I have an ARM based device, running linux, which is connected to a camera, and I'm trying to store captured frames to HD efficiently.
I'm developing in user space, but can modify drivers at will
I'm coding in C
Frames which are written into memory using DMA, and I have their physical memory pointer.
I am able to control all the frame capturing flow, and I can tell when the frame buffers are stable (dqueued from the video4linux driver)
Linux version is 3.0.35
I'm familiar with kernel source code, not an expert, but I'm able to find my way in it and figure out things, as long as I get some hints...
I believe I have 2 alternatives:
Find the optimal configuration for my filesystem, for opening the file and writing into it. I'm now using ext4, and standard fopen() fwrite() functions. I understand I can also use mmap, or add O_DIRECT flag when calling open(), but didn't try it yet.
Find a way to pass the physical address of the buffer (I can get it
from my Video4Linux driver) directly to the filesystem/hard drive driver,
so the data will be transfered directly from there.
I found method 1 to be slow, having memory transactions as my bottleneck, since fwrite involves copying data from userspace to kernel space, and then again into some sort of cache, and then on to DMA. Too many memory transactions for a simple store...
Regarding method 2 - I don't know if that's possible, but if I was the one designing this system from scratch, this is what I would do.
Any thoughts?
Regarding method 1 (using open() and write(), mmap() and/or O_DIRECT)
can you recommend an optimal settings for my purpose?
Is method 2 (storing to HD directly from an existing DMA buffer) possible? If so - can you point me to an example?
the only problem with writing into a file via mmap on UNIXs, is that you either have to deal with signals in case of out-of-disk-space
or you have make certain that the file is not sparse
and thus all needed disk space is already allocated.
I think an uptodate G++ provides a method of converting signals into C++ exception handling,
but I'm not certain how supported this is on other systems than mac-os.
Related
I've been programming a Linux kernel module for several years for a PCIe device. One of the main feature is to transfer data from the PCIe card to the host memory using DMA.
I'm using streaming DMA, i.e. it's the user program that allocates the memory, and my kernel module has to do the job of locking the pages and creating the scatter gather structure. It works correctly.
However, when used on some more recent hardware with Intel processors, the function calls dma_map_page() and dma_unmap_page() are taking much longer time to execute.
I've tried to use dma_map_sg() and dma_unmap_sg(), it takes approximately the same longer-time.
I've tried to split the dma_unmap_sg() into a first call to dma_sync_for_cpu(), followed by the call to dma_unmap_sg_attr() with attribute DMA_ATTR_SKIP_CPU_SYNC. It works correctly. And I can see the additional time is spend on the unmap operation, not for the sync.
I've tried to play with the Linux kernel command line parameters relating to the iommu (on, force, strict=0), and also intel_iommu, with no change in the behavior.
Some other hardware show a decent transfer rate, i.e. more than 6GB/s on PCIe3x8 (max 8GB/s).
The issue on some recent hardware is limiting transfer rate to ~3GB/s (I've checked that the card is correctly configured for PCIe3x8, and the programmer of the Windows device driver manages to achieve the 6GB/s on the same system. Things are more behind the curtains in Windows and I cannot get much information from it.)
On some hardware, the behavior is either normal or slowed, depending on the Linux distribution (and the Linux kernel version I guess). On some other hardware, the roles are reversed, i.e. the slow one becomes the fast one and vice-versa.
I cannot figure out the cause of this. Any clue?
I've been using an AT28C256 as EEPROM 'ROM' for a Z80 project quite successfully. As the AT28C256 can be programmed at 5V using the /WE pin, I was thinking about also using it as a form of non-volatile SRAM, rather than adding another chip.
Yes, the AT28C256 is only 32kB in size, so I'm not using the whole 16-bit address space on the Z80 - but I wanted to know if this is possible?
Could I just OR the /MREQ and /WR lines on the Z80 together for the /WE on the AT28C256? Or am I missing something?
I could then set my Stack Pointer (SP) to the 32k boundary, rather than the usual 0xFFFF.
You can use an EEPROM like a RAM, but only if you take its behavior into account.
You can simply connect:
Z80-/MREQ to EEPROM-/CE, but you will need to gate this
Z80-/WR to EEPROM-/WE
Z80-/RD to EEPROM-/OE
Things to consider, consult the data sheet for details:
If you write a byte (or use the page write algorithm) the EEPROM will not output the stored values if you read it, until the self-timed write cycle has passed.
The write cycle is about some milliseconds long.
The EEPROM might fail after a few 10k write cycles (Thanks, Stefan Paul Noack).
You can't use it for the program that changes the chip's contents, because of point 1.
You can't use it for the stack or any other data that needs to be stored and retrieved quickly, because of point 2.
However, you can use it for the application's data. But you will need another memory for the program to run.
