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@HaomingJiang 2017-11-22T06:40:56.000000Z 字数 7523 阅读 1318

CSE6230 Exercise 06

Haoming Jiang

Performance Assessment Script

  1.   PetscLogDouble v1,v2,elapsed_time;
  2.   v1 = 0;
  3.   for (i = 0; i < numSteps; i++) {
  4.     if (!(i % 1000)) {
  5.       ierr = PetscGetTime(&v2);CHKERRQ(ierr);
  6.       elapsed_time = v2-v1;
  7.       h_globalAvg = 0.;
  8.       cerr = cudaMemcpyToSymbol(globalAvg, &h_globalAvg, sizeof(float)); CUDA_CHK(cerr);
  9.       average_distance<<<grid, block>>>(x, xInit);
  10.       cerr = cudaDeviceSynchronize(); CUDA_CHK(cerr);
  11.       cerr = cudaMemcpyFromSymbol(&h_globalAvg, globalAvg, sizeof(float)); CUDA_CHK(cerr);
  12.       ierr = PetscPrintf(comm, "Average distance traveled at time %g: %g; Petsctimer: %g\n", i * h_dt, (double) h_globalAvg, elapsed_time); CHKERRQ(ierr);
  13.       ierr = PetscGetTime(&v1);CHKERRQ(ierr);
  14.     }
  15.     cerr = cudaMemset(forces, 0, h_dim * h_numParticles * sizeof(PetscReal)); CUDA_CHK(cerr);
  16.     compute_forces<<<grid, block>>>(x, forces);
  17.     sum_noise_and_forces<<<grid, block>>>(x, forces, randState);
  18.     cerr = cudaDeviceSynchronize(); CUDA_CHK(cerr);
  19.   }
  20.   if ((i % 1000) != 1) {
  21.     ierr = PetscGetTime(&v2);CHKERRQ(ierr);
  22.     elapsed_time = v2-v1;
  23.     h_globalAvg = 0.;
  24.     cerr = cudaMemcpyToSymbol(globalAvg, &h_globalAvg, sizeof(float)); CUDA_CHK(cerr);
  25.     average_distance<<<grid, block>>>(x, xInit);
  26.     cerr = cudaDeviceSynchronize(); CUDA_CHK(cerr);
  27.     cerr = cudaMemcpyFromSymbol(&h_globalAvg, globalAvg, sizeof(float)); CUDA_CHK(cerr);
  28.     ierr = PetscPrintf(comm, "Average distance traveled at time %g: %g; Petsctimer: %g\n", i * h_dt, (double) h_globalAvg, elapsed_time); CHKERRQ(ierr);
  29.     ierr = PetscGetTime(&v1);CHKERRQ(ierr);
  30.   }
  1. #SBATCH  -J ex06-k80                     # Job name
  2. #SBATCH  -p GPU-shared                   # Queue (RM, RM-shared, GPU, GPU-shared)
  3. #SBATCH  -N 1                            # Number of nodes
  4. #SBATCH --gres=gpu:k80:1                 # GPU type and amount
  5. #SBATCH  -t 00:10:00                     # Time limit hrs:min:sec
  6. #SBATCH  -o ex06-k80-%j.out              # Standard output and error log
  8. module use /home/tisaac/opt/modulefiles
  9. module load petsc/cse6230-double
  10. module load cuda
  12. export PGI_ACC_TIME=1
  14. make ex06
  16. git rev-parse HEAD
  18. git diff-files
  20. nvprof ./ex06 -num_steps 1000

Output of Performance Script

  1. ==31674== NVPROF is profiling process 31674, command: ./ex06 -num_steps 1000
  2. Testing 10000 particles in 3 dimensions with steric repulsion:
  3.   particle mass:   1.
  4.   particle radius: 1.
  5.   k_repulsion:     100.
  6.   box length:      68.
  7.   time step:       0.0001
  8.   number of steps: 1000.
  9. Average distance traveled at time 0.: 0.;
  10.  Petsctimer: 9.53674e-07
  11. Average distance traveled at time 0.1: 0.775785;
  12.  Petsctimer: 62.338
  13. ==31674== Profiling application: ./ex06 -num_steps 1000
  14. ==31674== Profiling result:
  15. Time(%)      Time     Calls       Avg       Min       Max  Name
  16.  99.91%  61.8745s      1000  61.874ms  54.585ms  62.356ms  compute_forces(double*, double*)
  17.   0.08%  48.273ms      1000  48.272us  19.872us  74.815us  sum_noise_and_forces(double*, double*, curandStateXORWOW*)
  18.   0.01%  7.6320ms         1  7.6320ms  7.6320ms  7.6320ms  setup_kernel(curandStateXORWOW*, unsigned long)
  19.   0.00%  1.1592ms      1000  1.1590us  1.0550us  11.616us  [CUDA memset]
  20.   0.00%  772.92us         2  386.46us  370.55us  402.36us  average_distance(double*, double*)
  21.   0.00%  46.592us         1  46.592us  46.592us  46.592us  initialize_points(double*, curandStateXORWOW*)
  22.   0.00%  14.080us        11  1.2800us  1.1840us  1.7280us  [CUDA memcpy HtoD]
  23.   0.00%  6.3030us         1  6.3030us  6.3030us  6.3030us  [CUDA memcpy DtoD]
  24.   0.00%  5.9520us         2  2.9760us  2.9760us  2.9760us  [CUDA memcpy DtoH]
  26. ==31674== API calls:
  27. Time(%)      Time     Calls       Avg       Min       Max  Name
  28.  98.09%  61.9910s      1002  61.867ms  361.17us  66.332ms  cudaDeviceSynchronize
  29.   1.35%  851.75ms         8  106.47ms  6.8670us  850.27ms  cudaFree
  30.   0.29%  183.48ms      1000  183.48us  10.400us  4.1659ms  cudaMemset
  31.   0.22%  139.96ms      2004  69.838us  5.3140us  18.368ms  cudaLaunch
  32.   0.02%  11.414ms        10  1.1414ms  5.8890us  7.6609ms  cudaMemcpyToSymbol
  33.   0.02%  10.214ms      5008  2.0390us     136ns  1.7300ms  cudaSetupArgument
  34.   0.01%  5.3405ms      2004  2.6640us     166ns  151.62us  cudaConfigureCall
  35.   0.00%  1.7006ms       178  9.5530us     130ns  351.41us  cuDeviceGetAttribute
  36.   0.00%  1.4692ms         7  209.88us  8.9870us  561.43us  cudaMalloc
  37.   0.00%  823.24us         1  823.24us  823.24us  823.24us  cudaGetDeviceProperties
  38.   0.00%  601.29us         2  300.65us  214.85us  386.44us  cuDeviceTotalMem
  39.   0.00%  152.27us         2  76.137us  4.9450us  147.33us  cudaThreadSynchronize
  40.   0.00%  145.37us         2  72.686us  71.796us  73.577us  cuDeviceGetName
  41.   0.00%  49.641us         2  24.820us  23.784us  25.857us  cudaMemcpyFromSymbol
  42.   0.00%  47.484us         2  23.742us  21.850us  25.634us  cudaMemcpy
  43.   0.00%  12.186us        16     761ns     456ns  3.5290us  cudaEventCreateWithFlags
  44.   0.00%  10.923us        16     682ns     418ns  1.9100us  cudaEventDestroy
  45.   0.00%  7.9800us         1  7.9800us  7.9800us  7.9800us  cudaSetDeviceFlags
  46.   0.00%  6.0330us        11     548ns     282ns  2.2010us  cudaDeviceGetAttribute
  47.   0.00%  3.2600us         4     815ns     250ns  2.1460us  cuDeviceGetCount
  48.   0.00%  2.9700us         1  2.9700us  2.9700us  2.9700us  cudaGetDevice
  49.   0.00%  1.3480us         4     337ns     217ns     618ns  cuDeviceGet
  50.   0.00%     537ns         1     537ns     537ns     537ns  cuInit
  51.   0.00%     488ns         1     488ns     488ns     488ns  cuDriverGetVersion

Planned changes

We can use share memory to store the location curent praticle. So that it does not need to read from global memory evry time. It somehow dealwith the coalesced memory access problem.

Listing of compute_forces

  2. /* Compute the forces between two particles:
  3.    - one thread to update each particle */
  4. __global__ void compute_forces(PetscReal *x, PetscReal *forces)
  5. {
  6.   int lid           = threadIdx.x;
  7.   int id            = threadIdx.x + blockIdx.x * blockDim.x;
  8.   int gridSize      = blockDim.x * gridDim.x;
  9.   int i, j, k;
  10.   __shared__ double *localx;
  11.   if (!lid) {
  12.     localx = (double *) malloc(blockDim.x * dim * sizeof(double));
  13.   }
  14.   __syncthreads();
  17.   for (i = id; i < numParticles; i += gridSize) {
  18.     for (k = 0; k < dim; k++)
  19.     {
  20.       localx[k*blockDim.x+lid] = x[dim * i + k];
  21.     }
  23.     for (j = 0; j < numParticles; j++) {
  24.       double dist2 = 0.;
  26.       if (i == j) continue;
  27.       for (k = 0; k < dim; k++) {
  28.         double disp = remainder(localx[k*blockDim.x+lid] - x[dim * j + k],L);
  30.         dist2 += disp * disp;
  31.       }
  32.       if (dist2 < 4. * a * a) {
  33.         double dist = sqrt(dist2);
  34.         double f = krepul * (2. - dist);
  36.         for (k = 0; k < dim; k++) {
  37.           double disp = remainder(localx[k*blockDim.x+lid] - x[dim * j + k],L);
  39.           forces[dim * i + k] += f * disp / dist;
  40.         }
  41.       }
  42.     }
  43.   }
  45.   if (!lid) {
  46.     free(localx);
  47.   }
  49. }

Output of Performance Script

We can see the significant improvement, i.e. reduce the time from 62s to 27s.

