- 被加速的C/C++应用程序的异步流和可视化分
- 利用基本的 CUDA 内存管理技术来优化加速应用程序
- 被加速的C/C++应用程序的异步流和可视化分析
完成第三步中的GPU task便可拿到该课程的证书,完成作业。
我的代码思路是:
- 将bodyForce函数改为核函数,在GPU上运行。因为多个epoch必须按序执行,所以无法使用并发的cuda流,默认的串行流行为可以完成任务。
- 将bodyForce执行结束后的for循环改为核函数。
- 其他技巧就是第二个学习文档提到的块数和线程数设置
为了将博客升到6级,我得多写点博文了。
以下是在速度和正确性上都通过测试的cuda代码。
#include
#include
#include
#include "timer.h"
#include "check.h"
#define SOFTENING 1e-9f
/*
* Each body contains x, y, and z coordinate positions,
* as well as velocities in the x, y, and z directions.
*/
typedef struct { float x, y, z, vx, vy, vz; } Body;
/*
* Do not modify this function. A constraint of this exercise is
* that it remain a host function.
*/
void randomizeBodies(float *data, int n) {
for (int i = 0; i < n; i++) {
data[i] = 2.0f * (rand() / (float)RAND_MAX) - 1.0f;
}
}
/*
* This function calculates the gravitational impact of all bodies in the system
* on all others, but does not update their positions.
*/
__global__
void bodyForce(Body *p, float dt, int n) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride) {
float Fx = 0.0f; float Fy = 0.0f; float Fz = 0.0f;
for (int j = 0; j < n; j++) {
float dx = p[j].x - p[i].x;
float dy = p[j].y - p[i].y;
float dz = p[j].z - p[i].z;
float distSqr = dx*dx + dy*dy + dz*dz + SOFTENING;
float invDist = rsqrtf(distSqr);
float invDist3 = invDist * invDist * invDist;
Fx += dx * invDist3; Fy += dy * invDist3; Fz += dz * invDist3;
}
p[i].vx += dt*Fx;
p[i].vy += dt*Fy;
p[i].vz += dt*Fz;
}
}
__global__ void add(Body*p, float dt,int n) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride) {
p[i].x += p[i].vx*dt;
p[i].y += p[i].vy*dt;
p[i].z += p[i].vz*dt;
}
}
int main(const int argc, const char** argv) {
int deviceId;
int numberOfSMs;
cudaGetDevice(&deviceId);
cudaDeviceGetAttribute(&numberOfSMs, cudaDevAttrMultiProcessorCount, deviceId);
/*
* Do not change the value for `nBodies` here. If you would like to modify it,
* pass values into the command line.
*/
int nBodies = 2<<11;
int salt = 0;
if (argc > 1) nBodies = 2<<atoi(argv[1]);
/*
* This salt is for assessment reasons. Tampering with it will result in automatic failure.
*/
if (argc > 2) salt = atoi(argv[2]);
const float dt = 0.01f; // time step
const int nIters = 10; // simulation iterations
int bytes = nBodies * sizeof(Body);
float *buf;
cudaMallocManaged(&buf, bytes);
//cudaMemPrefetchAsync(buf, bytes, deviceId);
Body *p = (Body*)buf;
/*
* As a constraint of this exercise, `randomizeBodies` must remain a host function.
*/
randomizeBodies(buf, 6 * nBodies); // Init pos / vel data
size_t threadsPerBlock = 256;
size_t numberOfBlocks = 32 * numberOfSMs;
double totalTime = 0.0;
/*
* This simulation will run for 10 cycles of time, calculating gravitational
* interaction amongst bodies, and adjusting their positions to reflect.
*/
/*******************************************************************/
// Do not modify these 2 lines of code.
for (int iter = 0; iter < nIters; iter++) {
StartTimer();
/*******************************************************************/
/*
* You will likely wish to refactor the work being done in `bodyForce`,
* as well as the work to integrate the positions.
*/
bodyForce<<< numberOfBlocks, threadsPerBlock >>>(p, dt, nBodies); // compute interbody forces
cudaDeviceSynchronize();
add<<< numberOfBlocks, threadsPerBlock >>>(p, dt, nBodies);
// Do not modify the code in this section.
const double tElapsed = GetTimer() / 1000.0;
totalTime += tElapsed;
}
cudaDeviceSynchronize();
double avgTime = totalTime / (double)(nIters);
float billionsOfOpsPerSecond = 1e-9 * nBodies * nBodies / avgTime;
#ifdef ASSESS
checkPerformance(buf, billionsOfOpsPerSecond, salt);
#else
checkAccuracy(buf, nBodies);
printf("%d Bodies: average %0.3f Billion Interactions / second\n", nBodies, billionsOfOpsPerSecond);
salt += 1;
#endif
/*******************************************************************/
/*
* Feel free to modify code below.
*/
cudaFree(buf);
}