All of the NVIDIA applets (control panel, experience, etc.) accuracy report the GTX 960 and capabilities.
Cuda dim3 gtx 960 driver#
blockDim has the variable type of dim3, which is an 3-component integer vector type that is used to specify dimensions. However, the 347.52 driver supports the GTX 960. As you may notice, we introduced a new CUDA built-in variable blockDim into this code.
Cuda dim3 gtx 960 code#
Import numpy as np import pandas as pd import csv import math, itertools import os import subprocess import matplotlib.pyplot as plt from multiprocessing import Pool, Manager from collections import Counter from stop_words import get_stop_words import natsort from natsort import natsorted from scipy import spatial from scipy.stats import pearsonr, spearmanr from sklearn.svm import SVR, LinearSVR from sklearn.externals import joblib from sklearn.model_selection import cross_val_predict, cross_val_score from sklearn.model_selection import train_test_split, StratifiedKFold from trics import r2_score, f1_score from trics import classification_report, precision_recall_fscore_support from sklearn.feature_extraction.text import CountVectorizer from imblearn.over_sampling import RandomOverSampler from keras.preprocessing import sequence from _utils import to_categorical from keras.models import Sequential, load_model, model_from_json from keras.layers import Dense, Activation, Embedding, Bidirectional, Dropout, LSTM from keras.regularizers import l2 import keras.backend as K from theano import function import warningsġ9 //If true, when there is a gpu malloc or free error, we print the size of allocated memory on the device.Ģ2 //If true, we fill with NAN allocated device memory.Ģ5 //If true, we print out when we free a device pointer, uninitialize aĢ6 //CudaNdarray, or allocate a device pointerĢ9 //If true, we do error checking at the start of functions, to make sure thereģ0 //is not a pre-existing error when the function is called.ģ1 //You probably need to set the environment variableģ2 //CUDA_LAUNCH_BLOCKING=1, and/or modify the CNDA_THREAD_SYNCģ3 //preprocessor macro in cuda_ndarray.cuhĤ8 CudaNdarray_Dimshuffle(PyObject* _unused, PyObject* args) Ĥ9 static PyObject *CudaNdarray_get_shape(CudaNdarray *self, void *closure) ĥ4 * In the test program I'm using, the _outstanding_mallocs decreases with every call.ĥ5 * This suggests there are more free() calls being made than alloc(), but I can't figure out why.Ħ9 table_struct _alloc_size_table ħ4 return device_malloc(size, VERBOSE_DEVICE_MALLOC) ħ9 int initCnmem(int card_number_provided, int card_nb, size_t mem) Ĥ77 decl_k_elemwise_unary_rowmajor(k_elemwise_unary_rowmajor_exp, unary_exp)Ĥ83 //DON'T use directly(if their is other CudaNdarray that point to it, it will cause problem)! use Py_DECREF() insteadĤ85 CudaNdarray_dealloc(CudaNdarray* self)Ĥ89 printf("WARNING:CudaNdarray_dealloc called when there is still active reference to it. GIGABYTE GeForce GTX 1050 Ti 4GB GDDR5 PCI Express 3.0 x16 ATX Video Cards GV-N105TD5-4GD. This 4 lines of code will assign index to the thread so that they can match up with entries in output matrix. Welcome to the Geekbench CUDA Benchmark Chart.
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