I have a computer with
Processor: i7 2.60 GHz,
16 GB Ram,
64-bit OS
How can I set the memory configuration in OrientDB?
Can someone help me ?
Where and how please.
Related
I want to buy a vps with the following specifications:
2 Core (s) 2.4 GHz
8 GB of RAM
Debian 9 x64
and a dedicated server
CPU: Intel Xeon E3-1245v5 - 4 / 8t - 3.5GHz /3.9GHz
RAM: 16GB DDR4 ECC 2133 MHz
Disks: SoftRaid 2x2TB
Debian 9 x64
How many connected clients does each server support with vernemq or mosquitto with QOS 1?
Well, I have solved the doubt, for more than 1000 users you just have to modify the file /etc/init.d/mosquitto
add the following line to the beginning of the file
ulimit -n 20000
where 20000 is the number of connections to support
I transforming an XML to XML using SAXON9EE. Size of source file is 200MB, Size of XSL is 65KB
The translation time is different on different machines.
On Windwows Vista, 64bit, 24GB RAM takes 4hrs
On Windows XP, 32bit, 4GB RAM takes 6hrs
On Linux 32 bit, 8GB RAM takes 14hrs
On Sun OS, 64bit, 32GB RAM takes 48hrs. Here are some output from Sun OS TOP command 309 processes: 290 sleeping, 3 zombie, 3 stopped,
13 on cpu CPU states: 63.6% idle, 35.0% user, 1.4% kernel, 0.0%
iowait, 0.0% swap Memory: 32G phys mem, 10G free mem, 4005M total
swap, 4005M free swap
On another Sun OS, 64bit, 8GB RAM, takes 48hrs.
My requirement is to run on Sun OS and reduce the time. Why does Sun OS take such a long time. I have tried changing the heap size, no luck. Should I try changing any other parameter.
Please advice.
Thanks
Regards
Siva
I have Tesla C2075. I wanted to know global memory size. So I ran deviceQuery SDK sample. It reports me 4GB of global memory but when I run nvidia-smi -q, it reports 6GB of global memory. Why this mismatch occurs? Is some memory specially dedicated for OS?
./deviceQuery reports:
CUDA Device Query (Runtime API) version (CUDART static linking)
Found 1 CUDA Capable device(s)
Device 0: "Tesla C2075"
CUDA Driver Version / Runtime Version 5.0 / 5.0
CUDA Capability Major/Minor version number: 2.0
Total amount of global memory: 4096 MBytes (4294967295 bytes)
nvidia-smi -q output:
Memory Usage
Total : 5375 MB
Used : 39 MB
Free : 5336 MB
You're running 32-bit Linux, so you will only have 4GB of device memory available to your process.
The device still has 6GB, so if you have two processes sharing the device then between them they can occupy the full 6GB, but each process can only use 4GB.
I have some performance issues with ADS 10.1 after changing the network.
Let me explain:
Old configuration of the network:
Server
- Server2003R2
- Processor: Intel XEON 5130, 2 GHZ
- 4 GB RAM
- ADS 7.01
Workstation(s)
- Intel 2160, 1,8 Ghz
- 2 GB RAM
New situation
Server:
- Server 2008R2 SP1
- Intel XEON E5530 2,4 Ghz
- 14 GB RAM
- ADS 10.10.0.28
- Runs in virtual environment on ESXi server version 5.0.0
Workstation(s)
- Windows 7
- I3-2120 3.3 ghz
- 8 gb RAM
Our application (built in VO 2.7) is approximately 3 times slower in new configuration.
Has anyone an idea how this is possible?
I already disabled SMB2 on workstations.
Kind regards,
Erik
We just solved a similar issue for one of our clients. It turns out the virtual NIC speed was causing the issue. We had our customer bump the speed to gigabit and now they are working at a reasonable speed.
I have successfully installed a 64 bit Fedora 11 guest os using VirtualBox on a host machine (AMD64) running 32 bit Windows XP .
At the moment the host machine has 2 Gb ram installed and I've allocated 1 Gb to the guest, which all works well.
The host machine can hold a maximum of 4 Gb ram, so I was wondering if it's worth buying an extra 2 Gb for it.
I know that 32 bit Windows XP can't use all of the 4 Gb, but can the guest os use any of the ram that the host os can't use?
No, you are limited what the host OS can see. If you open up task manager in the host OS, the guest OS's memory is mapped within there, so having memory that's mapped outside of the host OS is not possible.
That shouldn't discourage you from getting the extra ram, however. If you upgrade to 4 (or 3.5GB) then you'll still have about ~3.2GB of addressable memory to use, which is a substantial increase over 2GB especially if your memory usage is already near 2GB.