gpu

问题 Why am I seeing different result on GPU compare to sequential CPU? import numpy from numba import cuda from functools import reduce A = (numpy.arange(100, dtype=numpy.float64)) + 1 cuda.reduce(lambda a, b: a + b * 20)(A) # result 12952749821.0 reduce(lambda a, b: a + b * 20, A) # result 100981.0 import numba numba.__version__ # '0.34.0+5.g1762237' Similar behavior happens when using Java Stream API to parallelize reduction on CPU: int n = 10; float inputArray[] = new float[n]; ArrayList<Float

问题 Why am I seeing different result on GPU compare to sequential CPU? import numpy from numba import cuda from functools import reduce A = (numpy.arange(100, dtype=numpy.float64)) + 1 cuda.reduce(lambda a, b: a + b * 20)(A) # result 12952749821.0 reduce(lambda a, b: a + b * 20, A) # result 100981.0 import numba numba.__version__ # '0.34.0+5.g1762237' Similar behavior happens when using Java Stream API to parallelize reduction on CPU: int n = 10; float inputArray[] = new float[n]; ArrayList<Float

问题 In the following code, I try to search over different hyper-parameters of xgboost. param_test1 = { 'max_depth':list(range(3,10,2)), 'min_child_weight':list(range(1,6,2)) } predictors = [x for x in train_data.columns if x not in ['target', 'id']] gsearch1 = GridSearchCV(estimator=XGBClassifier(learning_rate =0.1, n_estimators=100, max_depth=5, min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= 'binary:logistic', n_jobs=4, scale_pos_weight=1, seed=27, kvargs={'tree

问题 What is the internal operation that allows nvidia-smi fetch the hardware level details? The tool executes even when some process is already running on the GPU device and gets the utilization details, name and id of the process etc. Is it possible to develop such a tool at the user level? How is NVML related? 回答1: Nvidia-smi is a thin wrapper around NVML. You can code with NVML with help of SDK contained in Tesla Deployment Kit. Everything that can be done with nvidia-smi can be queried

问题 What is the internal operation that allows nvidia-smi fetch the hardware level details? The tool executes even when some process is already running on the GPU device and gets the utilization details, name and id of the process etc. Is it possible to develop such a tool at the user level? How is NVML related? 回答1: Nvidia-smi is a thin wrapper around NVML. You can code with NVML with help of SDK contained in Tesla Deployment Kit. Everything that can be done with nvidia-smi can be queried

问题 I am using numba cuda to calculate a function. The code is simply to add up all the values into one result, but numba cuda gives me a different result from numpy. numba code import math def numba_example(number_of_maximum_loop,gs,ts,bs): from numba import cuda result = cuda.device_array([3,]) @cuda.jit(device=True) def BesselJ0(x): return math.sqrt(2/math.pi/x) @cuda.jit def cuda_kernel(number_of_maximum_loop,result,gs,ts,bs): i = cuda.grid(1) if i < number_of_maximum_loop: result[0] +=

问题 I am using numba cuda to calculate a function. The code is simply to add up all the values into one result, but numba cuda gives me a different result from numpy. numba code import math def numba_example(number_of_maximum_loop,gs,ts,bs): from numba import cuda result = cuda.device_array([3,]) @cuda.jit(device=True) def BesselJ0(x): return math.sqrt(2/math.pi/x) @cuda.jit def cuda_kernel(number_of_maximum_loop,result,gs,ts,bs): i = cuda.grid(1) if i < number_of_maximum_loop: result[0] +=

问题 We are looking for some advice with slurm salloc gpu allocations. Currently, given: % salloc -n 4 -c 2 -gres=gpu:1 % srun env | grep CUDA CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 However, we desire more than just device 0 to be used. Is there a way to specify an salloc with srun/mpirun to get the following? CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=1 CUDA_VISIBLE_DEVICES=2 CUDA_VISIBLE_DEVICES=3 This is desired such that each task gets 1

问题 We are looking for some advice with slurm salloc gpu allocations. Currently, given: % salloc -n 4 -c 2 -gres=gpu:1 % srun env | grep CUDA CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=0 However, we desire more than just device 0 to be used. Is there a way to specify an salloc with srun/mpirun to get the following? CUDA_VISIBLE_DEVICES=0 CUDA_VISIBLE_DEVICES=1 CUDA_VISIBLE_DEVICES=2 CUDA_VISIBLE_DEVICES=3 This is desired such that each task gets 1

问题 I would like to do a large analysis using bootstrapping. I saw that the speed of bootstrapping is increased using parallel computing as in the following code: Parallel computing # detect number of cpu library(parallel) detectCores() library(boot) # boot function --> mean bt.mean <- function(dat, d){ x <- dat[d] m <- mean(x) return(m) } # obtain confidence intervals # use parallel computing with 4 cpus x <- mtcars$mpg bt <- boot(x, bt.mean, R = 1000, parallel = "snow", ncpus = 4) quantile(bt$t