| ubuntu 에서 vectorAdd 직접 컴파일 하기 (0) | 2012.06.03 |
|---|---|
| CUDA devicequery - ION 330 (0) | 2012.06.02 |
| nvidia ion cuda core와 h.264 library (0) | 2012.05.22 |
| CUDA API 메모리 종류 (0) | 2012.05.18 |
| Interoperability (상호운용성) (0) | 2012.05.04 |
MPEG-2/VC-1 support
Decode Acceleration for G8x, G9x (Requires Compute 1.1 or higher)
Full Bitstream Decode for MCP79, MCP89, G98, GT2xx, GF1xx
MPEG-2 CUDA accelerated decode with a GPUs with 8+ SMs (64 CUDA cores). (Windows)
Supports HD (1080i/p) playback including Bluray content
R185+ (Windows), R260+ (Linux)
H.264/AVCHD support
Baseline, Main, and High Profile, up to Level 4.1
Full Bitstream Decoding in hardware including HD (1080i/p) Bluray content
Supports B-Frames, bitrates up to 45 mbps
Available on NVIDIA GPUs: G8x, G9x, MCP79, MCP89, G98, GT2xx, GF1xx
R185+ (Windows), R260+ (Linux)
[출처 : CUDA_VideoDecoder_Library.pdf] |
| Supported on all CUDA-enabled GPUs with 32 scalar processor cores or more [출처 : CUDA_VideoEncoder_Library.pdf] |
Device 0: "ION"
CUDA Driver Version: 2.30
CUDA Runtime Version: 2.30
CUDA Capability Major revision number: 1
CUDA Capability Minor revision number: 1
Total amount of global memory: 268435456 bytes
Number of multiprocessors: 2
Number of cores: 16
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 16384 bytes
Total number of registers available per block: 8192
Warp size: 32
Maximum number of threads per block: 512
Maximum sizes of each dimension of a block: 512 x 512 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 1
Maximum memory pitch: 262144 bytes
Texture alignment: 256 bytes
Clock rate: 1.10 GHz
Concurrent copy and execution: No
Run time limit on kernels: No
Integrated: Yes
Support host page-locked memory mapping: Yes
Compute mode: Default (multiple host threads can use this devi
[링크 : http://forums.nvidia.com/index.php?showtopic=100288 ] |
| CUDA devicequery - ION 330 (0) | 2012.06.02 |
|---|---|
| cuda 5 preview (0) | 2012.06.02 |
| CUDA API 메모리 종류 (0) | 2012.05.18 |
| Interoperability (상호운용성) (0) | 2012.05.04 |
| cuda 내장변수 (0) | 2012.04.30 |
| 5.3.2 Device Memory Accesses .................................................................... 70
5.3.2.1 Global Memory ............................................................................ 70
5.3.2.2 Local Memory .............................................................................. 72
5.3.2.3 Shared Memory ........................................................................... 72
5.3.2.4 Constant Memory ........................................................................ 73
5.3.2.5 Texture and Surface Memory ........................................................ 73 [출처 : CUDA C Programming guide.pdf] |
| Device 0: "GeForce 8800 GT" CUDA Driver Version: 3.20 CUDA Runtime Version: 3.10 CUDA Capability Major revision number: 1 CUDA Capability Minor revision number: 1 Total amount of global memory: 536543232 bytes Number of multiprocessors: 14 Number of cores: 112 Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 16384 bytes Total number of registers available per block: 8192 Warp size: 32 Maximum number of threads per block: 512 Maximum sizes of each dimension of a block: 512 x 512 x 64 Maximum sizes of each dimension of a grid: 65535 x 65535 x 1 2011/01/02 - [Programming/openCL / CUDA] - deviceQuery on 8600GT 512MB + CUDA 하드웨어 구조 |
 |
 |
| // Matrices are stored in row-major order:
// M(row, col) = *(M.elements + row * M.width + col)
typedef struct {
int width;
int height;
float* elements;
} Matrix;
// Thread block size
#define BLOCK_SIZE 16
// Forward declaration of the matrix multiplication kernel
__global__ void MatMulKernel(const Matrix, const Matrix, Matrix);
// Matrix multiplication - Host code
// Matrix dimensions are assumed to be multiples of BLOCK_SIZE
void MatMul(const Matrix A, const Matrix B, Matrix C)
{
// Load A and B to device memory
Matrix d_A;
d_A.width = A.width; d_A.height = A.height;
size_t size = A.width * A.height * sizeof(float);
cudaMalloc(&d_A.elements, size);
cudaMemcpy(d_A.elements, A.elements, size,
cudaMemcpyHostToDevice);
Matrix d_B;
d_B.width = B.width; d_B.height = B.height;
size = B.width * B.height * sizeof(float);
cudaMalloc(&d_B.elements, size);
cudaMemcpy(d_B.elements, B.elements, size,
cudaMemcpyHostToDevice);
// Allocate C in device memory
Matrix d_C;
d_C.width = C.width; d_C.height = C.height;
size = C.width * C.height * sizeof(float);
cudaMalloc(&d_C.elements, size);
// Invoke kernel
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y);
MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
// Read C from device memory
cudaMemcpy(C.elements, Cd.elements, size,
cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_A.elements);
cudaFree(d_B.elements);
cudaFree(d_C.elements);
}
// Matrix multiplication kernel called by MatMul()
__global__ void MatMulKernel(Matrix A, Matrix B, Matrix C)
{
// Each thread computes one element of C
// by accumulating results into Cvalue
float Cvalue = 0;
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
for (int e = 0; e < A.width; ++e)
Cvalue += A.elements[row * A.width + e]
* B.elements[e * B.width + col];
C.elements[row * C.width + col] = Cvalue;
} |
// Matrices are stored in row-major order:
// M(row, col) = *(M.elements + row * M.stride + col)
typedef struct {
int width;
int height;
int stride;
float* elements;
} Matrix;
// Get a matrix element
__device__ float GetElement(const Matrix A, int row, int col)
{
return A.elements[row * A.stride + col];
}
// Set a matrix element
__device__ void SetElement(Matrix A, int row, int col,
float value)
{
A.elements[row * A.stride + col] = value;
}
// Get the BLOCK_SIZExBLOCK_SIZE sub-matrix Asub of A that is
// located col sub-matrices to the right and row sub-matrices down
// from the upper-left corner of A
__device__ Matrix GetSubMatrix(Matrix A, int row, int col)
{
Matrix Asub;
Asub.width = BLOCK_SIZE;
Asub.height = BLOCK_SIZE;
Asub.stride = A.stride;
Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row
+ BLOCK_SIZE * col];
return Asub;
}
// Thread block size
#define BLOCK_SIZE 16
// Forward declaration of the matrix multiplication kernel
__global__ void MatMulKernel(const Matrix, const Matrix, Matrix);
// Matrix multiplication - Host code
// Matrix dimensions are assumed to be multiples of BLOCK_SIZE
void MatMul(const Matrix A, const Matrix B, Matrix C)
{
// Load A and B to device memory
Matrix d_A;
d_A.width = d_A.stride = A.width; d_A.height = A.height;
size_t size = A.width * A.height * sizeof(float);
cudaMalloc(&d_A.elements, size);
cudaMemcpy(d_A.elements, A.elements, size,
cudaMemcpyHostToDevice);
Matrix d_B;
d_B.width = d_B.stride = B.width; d_B.height = B.height;
size = B.width * B.height * sizeof(float);
cudaMalloc(&d_B.elements, size);
cudaMemcpy(d_B.elements, B.elements, size,
cudaMemcpyHostToDevice);
// Allocate C in device memory
Matrix d_C;
d_C.width = d_C.stride = C.width; d_C.height = C.height;
size = C.width * C.height * sizeof(float);
cudaMalloc(&d_C.elements, size);
// Invoke kernel
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y);
MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
// Read C from device memory
cudaMemcpy(C.elements, d_C.elements, size,
cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_A.elements);
cudaFree(d_B.elements);
cudaFree(d_C.elements);
}
