Updated 4/11/2018
Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.
bool isAnagram(const std::string& str1, const std::string& str2) | |
{ | |
if(str1.size() != str2.size()){ | |
return false; | |
} | |
constexpr uint16_t maxSizeOfMap = 1<<(sizeof(char)*8); | |
std::array<uint32_t,maxSizeOfMap> mapStr1; | |
std::array<uint32_t,maxSizeOfMap> mapStr2; | |
// Don't forget to perform initialization of variables in C++ |
bool isAnagram(const std::string& str1, const std::string& str2) | |
{ | |
if(str1.size() != str2.size()){ | |
return false; | |
} | |
std::unordered_map<char,uint32_t> mapStr1; | |
std::unordered_map<char,uint32_t> mapStr2; | |
constexpr uint16_t maxSizeOfMap = 1<<(sizeof(char)*8); |
bool isAnagram(const std::string& str1, const std::string& str2){ | |
// Two string can't be anagram if they are of different size [O(1)] | |
if(str1.size()!=str2.size()){ | |
return false; | |
} | |
// Copy have to be done to sort it later [O(n) space and O(n) time both] | |
std::string str1Copy = str1; | |
std::string str2Copy = str2; |
Updated 4/11/2018
Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.
Ubuntu 15.10 have been released for a couple of days. It is a bleeding-edge system coming with Linux kernel 4.2 and GCC 5. However, compiling and running Caffe on this new system is no longer as smooth as on earlier versions. I have done some research related to this issue and finally find a way out. I summarize it here in this short tutorial and I hope more people and enjoy this new system without breaking their works.
The latest NVIDIA driver is officially included in Ubuntu 15.10 repositories. One can install it directly via apt-get
.
sudo apt-get install nvidia-352-updates nvidia-modprobe
The nvidia-modprobe
utility is used to load NVIDIA kernel modules and create NVIDIA character device files automatically everytime your machine boots up.
Reboot your machine and verify everything works by issuing nvidia-smi
or running deviceQuery
in CUDA samples.