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
Related
I'm on Windows 11, using WSL2 (Windows Subsystem for Linux). I recently upgraded my RAM from 32 GB to 64 GB.
While I can make my computer use more than 32 GB of RAM, WSL2 seems to be refusing to use more than 32 GB. For example, if I do
>>> import torch
>>> a = torch.randn(100000, 100000) # 40 GB tensor
Then I see the memory usage go up until it hit's 30-ish GB, at which point, I see "Killed", and the python process gets killed. Checking dmesg, it says that it killed the process because "Out of memory".
Any idea what the problem might be, or what the solution is?
According to this blog post, WSL2 is automatically configured to use 50% of the physical RAM of the machine. You'll need to add a memory=48GB (or your preferred setting) to a .wslconfig file that is placed in your Windows home directory (\Users\{username}\).
[wsl2]
memory=48GB
After adding this file, shut down your distribution and wait at least 8 seconds before restarting.
Assume that Windows 11 will need quite a bit of overhead to operate, so setting it to use the full 64 GB would cause the Windows OS to run out of memory.
Vivado consumes all of the free memory space in my machine during synthesis and for this reason, the machine either hangs or crashes after a while.
I encountered this issue when using Vivado 2018.1 on Windows 10 (w/ 8GB RAM) and Vivado 2020.1 on CentOS 7 (w/ 16GB RAM).
Is there any option in Vivado to limit its memory usage?
If this problem happens when you are synthesizing multiple out of context modules, try reducing the Number of Jobs when you start the run.
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.
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 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.