// Matrix multiplication kernel called by MatMul()
__global__ void MatMulKernel(Matrix A, Matrix B, Matrix C)
{
// Block row and column
int blockRow = blockIdx.y;
int blockCol = blockIdx.x;
// Each thread block computes one sub-matrix Csub of C
Matrix Csub = GetSubMatrix(C, blockRow, blockCol);
// Each thread computes one element of Csub
// by accumulating results into Cvalue
float Cvalue = 0;
// Thread row and column within Csub
int row = threadIdx.y;
int col = threadIdx.x;
// Loop over all the sub-matrices of A and B that are
// required to compute Csub
// Multiply each pair of sub-matrices together
// and accumulate the results
for (int m = 0; m < (A.width / BLOCK_SIZE); ++m) {
// Get sub-matrix Asub of A
Matrix Asub = GetSubMatrix(A, blockRow, m);
// Get sub-matrix Bsub of B
Matrix Bsub = GetSubMatrix(B, m, blockCol);
// Shared memory used to store Asub and Bsub respectively
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
// Load Asub and Bsub from device memory to shared memory
// Each thread loads one element of each sub-matrix
As[row][col] = GetElement(Asub, row, col);
Bs[row][col] = GetElement(Bsub, row, col);
// Synchronize to make sure the sub-matrices are loaded
// before starting the computation
__syncthreads();
// Multiply Asub and Bsub together
for (int e = 0; e < BLOCK_SIZE; ++e)
Cvalue += As[row][e] * Bs[e][col];
// Synchronize to make sure that the preceding
// computation is done before loading two new
// sub-matrices of A and B in the next iteration
__syncthreads();
}
// Write Csub to device memory
// Each thread writes one element
SetElement(Csub, row, col, Cvalue);
}
|
| cuda 5 preview (0) | 2012.06.02 |
|---|---|
| nvidia ion cuda core와 h.264 library (0) | 2012.05.22 |
| Interoperability (상호운용성) (0) | 2012.05.04 |
| cuda 내장변수 (0) | 2012.04.30 |
| kernel block 과 thread (0) | 2012.04.26 |
| 3.2.11 Graphics Interoperability Some resources from OpenGL and Direct3D may be mapped into the address space of CUDA, either to enable CUDA to read data written by OpenGL or Direct3D, or to enable CUDA to write data for consumption by OpenGL or Direct3D. 3.2.11 그래픽 상호운용성 OpenGL이나 Direct3D에 의해 쓰여진 데이터를 CUDA에서 읽도록 허용 하거나 OpenGL 이나 Direct3D에 의해서 사용(소비)될 데이터를 CUDA가 쓸 수 있도록 OpenGL과 Direct3D로 부터의 몇몇 자원들은 CUDA의 주소 공간에 할당(연관/매핑)되어질수 있다. |
| nvidia ion cuda core와 h.264 library (0) | 2012.05.22 |
|---|---|
| CUDA API 메모리 종류 (0) | 2012.05.18 |
| cuda 내장변수 (0) | 2012.04.30 |
| kernel block 과 thread (0) | 2012.04.26 |
| cuda 4.2 devicequey (0) | 2012.04.23 |
| CUDA API 메모리 종류 (0) | 2012.05.18 |
|---|---|
| Interoperability (상호운용성) (0) | 2012.05.04 |
| kernel block 과 thread (0) | 2012.04.26 |
| cuda 4.2 devicequey (0) | 2012.04.23 |
| cuda 4.2 released (0) | 2012.04.22 |
| // Invoke kernel
int N = 50000;
int threadsPerBlock = 256;
int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
VecAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, N);
|
| Device 0: "GeForce 8800 GT" CUDA Driver Version: 3.20 CUDA Runtime Version: 3.10 CUDA Capability Major revision number: 1 CUDA Capability Minor revision number: 1 Total amount of global memory: 536543232 bytes Number of multiprocessors: 14 Number of cores: 112 Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 16384 bytes Total number of registers available per block: 8192 Warp size: 32 Maximum number of threads per block: 512 Maximum sizes of each dimension of a block: 512 x 512 x 64 Maximum sizes of each dimension of a grid: 65535 x 65535 x 1 2011/01/02 - [Programming/openCL / CUDA] - deviceQuery on 8600GT 512MB + CUDA 하드웨어 구조 |
| dim3 blocksPerGrid(1,1);
dim3 threadsPerBlock(8,8,8);
이 코드는 8*8*8 = 512로 쓰레드의 최대 갯수를 넘지 않아 실행이 되지만 이 코드는 9*9*9 = 729로 쓰레드의 최대 갯수를 넘어 실행이 되지 않고 오류가 발생한다.
