If we look at the graphics and results, today's winner is 4 X NVIDIA Tesla V100 GPUs :).The old measuring stick of “ Can It Run Crysis?” doesn’t hold water anymore. My own conclusions based on these resultsĪccording to my observations, in short and simple operations, all GPUs, regardless of GPU video memory and CPU, can finish in a very short and close time.īut in long and laborious calculations, high GPU memory and a good CPU allow it to stand out from other competitors. The machine with 4X NVIDIA Tesla P100 GPU won the race 2-b by a small margin. # run the compiled file, test bas = time. The files are compiled with nvcc (Cuda compiler). Test 2-a, let's first see which GPU will compile the Cuda file named matrixmul.cu. } Test-2a, Performance of GPUs, in seconds : Then I multiplied these matrices with each other. With C ++, I manually allocated two places in the GPU memory (10000 rows and 10000 columns) and assigned values to these reserved areas with loops. Our machine with 4 X NVIDIA Tesla V100 GPU won this race.Ģ. First I multiply matrix a and b and assign it to variable y, then I multiply matrix c and d and assign it to variable z, and finally I multiply matrix y and z and assign it to variable x, and I did this operation 1000 times in total.Ī = torch. I created four matrices with 10000 rows and 10000 columns on the GPU. We have a Tesla T4 GPU with 15 GB of video memory, we also have a 1-core Intel (R) Xeon (R) CPU.ġ.We have a Tesla P100 GPU with 16.2GB of video memory, we also have a 1-core Intel (R) Xeon (R) CPU.We have a Tesla P4 GPU with 7.6GB of video memory, we also have an Intel (R) Xeon (R) CPU with 1 core. Yes, as you can see, we have a machine with 4 Tesla V100 GPUs(It has 64GB of video memory.) in total and we also have a 16-core Intel (R) Xeon (R) CPU. There are a total of 4 GPU bananas belonging to the Tesla series, let's examine them in order. Initialize matrices on the host for ( int i= 0 i d_A(SIZE) Perform matrix multiplication C = A*B // where A, B and C are NxN matrices int N = 10000 First I multiply matrix a and b and assign it to variable y, then I multiply matrix c and d and assign it to variable z, and finally I multiply matrix y and z and assign it to variable x, and I did this operation 1000 times in total. Speed test : I created four matrices with 10000 rows and 10000 columns on the GPU. The GPU can be just above the graphics card or integrated into the motherboard.ġ. Modern GPUs are extremely efficient at rendering and displaying computer graphics, and their high parallel structures make it more efficient than CPU for complex algorithms. Graphics processor unit : The graphics processor unit, or GPU for short, is the device used for graphic creation in personal computers, workstations or game consoles. WARNING : Instead of evaluating these GPUs alone, I recommend you to examine them with all their hardware, these GPUs may give different results in different applications or tests at different times. GPUs have been accessed via Google Colab and AWS. Hello, I have prepared two speed tests for you on NVIDIA GPUs that I have access to.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |