I am using Matlab r2012b on win7 32-bit with 4GB RAM.
However, the memory limit on Matlab process is pretty low. On memory command, I am getting the following output:
Maximum possible array: 385 MB (4.038e+08 bytes) *
Memory available for all arrays: 1281 MB (1.343e+09 bytes) **
Memory used by MATLAB: 421 MB (4.413e+08 bytes)
Physical Memory (RAM): 3496 MB (3.666e+09 bytes)
* Limited by contiguous virtual address space available.
** Limited by virtual address space available.
I need to increase the limit to as much as possible.
System: Windows 7 32 bit
RAM: 4 GB
Matlab: r2012b
For general guidance with memory management in MATLAB, see this MathWorks article. Some specific suggestions follow.
Set the /3GB switch in the boot.ini to increase the memory available to MATLAB. Or set it with a properties dialog if you are averse to text editors. This is mentioned in this section of the above MathWorks page.
Also use pack to increase the Maximum possible array by compacting the memory. The 32-bit MATLAB memory needs blocks of contiguous free memory, which is where this first value comes from. The pack command saves all the variables, clears the workspace, and reloads them so that they are contiguous in memory.
More on overall memory, try disabling the virtual machine, closing programs, stopping unnecessary Windows services. No easy answer for this part.
Related
I'm trying to run the train file from this Unet with their default hyperparameters, batch size = 1.
I have a GTX970 with 4GB and made Windows use the integrated graphics.
When I run nvidia-smi, it says that the memory of the GPU is almost free (52MiB / 4096MiB), "No running processes found " and pytorch uses the GPU not the integrated graphics
I do not understand what is using the memory:
RuntimeError: CUDA out of memory. Tried to allocate 150.00 MiB (GPU 0; 4.00 GiB total capacity; 2.77 GiB already allocated; 72.46 MiB free; 2.82 GiB reserved in total by PyTorch).
GPU memory allocation is not done all at once. As the program loads the data and the model, GPU memory usage gradually increases until the training actually starts. In your case, the program has allocated 2.7GB and tries to get more memory before training starts, but there is not enough space. 4GB GPU memory is usually too small for CV deep learning algorithms.
I am running a 32-bit version of Matlab R2013a on my computer (4GB RAM, and 32-bit Windows 7).
I have dataset (~ 60 MB) and I want to read it using
ds = dataset('File', myFile, 'Delimiter', ',');
And each time I face Out of Memory error. Theoretically, I should be able to use 2GB of RAM, so there should be no problem reading such small files.
Here is what I got when typed memory
Maximum possible array: 36 MB (3.775e+07 bytes) *
Memory available for all arrays: 421 MB (4.414e+08 bytes) **
Memory used by MATLAB: 474 MB (4.969e+08 bytes)
Physical Memory (RAM): 3317 MB (3.478e+09 bytes)
* Limited by contiguous virtual address space available.
** Limited by virtual address space available.
I followed every instructions I found (this is not a new issue), but for my case it seems rather weird, because I cannot run a simple program now.
System: Windows 7 32 bit
Matlab: R2013a
RAM: 4 GB
Clearly your issue is right here.
Maximum possible array: 36 MB (3.775e+07 bytes) *
You are either using a lot of memory in your system and/or you have a very low swap space.
I want to allocate a large DMA buffer, about 40 MB in size. When I use dma_alloc_coherent(), it fails and what I see is:
------------[ cut here ]------------
WARNING: at mm/page_alloc.c:2106 __alloc_pages_nodemask+0x1dc/0x788()
Modules linked in:
[<8004799c>] (unwind_backtrace+0x0/0xf8) from [<80078ae4>] (warn_slowpath_common+0x4c/0x64)
[<80078ae4>] (warn_slowpath_common+0x4c/0x64) from [<80078b18>] (warn_slowpath_null+0x1c/0x24)
[<80078b18>] (warn_slowpath_null+0x1c/0x24) from [<800dfbd0>] (__alloc_pages_nodemask+0x1dc/0x788)
[<800dfbd0>] (__alloc_pages_nodemask+0x1dc/0x788) from [<8004a880>] (__dma_alloc+0xa4/0x2fc)
[<8004a880>] (__dma_alloc+0xa4/0x2fc) from [<8004b0b4>] (dma_alloc_coherent+0x54/0x60)
[<8004b0b4>] (dma_alloc_coherent+0x54/0x60) from [<803ced70>] (mxc_ipu_ioctl+0x270/0x3ec)
[<803ced70>] (mxc_ipu_ioctl+0x270/0x3ec) from [<80123b78>] (do_vfs_ioctl+0x80/0x54c)
[<80123b78>] (do_vfs_ioctl+0x80/0x54c) from [<8012407c>] (sys_ioctl+0x38/0x5c)
[<8012407c>] (sys_ioctl+0x38/0x5c) from [<80041f80>] (ret_fast_syscall+0x0/0x30)
---[ end trace 4e0c10ffc7ffc0d8 ]---
I've tried different values and it looks like dma_alloc_coherent() can't allocate more than 2^25 bytes (32 MB).
How can such a large DMA buffer can be allocated?
After the system has booted up dma_alloc_coherent() is not necessarily reliable for large allocations. This is simply because non-moveable pages quickly fill up your physical memory making large contiguous ranges rare. This has been a problem for a long time.
Conveniently a recent patch-set may help you out, this is the contiguous memory allocator which appeared in kernel 3.5. If you're using a kernel with this then you should be able to pass cma=64M on your kernel command line and that much memory will be reserved (only moveable pages will be placed there). When you subsequently ask for your 40M allocation it should reliably succeed. Simples!
For more information check out this LWN article:
https://lwn.net/Articles/486301/
I am working to optimize a fluid flow and heat transfer analysis program written in Fortran. As I try to run larger and larger mesh simulations, I'm running into memory limitation problems. The mesh, though, is not all that big. Only 500,000 cells and small-peanuts for a typical CFD code to run. Even when I request 80 GB of memory for my problem, it's crashing due to insufficient virtual memory.
I have a few guesses at what arrays are hogging up all that memory. One in particular is being allocated to (28801,345600). Correct me if I'm wrong in my calculations, but a double precision array is 8 bits per value. So the size of this array would be 28801*345600*8=79.6 GB?
Now, I think that most of this array ends up being zeros throughout the calculation so we don't need to store them. I think I can change the solution algorithm to only store the non-zero values to work on in a much smaller array. However, I want to be sure that I'm looking at the right arrays to reduce in size. So first, did I correctly calculate the array size above? And second, is there a way I can have Fortran show array sizes in MB or GB during runtime? In addition to printing out the most memory intensive arrays, I'd be interested in seeing how the memory requirements of the code are changing during runtime.
Memory usage is a quite vaguely defined concept on systems with virtual memory. You can have large amounts of memory allocated (large virtual memory size) but only a small part of it actually being actively used (small resident set size - RSS).
Unix systems provide the getrusage(2) system call that returns information about the amount of system resources in use by the calling thread/process/process children. In particular it provides the maxmimum value of the RSS ever reached since the process was started. You can write a simple Fortran callable helper C function that would call getrusage(2) and return the value of the ru_maxrss field of the rusage structure.
If you are running on Linux and don't care about portability, then you may just open and read from /proc/self/status. It is a simple text pseudofile that among other things contains several lines with statistics about the process virtual memory usage:
...
VmPeak: 9136 kB
VmSize: 7896 kB
VmLck: 0 kB
VmHWM: 7572 kB
VmRSS: 6316 kB
VmData: 5224 kB
VmStk: 88 kB
VmExe: 572 kB
VmLib: 1708 kB
VmPTE: 20 kB
...
Explanation of the various fields - here. You are mostly interested in VmData, VmRSS, VmHWM and VmSize. You can open /proc/self/status as a regular file with OPEN() and process it entirely in your Fortran code.
See also what memory limitations are set with ulimit -a and ulimit -aH. You may be exceeding the hard virtual memory size limit. If you are submitting jobs through a distributed resource manager (e.g. SGE/OGE, Torque/PBS, LSF, etc.) check that you request enough memory for the job.
I am trying to understand how heap fragmenation works. What does the following output tell me?
Is this heap overly fragmented?
I have 243010 "free objects" with a total of 53304764 bytes. Are those "free object" spaces in the heap that once contained object but that are now garabage collected?
How can I force a fragmented heap to clean up?
!dumpheap -type Free -stat
total 243233 objects
Statistics:
MT Count TotalSize Class Name
0017d8b0 243010 53304764 Free
It depends on how your heap is organized. You should have a look at how much memory in Gen 0,1,2 is allocated and how much free memory you have there compared to the total used memory.
If you have 500 MB managed heap used but and 50 MB is free then you are doing pretty well. If you do memory intensive operations like creating many WPF controls and releasing them you need a lot more memory for a short time but .NET does not give the memory back to the OS once you allocated it. The GC tries to recognize allocation patterns and tends to keep your memory footprint high although your current heap size is way too big until your machine is running low on physical memory.
I found it much easier to use psscor2 for .NET 3.5 which has some cool commands like ListNearObj where you can find out which objects are around your memory holes (pinned objects?). With the commands from psscor2 you have much better chances to find out what is really going on in your heaps. Most commands are also available in SOS.dll in .NET 4 as well.
To answer your original question: Yes free objects are gaps on the managed heap which can simply be the free memory block after your last allocated object on a GC segement. Or if you do !DumpHeap with the start address of a GC segment you see the objects allocated in that managed heap segment along with your free objects which are GC collected objects.
This memory holes do normally happen in Gen2. The object addresses before and after the free object do tell you what potentially pinned objects are around your hole. From this you should be able to determine your allocation history and optimize it if you need to.
You can find the addresses of the GC Heaps with
0:021> !EEHeap -gc
Number of GC Heaps: 1
generation 0 starts at 0x101da9cc
generation 1 starts at 0x10061000
generation 2 starts at 0x02aa1000
ephemeral segment allocation context: none
segment begin allocated size
02aa0000 02aa1000** 03836a30 0xd95a30(14244400)
10060000 10061000** 103b8ff4 0x357ff4(3506164)
Large object heap starts at 0x03aa1000
segment begin allocated size
03aa0000 03aa1000 03b096f8 0x686f8(427768)
Total Size: Size: 0x115611c (18178332) bytes.
------------------------------
GC Heap Size: Size: 0x115611c (18178332) bytes.
There you see that you have heaps at 02aa1000 and 10061000.
With !DumpHeap 02aa1000 03836a30 you can dump the GC Heap segment.
!DumpHeap 02aa1000 03836a30
Address MT Size
...
037b7b88 5b408350 56
037b7bc0 60876d60 32
037b7be0 5b40838c 20
037b7bf4 5b408350 56
037b7c2c 5b408728 20
037b7c40 5fe4506c 16
037b7c50 60876d60 32
037b7c70 5b408728 20
037b7c84 5fe4506c 16
037b7c94 00135de8 519112 Free
0383685c 5b408728 20
03836870 5fe4506c 16
03836880 608c55b4 96
....
There you find your free memory blocks which was an object which was already GCed. You can dump the surrounding objects (the output is sorted address wise) to find out if they are pinned or have other unusual properties.
You have 50MB of RAM as Free space. This is not good.
Having .NET allocating blocks of 16MB from process, we have a fragmentation issue indeed.
There are plenty of reasons to fragmentation to occure in .NET.
Have a look here and here.
In your case it is possibly a pinning. As 53304764 / 243010 makes 219.35 bytes per object - much lower then LOH objects.