dim3 blocksPerGrid(1,1);
dim3 threadsPerBlock(9,9,9);
2011/01/22 - [Programming/openCL / CUDA] - CUDA 관련 해외글 |
| Interoperability (상호운용성) (0) | 2012.05.04 |
|---|---|
| cuda 내장변수 (0) | 2012.04.30 |
| cuda 4.2 devicequey (0) | 2012.04.23 |
| cuda 4.2 released (0) | 2012.04.22 |
| CUDA 장치별 cuda core 갯수 (0) | 2012.04.09 |
| C:\ProgramData\NVIDIA Corporation\NVIDIA GPU Computing SDK 4.2\C\bin\win32\Release>deviceQuery.exe
[deviceQuery.exe] starting...
deviceQuery.exe Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Found 1 CUDA Capable device(s)
Device 0: "GeForce 8800 GT"
CUDA Driver Version / Runtime Version 4.2 / 4.2
CUDA Capability Major/Minor version number: 1.1
Total amount of global memory: 512 MBytes (536870912 bytes)
(14) Multiprocessors x ( 8) CUDA Cores/MP: 112 CUDA Cores
GPU Clock rate: 1500 MHz (1.50 GHz)
Memory Clock rate: 900 Mhz
Memory Bus Width: 256-bit
Max Texture Dimension Size (x,y,z) 1D=(8192), 2D=(65536,32768), 3D=(2048,2048,2048)
Max Layered Texture Size (dim) x layers 1D=(8192) x 512, 2D=(8192,8192) x 512
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 16384 bytes
Total number of registers available per block: 8192
Warp size: 32
Maximum number of threads per multiprocessor: 768
Maximum number of threads per block: 512
Maximum sizes of each dimension of a block: 512 x 512 x 64
Maximum sizes of each dimension of a grid: 65535 x 65535 x 1
Maximum memory pitch: 2147483647 bytes
Texture alignment: 256 bytes
Concurrent copy and execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Concurrent kernel execution: No
Alignment requirement for Surfaces: Yes
Device has ECC support enabled: No
Device is using TCC driver mode: No
Device supports Unified Addressing (UVA): No
Device PCI Bus ID / PCI location ID: 2 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 4.2, CUDA Runtime Version = 4.2, NumDevs = 1, Device = GeForce 8800 GT
[deviceQuery.exe] test results...
PASSED
|
| cuda 내장변수 (0) | 2012.04.30 |
|---|---|
| kernel block 과 thread (0) | 2012.04.26 |
| cuda 4.2 released (0) | 2012.04.22 |
| CUDA 장치별 cuda core 갯수 (0) | 2012.04.09 |
| AMD APP SDK 예제 컴파일 (0) | 2012.03.12 |
| kernel block 과 thread (0) | 2012.04.26 |
|---|---|
| cuda 4.2 devicequey (0) | 2012.04.23 |
| CUDA 장치별 cuda core 갯수 (0) | 2012.04.09 |
| AMD APP SDK 예제 컴파일 (0) | 2012.03.12 |
| openCL - ATI APP SDK (0) | 2012.03.11 |
| Multi Processor | Cores | Total | |
| 8800 GT | 14 | 8 | 112 |
| 8800 GTX | 16 | 8 | 128 |
| GTX 480 | 15 | 32 | 480 |
| cuda 4.2 devicequey (0) | 2012.04.23 |
|---|---|
| cuda 4.2 released (0) | 2012.04.22 |
| AMD APP SDK 예제 컴파일 (0) | 2012.03.12 |
| openCL - ATI APP SDK (0) | 2012.03.11 |
| ATI Stream 하드웨어 요구사항 (0) | 2011.10.07 |
| C:\Users\minimonk\Documents\AMD APP\samples\opencl\bin\x86>NBody.exe
Platform 0 : Advanced Micro Devices, Inc.
GPU not found. Falling back to CPU device
Platform found : Advanced Micro Devices, Inc.
Selected Platform Vendor : Advanced Micro Devices, Inc.
Device 0 : AMD Athlon(tm) 64 X2 Dual Core Processor 4600+ Device ID is 014A14F8 |
| cuda 4.2 released (0) | 2012.04.22 |
|---|---|
| CUDA 장치별 cuda core 갯수 (0) | 2012.04.09 |
| openCL - ATI APP SDK (0) | 2012.03.11 |
| ATI Stream 하드웨어 요구사항 (0) | 2011.10.07 |
| CUDA processor roadmap / CUDA SDK 4.0 (1) | 2011.07.03 |
|
