tesla p40

mnist data set, mainly to examine the performance of the CPU and GPU under different systems. Can see the obvious difference, although the Mnist dataset is very simple, believe that the complex data set, the difference will be greater, UBUNTU+GPU is the only choice.Test Platform 1:I7-4770K/16G/GTX 770/cuda 6.5MNIST Windows8.1 on cpu:620sMNIST Windows8.1 on gpu:190sMNIST Ubuntu 14.04 on cpu:270sMNIST Ubuntu 14.04 on gpu:160sMNIST Ubuntu 14.04 on Gpuwith cudnn:30sCifar10_full on Gpuwihtout cudnn:

the performance of the CPU and GPU under different systems. Can see the obvious difference, although the Mnist dataset is very simple, believe that the complex data set, the difference will be greater, UBUNTU+GPU is the only choice.Test Platform 1 : I7-4770K/16G/GTX 770/cuda 6.5MNIST Windows8.1 on CPU : 620sMNIST Windows8.1 on GPU : 190sMNIST Ubuntu 14.04 on CPU : 270sMNIST Ubuntu 14.04 on GPU : 160sMNIST Ubuntu 14.04 on Gpuwith CuDNN : 101cifar10_full on Gpuwihtout CuDNN : 73m45s = 4428s ( Ite

= My_car.read_odometer () my_car.updata_odometer (My_car.read_) Odometer () My_car.increment_odometer () My_car.read_odometer () class Electriccar (CAR): "", DocString for Electriccar "" Def __init__ (self, make, model, year): ' Initialize the properties of the parent class ' super (Electriccar, self). __init__ (make, model, year) #self . arg = arg My_tesla = Electriccar (' Tesla ', ' Model S ', 2016) print (My_tesla.get_name ())Result :2016

Stored Procedures | issues Only one table is involved: Xkb_treenode The table structure is like this: node_id int//Node ID parentnode_id int//parent Node ID Node_text varchar//node content IsModule bit//whether leaf node The data that is now saved is: node_id parentnode_id Node_text IsModule 1-1 Languages and Literature 0 2-1 Mathematics 0 3-1 Technology 0 4 1 Languages 0 5 1 Foreign Languages 0 6 5 English 0 7 6 Junior English 0 8 7 Tesla Tower 1

TABLE PARTITION. 1.0 data_input_gzsps_pub Tb_im_order_que_gz P35 TABLE PARTITION. 1.0 data_input_gzsps_pub Tb_im_order_que_gz P36 TABLE PARTITION. 1.0 data_input_gzsps_pub Tb_im_order_que_gz P37 TABLE PARTITION. 1.0 data_input_gzsps_pub Tb_im_order_q Ue_gz P38 TABLE PARTITION. 1.0 data_input_gzsps_pub Tb_im_order_que_gz P39 TABLE PARTITION. 1.0 data_input_gzsps_pub Tb_im_order_que_gz

some energy. In my extracurricular search, I am very attracted to the now very famous Tesla Tesla: Tesla is committed to using the most innovative technology to accelerate the development of sustainable transport. Tesla has technically provided an efficient way to achieve sustainable energy supply, reducing global tra

difference will be greater, UBUNTU+GPU is the only choice.Test Platform 1:I7-4770K/16G/GTX 770/cuda 6.5MNIST Windows8.1 on cpu:620sMNIST Windows8.1 on gpu:190sMNIST Ubuntu 14.04 on cpu:270sMNIST Ubuntu 14.04 on gpu:160sMNIST Ubuntu 14.04 on GPUs with cudnn:30sCifar10_full on GPU wihtout cudnn:73m45s = 4428s (iteration 70000)Cifar10_full on GPU with cudnn:20m7s = 1207s (iteration 70000)Test Platform 2: Gigabyte p35x v3,[email protected]/16g/nvidia GTX 980 8GMNIST Ubuntu 15.04 on GPUs with cudnn:

installation are complete, and the following is a simple set of data controls. The experiment originates from the mnist data set, which mainly investigates the performance of CPU and GPU under different systems. Can see the obvious difference. Although the mnist datasets are very easy to believe, the difference is greater and UBUNTU+GPU is the only choice for complex datasets.Test Platform 1 : I7-4770K/16G/GTX 770/cuda 6.5MNIST Windows8.1 on CPU : 620sMNIST Windows8.1 on GPU : 190sMNIST Ubuntu

shopping cart, the requirements are as follows:To print the product details, the user enters the product name and the number of purchases,The product name, price, purchase number added to the shopping list, if the input is empty or other illegal input requires the user to re-enterMsg_dic={'Apple': 10,'Tesla': 100000,'mac': 3000,'Lenovo': 30000,'Chicken': 10,}shopping_cart=[] whileTrue: forKinchMsg_dic:info='Product Name:%s Price:%s'%(k, msg_dic[k])Pr

