tesla cuda

Cuda register array resolution, cuda register About cuda register array When performing Parallel Optimization on some algorithms based on cuda, in order to improve the running speed of the algorithm as much as possible, sometimes we want to use register arrays to make the algorithm fly fast, but the effect is always u

Win10 with CMake 3.5.2 and vs update1 compiling GPU version (Cuda 8.0, CUDNN v5 for Cuda 8.0) Open compile release and debug version with VS 2015 See the example on the net there are three inside the project Folders include (Include directories containing Mxnet,dmlc,mshadow)Lib (contains Libmxnet.dll, libmxnet.lib, put it in vs. compiled)Python (contains a mxnet,setup.py, and build, but the build contains t

Today we have a few gains, successfully running the array summation code: Just add the number of n sumEnvironment: cuda5.0,vs2010#include "cuda_runtime.h"#include "Device_launch_parameters.h"#include cudaerror_t Addwithcuda (int *c, int *a);#define TOTALN 72120#define Blocks_pergrid 32#define THREADS_PERBLOCK 64//2^8__global__ void Sumarray (int *c, int *a)//, int *b){__shared__ unsigned int mycache[threads_perblock];//sets the shared memory within each block threadsperblock==blockdim.xint i = t

Install cuda6.5 + vs2012, the operating system is win8.1 version, first of all the next GPU-Z detected a bit: It can be seen that this video card is a low-end configuration, the key is to look at two: Shaders = 384, also known as Sm, or the number of core/stream processors. The larger the number, the more parallel threads are executed, and the larger the computing workload per unit time. Buswidth = 64bit. The larger the value, the faster the data processing speed. Next let's take a look at the

Install and configure CUDA in Ubuntu 14.04 First, I installed Ubuntu 14.04.1. 1. Pre-Check Check the system as shown in reference 1. Run the following command: :~ $ Lspci | grep-I nvidia. 0 3D controller: NVIDIA Corporation GK110GL [Tesla K20c] (rev a1). 0 VGA compatible controller: NVIDIA Corporation gk0000gl [Quadro K4000] (rev a1). 1 Audio device: NVIDIA Corporation GK106 HDMI Audio Controller (rev a1) T

First, I installed Ubuntu14.04.1. 1. Pre-check the system as shown in reference 1. Run the following command ::~ $ Lspci | grep-invidia03: 00.03 Dcontroller: nvidiaconfigurationgk110gl [TeslaK20c] (reva1). 0 VGAcompatiblecontroller: NVIDIAC First, I installed Ubuntu 14.04.1. 1. Pre-Check Check the system as shown in reference 1. Run the following command: :~ $ Lspci | grep-I nvidia. 0 3D controller: NVIDIA Corporation GK110GL [Tesla K20c] (rev a1). 0

CUDA Installation Guide on Linux systems Applicable operating system Fedora 7,8,9,10 Redhat Enterprise 3.x,4.x,5.x SUSE Linux Enterprise Desktop 10-sp1,10.2,11.0 OpenSUSE 10.1,10.2,10.3,11.0,11.1 Ubuntu 7.04, 7.10.,8.04,8.10,9.04 -------------------------------------------------------------------------------- Download and operating system matching Driver, SDK, Tookit Address: http://www.nvidia.com/object/cuda_get.html ---------------------------------

Cuda Memory Model: GPU chip: Register, shared memory; Onboard memory: local memory, constant memory, texture memory, texture memory, global memory; Host memory: host memory, pinned memory. Register: extremely low access latency; Basic Unit: register file (32bit/each) Computing power 1.0/1.1 hardware: 8192/Sm; Computing power 1.2/1.3 hardware: 16384/Sm; The register occupied by each thread is limited. Do not assign too many private variables to it dur

In this paper, the basic concepts of CUDA parallel programming are illustrated by the vector summation operation. The so-called vector summation is the addition of the corresponding element 22 in the two array data, and the result is saved in the third array. As shown in the following:1. CPU-based vector summation:The code is simple:#include the use of the while loop above is somewhat complex, but it is intended to allow the code to run concurrently o

support for NVIDIA libraries and using the resulting binaries to speed up video Encodin G/decoding. FFmpeg supports following functionality accelerated by video hardware on NVIDIA gpus:hardware-accelerated encoding of H.2 hardware-accelerated decoding** of H. hevc*, HEVC, VP9, VP8, MPEG2, and mpeg4* granular control over encoding SE Ttings such as encoding preset, rate control and other video quality parameters Create high-performance end-to-end Hardwar e-accelerated video processing, 1:n encod

thread in the block is sent to an SP; When the number of blocks is several times the number of processing cores, the GPU computing capability can be fully utilized: if it is too small, it cannot reflect its computing speed advantage over the traditional method. Thread: has its own private register and local memory; Threads in the same block can communicate with each other through the shared storage and synchronization mechanism. Actual running unit: Warp (thread bundle), which is determined b

Two-dimensional FFT in cuda-cufftExecC2C, cuda-cufftexecc2c #include

=2; - float*x_h, *x_d, *y_h, *Y_d; -X_h = (float*) malloc (n *sizeof(float)); -Y_h = (float*) malloc (n *sizeof(float)); + for(inti =0; I ) - { +X_h[i] = (float) I; AY_h[i] =1.0; at } -Cudamalloc (x_d, n *sizeof(float)); -Cudamalloc (y_d, n *sizeof(float)); -cudamemcpy (X_d, X_h, n *sizeof(float), cudamemcpyhosttodevice); -cudamemcpy (Y_d, Y_h, n *sizeof(float), cudamemcpyhosttodevice); -Saxpy 1, ->>>(A, x_d, Y_d, n); incudamemcpy (Y_h, Y_d, n *sizeof(float), cudamemcpydeviceto