And if your program needs a stack or other variables to be written quickly, you will need an additional RAM. (Note: I remember a Z80 application that implemented a printer queue with just simple DRAM, using only the CPU's registers for the program's variables, and using the DRAM only for the data to buffer.)
To have multiple chip's as memory, you will need to gate the /CE-pins of these memories depending on their address range.
I read the following on https://blogs.oracle.com/roch/entry/does_zfs_really_use_more
There is one peculiar workload that does lead ZFS to consume more
memory: writing (using syscalls) to pages that are also mmaped. ZFS
does not use the regular paging system to manage data that passes
through reads and writes syscalls. However mmaped I/O which is closely
tied to the Virtual Memory subsystem still goes through the regular
paging code . So syscall writting to mmaped pages, means we will keep
2 copies of the associated data at least until we manage to get the
data to disk. We don't expect that type of load to commonly use large
amount of ram
What does this mean exactly? does this mean that zfs will "uselessly" double cache any memory region that is backed by a memory mapped file? or does "using syscalls" mean writing using some other method of writing that I am not familiar with.
If so, am I better off keeping the working directories of files written this way on a ufs partition?
Does this mean that zfs will "uselessly" double cache any memory region that is backed by a memory mapped file?
Hopefully, no.
or does "using syscalls" mean writing using some other method of writing that I am not familiar with.
That method is just regular low level write(fd, buf, nbytes) system calls and similars and not what memory mapped files are designed to support: accessing file content just with reading / writing memory by using pointers, using the file data as a byte array or whatever.
If so, am I better off keeping the working directories of files written this way on a ufs partition?
No, unless memory mapped files that are also written to using system calls sum to a significant part of your RAM workload, which is quite unlikely to happen.
PS: Note that this blog is almost ten years old. There might have been changes in the implementation since that time.
I have to deliver an application as a standalone Matlab executable to a client. The code include a series of calls to a function that internally creates several cell arrays.
My problem is that an out-of-memory error happens when the number of calls to this function increases in response to the increase in the user load. I guess this is low-level memory fragmentation as the workspace variables are independent from the number of loops.
As mentioned here, quitting and restarting Matlab is the only solution for this type of out-of-memory errors at the moment.
My question is that how I can implement such a mechanism in a standalone application to save data, quit and restart itself in the case of out-of-memory error (or when high likelihood of such an error is predicted somehow).
Is there any best practice available?
Thanks.
This is a bit of a tough one. Instead of looking to restart to clear things out, could you change the code to break the work in to chunks to make it more efficient? Fragmentation is mostly proportional to the peak cell-related memory usage and how much the size of data items varies, and less to the total usage over time. If you can break a large piece of work in to smaller pieces done in sequence, this can lower the "high water mark" of your fragmented memory usage. You can also save on memory usage by using "flyweight" data structures that share their backing data values, or sometimes converting to cell-based structures to reference objects or numeric codes. Can you share an example of your code and data structure with us?
In theory, you could get a clean slate by saving your workspace and relevant state out to a mat file and having the executable launch another instance of itself with an option to reload that state and proceed, and then having the original executable exit. But that's going to be pretty ugly in terms of user experience and your ability to debug it.
Another option would be to offload the high-fragmentation code in to another worker process which could be killed and restarted, while the main executable process survives. If you have the Parallel Computation Toolbox, which can now be compiled in to standalone Matlab executables, this would be pretty straightforward: open a worker pool of one or two workers, and run the fraggy code inside them using synchronous calls, periodically killing the workers and bringing up new ones. The workers are independent processes which start out with non-fragmented memory spaces. If you don't have PCT, you could roll your own by compiling your application as two separate apps - the driver app and worker app - and have the main app spin up a worker and control it via IPC, passing your data back and forth as MAT files or bytestreams. That's not going to be a lot of fun to code, though.
Perhaps you could also push some of the fraggy code down in to the Java layer, which handles cell-like data structures more gracefully.
Changing the code to be less fraggy in the first place is probably the simpler and easier approach, and results in a less complicated application design. In my experience it's often possible. If you share some code and data structure details, maybe we can help.
Another option is to periodically check for memory fragmentation with a function like chkmem.
You could integrate this function to be called silently from you code each couple of iterations, or use a timer object to have it called every X minutes...
The idea is to use thse undocumented functions feature memstats and feature dumpmem to get the largest free memory blocks available in addition to the largest variables currently allocated. Using that you could make a guess if there is a sign of memory fragmentation.
When detected, you would warn the user and instruct them you how to save their current session (export to MAT-file), restart the app, and restore the session upon restart.
Stonebraker's paper (Operating System Support for Database Management) explains that, "the overhead to fetch a block from the buffer pool manager usually includes that of a system call and a core-to-core move." Forget about the buffer-replacement strategy, etc. The only point I question is the quoted.
My understanding is that when a DBMS wants to read a block x it issues a common read instruction. There should be no difference from that of any other application requesting a read.
I'm not looking for generic answers (I got them, and read papers). I seek a detailed answer of the described problem.
See Does a file read from a Java application invoke a system call?
Reading from your other question, and working forward:
When the DBMS must bring a page from disk it will involve at least one system call. At his point most DBMSs place the page into their own buffer. (They also end up in the OS' buffer, but that's unimportant).
So, we have one system call. However, we can avoid any further system calls. This is possible because the DBMS is caching pages in its own memory space. The first thing the DBMS will do when it decides it needs a page is check and see if it has it in its cache. If it does, it retrieves it from there without ever invoking a system call.
The DBMS is free to expire pages in its cache in whatever way is most beneficial for its IO needs. The OS's cache is expired in a more general way since the OS has other things to worry about. One example of this is that a DBMS will typically use a great deal of memory to cache pages as it knows that disk IO is one of the most expensive things it can do. The OS won't do this as it has to balance the cost of disk IO against having memory for other applications to use.
The operating system disk i/o must be generalised to work for a variety of situations. The DBMS can sometimes gain significant performance using less general code that is optimised to its own needs.
The DBMS does its own caching, so doesn't want to work through the O/S caching. It "owns" the patch of disk, so it doesn't need to worry about sharing with other processes.
Update
The link to the paper is a help.
Firstly, the paper is almost thirty years old and is referring to long-obsolete hardware. Notwithstanding that, it makes quite interesting reading.
Firstly, understand that disk i/o is a layered process. It was in 1981 and is even more so now. At the lowest point, a device driver will issue physical read/write instructions to the hardware. Above that may be the o/s kernel code then the o/s user space code then the application. Between a C program's fread() and the disk heads moving, there are at least three or four levels and might be considerably more. The DBMS may seek to improve performance might seek to bypass some layers and talk directly with the kernel, or even lower.
I recall some years ago installing Oracle on a Sun box. It had an option to dedicate a disk as a "raw" partition, where Oracle would format the disk in its own manner and then talk straight to the device driver. The O/S had no access to the disk at all.
It's mainly a performance issue. A dbms has highly specific and unusual I/O demands.
The OS may have any number of processes doing I/O and filling its buffers with the assorted cached data that this produces.
And of course there is the issue of size and what gets cached (a dbms may be able to peform better cache for its needs than the more generic device buffer caching).
And then there is the issue that a generic “block” may in fact amount to a considerably larger I/O burden (this depends on partitioning and such like) than what a dbms ideally would like to bear; its own cache may be tuned to work better with the layout of the data on the disk and thereby able to minimise I/O.
A further thing is the issue of indexes and similar means to speed up queries, which of course works rather better if the cache actually knows what these mean in the first place.
The real issue is that the file buffer cache is not in the filesystem used by the DBMS; it's in the kernel and shared by all of the filesystems resident in the system. Any memory read out of the kernel must be copied into user space: this is the core-to-core move you read about.
Beyond this, some other reasons you can't rely on the system buffer pool:
Often, DBMS's have a really good idea about its upcoming access patterns, and it can't communicate these patterns to the kernel. This can lead to lower performance.
The buffer cache is traditional stored in a fixed-size kernel memory range, so it cannot grow or shrink. That also means the cache is much smaller than main memory, so by using the buffer cache a DBMS would be unable to take advantage of system resources.
I know this is old, but it came up as unanswered.
Essentially:
The OS uses a separate address spaces for every process.
Retrieving information from any other address space requires a system call or page fault. **(see below)
The DBMS is a process with its own address space.
The OS buffer pool Stonebraker describes is in the kernel address space.
So ... to get data from the kernel address space to the DBMS's address space, a system call or page fault is unavoidable.
You're correct that accessing data from the OS buffer pool manager is no more expensive than a normal read() call. (In fact, it's done with a normal read call.) However, Stonebraker is not talking about that. He's specifically discussing the caching needs of DBMSes, after the data has been read from the disk and is present in RAM.
In essence, he's saying that the OS's buffer pool cache is too slow for the DBMS to use because it's stored in a different address space. He's suggesting using a local cache in the same process (and therefore same address space), which can give you a significant speedup for applications like DBMSes which hit the cache heavily, because it will eliminate that syscall overhead.
Here's the exact paragraph where he discusses using a local cache in the same process:
However, many DBMSs including INGRES
[20] and System R [4] choose to put a
DBMS managed buffer pool in user space
to reduce overhead. Hence, each of
these systems has gone to the
trouble of constructing its own
buffer pool manager to enhance
performance.
He also mentions multi-core issues in the excerpt you quote above. Similar effects apply here, because if you can have just one cache per core, you may be able to avoid the slowdowns from CPU cache flushes when multiple CPUs are reading and writing the same data.
** BTW, I believe Stonebraker's 1981 paper is actually pre-mmap. He mentions it as future work. "The trend toward providing the file system as a part of shared virtual memory (e.g., Pilot [16]) may provide a solution to this problem."