  1. ==10405== NVPROF is profiling process 10405, command: ./ex06 -num_steps 1000
  2. Testing 10000 particles in 3 dimensions with steric repulsion:
  3.   particle mass:   1.
  4.   particle radius: 1.
  5.   k_repulsion:     100.
  6.   box length:      68.
  7.   time step:       0.0001
  8.   number of steps: 1000.
  9. Average distance traveled at time 0.: 0.;
  10.  Petsctimer: 9.53674e-07
  11. Average distance traveled at time 0.1: 0.775785;
  12.  Petsctimer: 27.553
  13. ==10405== Profiling application: ./ex06 -num_steps 1000
  14. ==10405== Profiling result:
  15. Time(%)      Time     Calls       Avg       Min       Max  Name
  16.  99.86%  27.4698s      1000  27.470ms  27.355ms  27.578ms  compute_forces(double*, double*)
  17.   0.12%  33.281ms      1000  33.280us  22.144us  47.135us  sum_noise_and_forces(double*, double*, curandStateXORWOW*)
  18.   0.01%  3.9751ms         1  3.9751ms  3.9751ms  3.9751ms  setup_kernel(curandStateXORWOW*, unsigned long)
  19.   0.00%  1.1581ms      1000  1.1580us  1.1200us  1.6320us  [CUDA memset]
  20.   0.00%  424.95us         2  212.48us  209.28us  215.68us  average_distance(double*, double*)
  21.   0.00%  29.696us         1  29.696us  29.696us  29.696us  initialize_points(double*, curandStateXORWOW*)
  22.   0.00%  14.656us        11  1.3320us  1.2160us  1.7600us  [CUDA memcpy HtoD]
  23.   0.00%  6.0800us         2  3.0400us  3.0080us  3.0720us  [CUDA memcpy DtoH]
  24.   0.00%  3.6800us         1  3.6800us  3.6800us  3.6800us  [CUDA memcpy DtoD]
  26. ==10405== API calls:
  27. Time(%)      Time     Calls       Avg       Min       Max  Name
  28.  97.37%  27.5102s      1002  27.455ms  212.97us  27.721ms  cudaDeviceSynchronize
  29.   2.36%  667.97ms         8  83.496ms  20.190us  666.12ms  cudaFree
  30.   0.14%  40.756ms      2004  20.337us  5.2480us  17.157ms  cudaLaunch
  31.   0.07%  18.939ms      1000  18.939us  9.0580us  236.52us  cudaMemset
  32.   0.03%  7.9732ms        10  797.32us  6.2620us  3.9868ms  cudaMemcpyToSymbol
  33.   0.01%  2.3126ms       178  12.992us     127ns  654.55us  cuDeviceGetAttribute
  34.   0.00%  1.2667ms         7  180.95us  8.3140us  334.40us  cudaMalloc
  35.   0.00%  1.2197ms      5008     243ns     135ns  10.188us  cudaSetupArgument
  36.   0.00%  943.42us         1  943.42us  943.42us  943.42us  cudaGetDeviceProperties
  37.   0.00%  830.14us      2004     414ns     166ns  10.570us  cudaConfigureCall
  38.   0.00%  478.86us         2  239.43us  220.98us  257.88us  cuDeviceTotalMem
  39.   0.00%  183.66us         2  91.828us  90.457us  93.199us  cuDeviceGetName
  40.   0.00%  73.310us         2  36.655us  25.746us  47.564us  cudaMemcpyFromSymbol
  41.   0.00%  47.385us         2  23.692us  21.845us  25.540us  cudaMemcpy
  42.   0.00%  23.497us        16  1.4680us  1.0820us  3.4840us  cudaEventDestroy
  43.   0.00%  22.304us         2  11.152us  10.900us  11.404us  cudaThreadSynchronize
  44.   0.00%  12.378us        16     773ns     458ns  3.5040us  cudaEventCreateWithFlags
  45.   0.00%  7.7670us         1  7.7670us  7.7670us  7.7670us  cudaSetDeviceFlags
  46.   0.00%  5.5410us        11     503ns     271ns  1.9720us  cudaDeviceGetAttribute
  47.   0.00%  3.3300us         4     832ns     213ns  2.3060us  cuDeviceGetCount
  48.   0.00%  2.7160us         1  2.7160us  2.7160us  2.7160us  cudaGetDevice
  49.   0.00%  1.4270us         4     356ns     275ns     590ns  cuDeviceGet
  50.   0.00%     591ns         1     591ns     591ns     591ns  cuDriverGetVersion
  51.   0.00%     572ns         1     572ns     572ns     572ns  cuInit
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