: $ ./reduceInteger starting reduction at device 0: Tesla M2070with array size 16777216 grid 32768 block 512cpu reduce elapsed 29 ms cpu_sum: 2139353471gpu Neighbored elapsed 11 ms gpu_sum: 2139353471 Consider the if condition in the previous section: If (tid % (2 * stride) = 0) Because this expression is true only for threads with even numbers of IDS, it causes high divergent warps. In the first iteration, only threads with even IDs execute commands

after optimization in float computing. However, considering the parallel performance of multiple CPU cores and Fortran, it is estimated that the ultimate advantage of GPU will not be that great, but it may be only 2-3 times better. For dual-precision computing, because the dual-precision of desktop graphics cards is only 1/8 of the single-precision (tesla computing card is 1/2, but expensive, the latest open puller 110 architecture

I have such a high capital to do the mortgage, this time when found, can be ruled out, This may be more than the efficiency of many industry experts. A manufacturing failure analysis and prediction, millions of times of the sensor signal detection value of the time series analysis, using CNN and RNN modeling, error classification and prediction. A bank bad customer detection, the customer hundreds of in-line savings, consumption, credit characteristics, as well as dozens of of the character

1. Optimize the shopping process, allow users to choose how many items to buy,2. Allow multiple users to log in, the next time you log in, continue to purchase the last balance, you can recharge (each user has a separate save file)3. Allow users to view previous purchase records (record to display the purchase time of the goods)4. Product List grading display, such as:First level menu:1. Home Appliance Class2. Clothing Category3. Mobile Phone class4. Car Class...Select one, Car class, enter 2nd

four to enroll in a group of four team challenge, the result is to take the championship, the bonus 30,000.Later, he finally tired of these cracked to crack go to the game .... He saw the next area:Artificial intelligenceHe ran to a university and wanted to read a PhD in artificial intelligence. There, he read all the most weighty AI papers of his time. But in college, he was once again disappointed.The high-flyers around, all thinking about how to enter the big company after graduation, how to

(Car):def __init__(self, make, model, year): Car.__init__(self, make, model, year) My_tesla= Electriccar ('Tesla','Model S', 2016)Print(My_tesla.get_descriptive_name ())------------------Line----------------------Tesla Model SHere car is Electriccar's "parent" or "superclass", and Electriccar is the "subclass" or "derived class" of car.The code "car.__init__ (self, made, model, year)" Lets Python make the

statementdef stu_register(name, age, course=‘PY‘ ,country=‘CN‘): print("----注册学生信息------") print("姓名:", name) print("age:", age) print("国籍:", country) print("课程:", course) if age > 22: return False else: return Trueregistriation_status = stu_register("王山炮",22,course="PY全栈开发",country=‘JP‘)if registriation_status: print("注册成功")else: print("too old to be a student.")Attention The function stops executing and returns the result as soon as it encount

needs involved are vastly different. L2 and L3 on the vehicle's automatic control ability, although not far away, but L3 allows automatic driving under limited conditions, and it needs to be able to judge the vast majority of the car's surrounding lanes, and at the same time replace the driver to make the various decisions in progress, and the decision-making process requires extremely large computational power behind it. Figure 丨 Mobileye before L2, because the main focus is on the visual comp

http://blog.itpub.net/23057064/viewspace-629236/ Nvidia graphics cards on the market are based on the Tesla architecture, divided into G80, G92, GT200 three series. The Tesla architecture is a processor array with the number of extendable places. Each GT200 GPU consists of 240 stream processors (streaming processor,sp), and each of the 8 stream processors is comprised of one stream multiprocessor (streaming

CUDA (Compute Unified Device Architecture), graphics manufacturer Nvidia launched the computing platform. Cuda™ is a general-purpose parallel computing architecture introduced by NVIDIA, which enables the GPU to solve complex computational problems. It contains the CUDA instruction set architecture (ISA) and the parallel computing engine within the GPU. The computing industry is developing "collaborative processing" from CPU-only "central processing" to CPU and GPU. To create this new paradigm

blockint *idata = g_idata + blockIdx.x*blockDim.x;int *odata = g_odata[blockIdx.x];// stop conditionif (isize == 2 tid == 0) {g_odata[blockIdx.x] = idata[0]+idata[1];return;}// nested invocationint istride = isize>>1;if(istride > 1 tid Compile and run. The following result is run on the Kepler K40: $ nvcc -arch=sm_35 -rdc=true nestedReduce.cu -o nestedReduce -lcudadevrt./nestedReduce starting reduction at device 0: Tesla K40carray 1048576 grid 204