multi-gpu

I have run into problems with Geforce GTX 690 while trying to track down the memory usage. A simple test program: BOOST_AUTO_TEST_CASE(cudaMemoryTest) { size_t mem_tot_0 = 0; size_t mem_free_0 = 0; size_t mem_tot_1 = 0; size_t mem_free_1 = 0; unsigned int mem_size = 100*1000000; float* h_P = new float[mem_size]; for(size_t i = 0; i < mem_size; i++) { h_P[i] = 0.f; } cudaSetDevice(0); cudaDeviceReset(); cudaMemGetInfo (&mem_free_0, & mem_tot_0); std::cout<<"Free memory before copy dev 0: "<<mem_free_0<<std::endl; cudaSetDevice(1); cudaDeviceReset(); cudaMemGetInfo (&mem_free_1, &mem_tot_1); std

From Nvidia's website, it explain the time-out problem: Q: What is the maximum kernel execution time? On Windows, individual GPU program launches have a maximum run time of around 5 seconds. Exceeding this time limit usually will cause a launch failure reported through the CUDA driver or the CUDA runtime, but in some cases can hang the entire machine, requiring a hard reset. This is caused by the Windows "watchdog" timer that causes programs using the primary graphics adapter to time out if they run longer than the maximum allowed time. For this reason it is recommended that CUDA is run on a

By the link is written: https://docs.nvidia.com/deploy/pdf/CUDA_Multi_Process_Service_Overview.pdf 1.1. AT A GLANCE 1.1.1. MPS The Multi-Process Service (MPS) is an alternative, binary-compatible implementation of the CUDA Application Programming Interface (API). The MPS runtime architecture is designed to transparently enable co-operative multi-process CUDA applications, typically MPI jobs , to utilize Hyper-Q capabilities on the latest NVIDIA (Kepler-based) Tesla and Quadro GPUs. Hyper-Q allows CUDA kernels to be processed concurrently on the same GPU; this can benefit performance when the

In our lab, we have NVIDIA Tesla K80 GPU accelerator computing with the following characteristics: Intel(R) Xeon(R) CPU E5-2670 v3 @2.30GHz, 48 CPU processors, 128GB RAM, 12 CPU cores running under Linux 64-bit. I am running the following code which does GridSearchCV after vertically appends different sets of dataframes into a single series of a RandomForestRegressor model. The two sample datasets I am considering are found in this link import sys import imp import glob import os import pandas as pd import math from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature

I had recently some trouble making my pc (ivybridge) use the onboard gpu (intel igpu HD4000) for normal screen display usage, while i run my CUDA programs for computations on the discrete Nvidia GT 640 i have on my machine. The problem was that under iGPU display, CUDA would be unable to spot the nvidia card , and the nvidia drivers would not load at all. Keep in mind that there are confirmed issues (mostly about concurrency) when using the nvidia windows drivers for display devices, and also want to use CUDA. Those issues can get overridden when you use the Intel gpu as display (thus loading