parallelism! • We need a more interesting example… • We’ll start by adding two integers and build up to vector addition a b c © NVIDIA 2013
- 18. Addition on the Device • A simple kernel to add two integers __global__ void add(int *a, int *b, int *c) { *c = *a + *b; } • As before __global__ is a CUDA C/C++ keyword meaning – add() will execute on the device – add() will be called from the host © NVIDIA 2013
- 19. Addition on the Device • Note that we use pointers for the variables __global__ void add(int *a, int *b, int *c) { *c = *a + *b; } • add() runs on the device, so a, b and c must point to device memory • We need to allocate memory on the GPU © NVIDIA 2013
- 20. Memory Management • Host and device memory are separate entities – Device pointers point to GPU memory May be passed to/from host code May not be dereferenced in host code – Host pointers point to CPU memory May be passed to/from device code May not be dereferenced in device code • Simple CUDA API for handling device memory – cudaMalloc(), cudaFree(), cudaMemcpy() – Similar to the C equivalents malloc(), free(), memcpy() © NVIDIA 2013
- 21. Addition on the Device: add() • Returning to our add() kernel __global__ void add(int *a, int *b, int *c) { *c = *a + *b; } • Let’s take a look at main()… © NVIDIA 2013
- 22. Addition on the Device: main() int main(void) { int a, b, c; // host copies of a, b, c int *d_a, *d_b, *d_c; // device copies of a, b, c int size = sizeof(int); // Allocate space for device copies of a, b, c cudaMalloc((void **)&d_a, size); cudaMalloc((void **)&d_b, size); cudaMalloc((void **)&d_c, size); // Setup input values a = 2; b = 7; © NVIDIA 2013
- 23. Addition on the Device: main() // Copy inputs to device cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice); cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice); // Launch add() kernel on GPU add<<<1,1>>>(d_a, d_b, d_c); // Copy result back to host cudaMemcpy(&c, d_c, size, cudaMemcpyDeviceToHost); // Cleanup cudaFree(d_a); cudaFree(d_b); cudaFree(d_c); return 0; } © NVIDIA 2013
- 24. RUNNING IN PARALLEL Heterogeneous Computing Blocks Threads Indexing Shared memory __syncthreads() Asynchronous operation Handling errors Managing devices CONCEPTS © NVIDIA 2013
- 25. Moving to Parallel • GPU computing is about massive parallelism – So how do we run code in parallel on the device? add<<< 1, 1 >>>(); add<<< N, 1 >>>(); • Instead of executing add() once, execute N times in parallel © NVIDIA 2013
- 26. Thread Block
- 27. Grid
- 28. Vector Addition on the Device • With add() running in parallel we can do vector addition • Terminology: each parallel invocation of add() is referred to as a block – The set of blocks is referred to as a grid – Each invocation can refer to its block index using blockIdx.x __global__ void add(int *a, int *b, int *c) { c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x]; } • By using blockIdx.x to index into the array, each block handles a different index © NVIDIA 2013
- 29. Vector Addition on the Device __global__ void add(int *a, int *b, int *c) { c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x]; } • On the device, each block can execute in parallel: c[0] = a[0] + b[0]; c[1] = a[1] + b[1]; c[2] = a[2] + b[2]; c[3] = a[3] + b[3]; Block 0 Block 1 Block 2 Block 3 © NVIDIA 2013
- 30. Declaring Variables __global__ declares kernel, which is called on host and executed on device __device__ declares device function, which is called and executed on device __host__ declares host function, which is called and executed on host dim3 gridDim dimensions of grid dim3 blockDim dimensions of block uint3 blockIdx block index within grid uint3 threadIdx thread index within block int warpSize number of threads in warp
- 31. Vector Addition on the Device: add() • Returning to our parallelized add() kernel __global__ void add(int *a, int *b, int *c) { c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x]; } • Let’s take a look at main()… © NVIDIA 2013
- 32. Vector Addition on the Device: main() #define N 512 int main(void) { int *a, *b, *c; // host copies of a, b, c int *d_a, *d_b, *d_c; // device copies of a, b, c int size = N * sizeof(int); // Alloc space for device copies of a, b, c cudaMalloc((void **)&d_a, size); cudaMalloc((void **)&d_b, size); cudaMalloc((void **)&d_c, size); // Alloc space for host copies of a, b, c and setup input values a = (int *)malloc(size); random_ints(a, N); b = (int *)malloc(size); random_ints(b, N); c = (int *)malloc(size); © NVIDIA 2013
- 33. Vector Addition on the Device: main() // Copy inputs to device cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice); cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice); // Launch add() kernel on GPU with N blocks add<<<N,1>>>(d_a, d_b, d_c); // Copy result back to host cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost); // Cleanup free(a); free(b); free(c); cudaFree(d_a); cudaFree(d_b); cudaFree(d_c); return 0; } © NVIDIA 2013
- 34. Review (1 of 2) • Difference between host and device – Host CPU – Device GPU • Using __global__ to declare a function as device code – Executes on the device – Called from the host • Passing parameters from host code to a device function © NVIDIA 2013
- 35. Review (2 of 2) • Basic device memory management – cudaMalloc() – cudaMemcpy() – cudaFree() • Launching parallel kernels – Launch N copies of add() with add<<<N,1>>>(…); – Use blockIdx.x to access block index © NVIDIA 2013
- 36. INTRODUCING THREADS Heterogeneous Computing Blocks Threads Indexing Shared memory __syncthreads() Asynchronous operation Handling errors Managing devices CONCEPTS © NVIDIA 2013
- 37. CUDA Threads • Terminology: a block can be split into parallel threads • Let’s change add() to use parallel threads instead of parallel blocks • We use threadIdx.x instead of blockIdx.x • Need to make one change in main()… __global__ void add(int *a, int *b, int *c) { c[threadIdx.x] = a[threadIdx.x] + b[threadIdx.x]; } © NVIDIA 2013
- 38. Vector Addition Using Threads: main() #define N 512 int main(void) { int *a, *b, *c; // host copies of a, b, c int *d_a, *d_b, *d_c; // device copies of a, b, c int size = N * sizeof(int); // Alloc space for device copies of a, b, c cudaMalloc((void **)&d_a, size); cudaMalloc((void **)&d_b, size); cudaMalloc((void **)&d_c, size); // Alloc space for host copies of a, b, c and setup input values a = (int *)malloc(size); random_ints(a, N); b = (int *)malloc(size); random_ints(b, N); c = (int *)malloc(size); © NVIDIA 2013
- 39. Vector Addition Using Threads: main() // Copy inputs to device cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice); cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice); // Launch add() kernel on GPU with N threads add<<<1,N>>>(d_a, d_b, d_c); // Copy result back to host cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost); // Cleanup free(a); free(b); free(c); cudaFree(d_a); cudaFree(d_b); cudaFree(d_c); return 0; } © NVIDIA 2013
- 40. COMBINING THREADS AND BLOCKS Heterogeneous Computing Blocks Threads Indexing Shared memory __syncthreads() Asynchronous operation Handling errors Managing devices CONCEPTS © NVIDIA 2013
- 41. Combining Blocks and Threads • We’ve seen parallel vector addition using: – Many blocks with one thread each – One block with many threads • Let’s adapt vector addition to use both blocks and threads • Why? We’ll come to that… • First let’s discuss data indexing… © NVIDIA 2013
- 42. 0 1 7 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 Indexing Arrays with Blocks and Threads • With M threads/block a unique index for each thread is given by: int index = threadIdx.x + blockIdx.x * M; • No longer as simple as using blockIdx.x and threadIdx.x – Consider indexing an array with one element per thread (8 threads/block) threadIdx.x threadIdx.x threadIdx.x threadIdx.x blockIdx.x = 0 blockIdx.x = 1 blockIdx.x = 2 blockIdx.x = 3 © NVIDIA 2013
- 43. Indexing Arrays: Example • Which thread will operate on the red element? int index = threadIdx.x + blockIdx.x * M; = 5 + 2 * 8; = 21; 0 1 7 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 threadIdx.x = 5 blockIdx.x = 2 0 1 31 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 M = 8 © NVIDIA 2013
- 44. Vector Addition with Blocks and Threads • What changes need to be made in main()? • Use the built-in variable blockDim.x for threads per block int index = threadIdx.x + blockIdx.x * blockDim.x; • Combined version of add() to use parallel threads and parallel blocks __global__ void add(int *a, int *b, int *c) { int index = threadIdx.x + blockIdx.x * blockDim.x; c[index] = a[index] + b[index]; } © NVIDIA 2013
- 45. Addition with Blocks and Threads: main() #define N (2048*2048) #define THREADS_PER_BLOCK 512 int main(void) { int *a, *b, *c; // host copies of a, b, c int *d_a, *d_b, *d_c; // device copies of a, b, c int size = N * sizeof(int); // Alloc space for device copies of a, b, c cudaMalloc((void **)&d_a, size); cudaMalloc((void **)&d_b, size); cudaMalloc((void **)&d_c, size); // Alloc space for host copies of a, b, c and setup input values a = (int *)malloc(size); random_ints(a, N); b = (int *)malloc(size); random_ints(b, N); c = (int *)malloc(size); © NVIDIA 2013
- 46. Addition with Blocks and Threads: main() // Copy inputs to device cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice); cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice); // Launch add() kernel on GPU add<<<N/THREADS_PER_BLOCK,THREADS_PER_BLOCK>>>(d_a, d_b, d_c); // Copy result back to host cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost); // Cleanup free(a); free(b); free(c); cudaFree(d_a); cudaFree(d_b); cudaFree(d_c); return 0; } © NVIDIA 2013
- 47. Handling Arbitrary Vector Sizes • Update the kernel launch: add<<<(N + M-1) / M,M>>>(d_a, d_b, d_c, N); • Typical problems are not friendly multiples of blockDim.x • Avoid accessing beyond the end of the arrays: __global__ void add(int *a, int *b, int *c, int n) { int index = threadIdx.x + blockIdx.x * blockDim.x; if (index < n) c[index] = a[index] + b[index]; } © NVIDIA 2013
- 48. Why Bother with Threads? • Threads seem unnecessary – They add a level of complexity – What do we gain? • Unlike parallel blocks, threads have mechanisms to: – Communicate – Synchronize • To look closer, we need a new example… © NVIDIA 2013
- 49. COOPERATING THREADS Heterogeneous Computing Blocks Threads Indexing Shared memory __syncthreads() Asynchronous operation Handling errors Managing devices CONCEPTS © NVIDIA 2013
- 50. Sharing Data Between Threads • Terminology: within a block, threads share data via shared memory • Extremely fast on-chip memory, user-managed • Declare using __shared__, allocated per block • Data is not visible to threads in other blocks © NVIDIA 2013
- 51. __syncthreads() • void __syncthreads(); • Synchronizes all threads within a block – Used to prevent RAW / WAR / WAW hazards • All threads must reach the barrier – In conditional code, the condition must be uniform across the block © NVIDIA 2013
- 52. Review (1 of 2) • Launching parallel threads – Launch N blocks with M threads per block with kernel<<<N,M>>>(…); – Use blockIdx.x to access block index within grid – Use threadIdx.x to access thread index within block • Allocate elements to threads: int index = threadIdx.x + blockIdx.x * blockDim.x; © NVIDIA 2013
- 53. Review (2 of 2) • Use __shared__ to declare a variable/array in shared memory – Data is shared between threads in a block – Not visible to threads in other blocks • Use __syncthreads() as a barrier – Use to prevent data hazards © NVIDIA 2013
- 54. MANAGING THE DEVICE Heterogeneous Computing Blocks Threads Indexing Shared memory __syncthreads() Asynchronous operation Handling errors Managing devices CONCEPTS © NVIDIA 2013
- 55. Coordinating Host & Device • Kernel launches are asynchronous – Control returns to the CPU immediately • CPU needs to synchronize before consuming the results cudaMemcpy() Blocks the CPU until the copy is complete Copy begins when all preceding CUDA calls have completed cudaMemcpyAsync() Asynchronous, does not block the CPU cudaDeviceSynchro nize() Blocks the CPU until all preceding CUDA calls have completed © NVIDIA 2013
- 56. Reporting Errors • All CUDA API calls return an error code (cudaError_t) – Error in the API call itself OR – Error in an earlier asynchronous operation (e.g. kernel) • Get the error code for the last error: cudaError_t cudaGetLastError(void) • Get a string to describe the error: char *cudaGetErrorString(cudaError_t) printf("%sn", cudaGetErrorString(cudaGetLastError())); © NVIDIA 2013
- 57. Device Management • Application can query and select GPUs cudaGetDeviceCount(int *count) cudaSetDevice(int device) cudaGetDevice(int *device) cudaGetDeviceProperties(cudaDeviceProp *prop, int device) • Multiple threads can share a device • A single thread can manage multiple devices cudaSetDevice(i) to select current device cudaMemcpy(…) for peer-to-peer copies✝ ✝ requires OS and device support © NVIDIA 2013
- 58. Introduction to CUDA C/C++ • What have we learned? – Write and launch CUDA C/C++ kernels • __global__, blockIdx.x, threadIdx.x, <<<>>> – Manage GPU memory • cudaMalloc(), cudaMemcpy(), cudaFree() – Manage communication and synchronization • __shared__, __syncthreads() • cudaMemcpy() vs cudaMemcpyAsync(), cudaDeviceSynchronize() © NVIDIA 2013
- 59. Compute Capability • The compute capability of a device describes its architecture, e.g. – Number of registers – Sizes of memories – Features & capabilities • The following presentations concentrate on Fermi devices – Compute Capability >= 2.0 Compute Capability Selected Features (see CUDA C Programming Guide for complete list) Tesla models 1.0 Fundamental CUDA support 870 1.3 Double precision, improved memory accesses, atomics 10-series 2.0 Caches, fused multiply-add, 3D grids, surfaces, ECC, P2P, concurrent kernels/copies, function pointers, recursion 20-series © NVIDIA 2013
- 60. IDs and Dimensions – A kernel is launched as a grid of blocks of threads • blockIdx and threadIdx are 3D • We showed only one dimension (x) • Built-in variables: – threadIdx – blockIdx – blockDim – gridDim Device Grid 1 Bloc k (0,0, 0) Bloc k (1,0, 0) Bloc k (2,0, 0) Bloc k (1,1, 0) Bloc k (2,1, 0) Bloc k (0,1, 0) Block (1,1,0) Thre ad (0,0, 0) Thre ad (1,0, 0) Thre ad (2,0, 0) Thre ad (3,0, 0) Thre ad (4,0, 0) Thre ad (0,1, 0) Thre ad (1,1, 0) Thre ad (2,1, 0) Thre ad (3,1, 0) Thre ad (4,1, 0) Thre ad (0,2, 0) Thre ad (1,2, 0) Thre ad (2,2, 0) Thre ad (3,2, 0) Thre ad (4,2, 0) © NVIDIA 2013
- 61. Textures • Read-only object – Dedicated cache • Dedicated filtering hardware (Linear, bilinear, trilinear) • Addressable as 1D, 2D or 3D • Out-of-bounds address handling (Wrap, clamp) 0 1 2 3 0 1 2 4 (2.5, 0.5) (1.0, 1.0) © NVIDIA 2013
- 62. Topics we skipped • We skipped some details, you can learn more: – CUDA Programming Guide – CUDA Zone – tools, training, webinars and more developer.nvidia.com/cuda • Need a quick primer for later: – Multi-dimensional indexing – Textures © NVIDIA 2013
- 63. Find Largest element of an array © NVIDIA 2013
- 64. © NVIDIA 2013
Editor's Notes
- #43: Identical to finding offset in 1-dimensional storage of a 2-dimensional matrix: int index = x + width * y;