I have an algorithm that takes 7 days to Run To Completion (and few more algorithms too)
Problem: In order to successfully Run the program, I need continuous power supply. And if out of luck, there is a power loss in the middle, I need to restart it again.
So I would like to ask a way using which I can make my program execute in phases (say each phase generates Results A,B,C,...) and now in case of a power loss I can some how use this intermediate results and continue/Resume the Run from that point.
Problem 2: How will i prevent a file from re opening every time a loop iterates ( fopen was placed in a loop that runs nearly a million times , this was needed as the file is being changed with each iteration)
You can separate it in some source files, and use make.
When each result phase is complete, branch off to a new universe. If the power fails in the new universe, destroy it and travel back in time to the point at which you branched. Repeat until all phases are finished, and then merge your results into the original universe via a transcendental wormhole.
Well, couple of options, I guess:
You split your algorithm along sensible lines with this a defined output from a phase that can be the input to the next phase. Then, configure your algorithm as a workflow (ideally soft-configured through some declaration file.
You add logic to your algorithm by which it knows what it has successfully completed (commited). Then, on failure, you can restart the algorithm and it bins all uncommitted data and restarts from the last commit point.
Note that both these options may draw out your 7hr run time further!
So, to improve the overall runtime, could you also separate your algorithm so that it has "worker" components that can work on "jobs" in parallel. This usually means drawing out some "dumb" but intensive logic (such as a computation) that can be parameterised. Then, you have the option of running your algorithm on a grid/ space/ cloud/ whatever. At least you have options to reduce the run time. Doesn't even need to be a space... just use queues (IBM MQ Series has a C interface) and just have listeners on other boxes listening to your jobs queue and processing your results before persisting the results. You can still phase the algorithm as discussed above too.
Problem 2: Opening the file on each iteration of the loop because it's changed
I may not be best qualified to answer this but doing fopen on each iteration (and fclose) presumably seems wasteful and slow. To answer, or have anyone more qualified answer, I think we'd need to know more about your data.
For instance:
Is it text or binary?
Are you processing records or a stream of text? That is, is it a file of records or a stream of data? (you aren't cracking genes are you? :-)
I ask as, judging by your comment "because it's changed each iteration", would you be better using a random-accessed file. By this, I'm guessing you're re-opening to fseek to a point that you may have passed (in your stream of data) and making a change. However, if you open a file as binary, you can fseek through anywhere in the file using fsetpos and fseek. That is, you can "seek" backwards.
Additionally, if your data is record-based or somehow organised, you could also create an index for it. with this, you could use to fsetpos to set the pointer at the index you're interested in and traverse. Thus, saving time in finding the area of data to change. You could even persist your index in an accompanying index file.
Note that you can write plain text to a binary file. Perhaps worth investigating?
Sounds like classical batch processing problem for me.
You will need to define checkpoints in your application and store the intermediate data until a checkpoint is reached.
Checkpoints could be the row number in a database, or the position inside a file.
Your processing might take longer than now, but it will be more reliable.
In general you should think about the bottleneck in your algo.
For problem 2, you must use two files, it might be that your application will be days faster, if you call fopen 1 million times less...
Related
I've written two relatively small programs using C. Both of them comunnicate with each other using textual data. Program A generates some problems from given input, B evaluates them and creates input for another iteration of A.
Here's a bash script that I currently use:
for i in {1..1000}
do
./A data > data2;
./B data2 > data;
done
The problem is that since what A and B do is not very time consuming, most of the time is spent (as I suppose) in starting apps up. When I measure time the script runs I get:
$ time ./bash.sh
real 0m10.304s
user 0m4.010s
sys 0m0.113s
So my main question is: is there any way to communicate data beetwen those two apps faster? I don't want to integrate them into one application, because I'm trying to build a toolset with independent, easly communicating tools (as was suggested in "The Art of Unix Programming" from which I'm learning the way to write reusable software).
PS. The data and data2 files contain sets of data needed in whole at once by those applications (so communicating by for e.g. one line of data at time is impossible).
Thanks for any suggestions.
cheers,
kajman
Can you create named pipe ?
mkfifo data1
mkfifo data2
./A data1 > data2 &
./B data2 > data1
If your application is reading and writing in a loop, this could work :)
If you used pipes to transfer the stdout of program A to the stdin of program B you would remove the need to write the file "data2" each loop.
./A data1 | ./B > data1
Program B would need to have the capability of using input from stdin rather than a specified file.
If you want to make a program run faster, you need to understand what is making the program run slowly. The field of computer science dedicated to measuring the performance of a running program is called profiling.
Once you discover which internal portion of your program is running slow, you can generally speed it up. How you go about speeding up that item depends heavily on what "the slow part" is doing and how it is "being done".
Several people have recommended pipes for moving the data directly from the output of one program into the input of another program. Assuming you rewrite your tools to handle input and output in a piped manner, this might improve performance. Again, it depends on what you are doing and how you are doing it.
For example, if your tool just fixes windows style end-of-lines into unix style end-of-lines, the program might read in one line, waiting for it to be available, check the end-of-line and write out the line with the desired end-of-line. Or the tool might read in all of the data, do a replacement call on each "wrong" end-of-line in memory, and then write out all of the data. With the first solution, piping speeds things up. With the second solution piping doesn't speed up anything.
The reason is is truly so hard to answer such a question is because the fix you need really depends on the code you have, the problem you are trying to solve, and the means by which you are solving it now. In the end, there isn't always a 100% guarantee that the code can be sped up; however, virtually every piece of code has opportunities to be sped up. Use profiling to speed up the parts that are slow, instead of wasting your time working on a part of your program that is only called once, and represents 0.001% of the program's runtime.
Remember if you speed up something that is 0.001% of your program's runtime by 50%, you actually only sped up your entire program by 0.0005%. Use profiling to determine the block of code that's taking up 90% of your runtime and concentrate on it.
I do have to wonder why, if A and B depend on each other to run, do you want them to be part of an independent toolset.
One solution is a compromise between the two:
Create a library that contains A.
Create a library that contains B.
Create a program that spawns two threads, 1 containing A and 2 containing B.
Create a semaphore that tells A to run and another that tells B to run.
After the function that calls A in 1, increment B's semaphore.
After the function that calls B in 2, increment A's semaphore.
Another possibility is to use file locking in your programs:
Make both A and B execute in infinite loops (or however many times you're processing data)
Add code to attempt to lock both files at the beginning of the infinite loop in A and B (if not, sleep and try again so that you don't do anything until you have the lock).
Add code to unlock and sleep for longer than the sleep in step 2 at the end of each loop.
Either of these solve the problem of having the overhead of launching the program between runs.
It's almost certainly not application startup which is the bottleneck. Linux will end up caching large portions of your programs, which means that launching will progressively get faster (to a point) the more times you start your program.
You need to look elsewhere for your bottleneck.
I am designing a file system in user space and need to test it. I do not want to use the available benchmarking tools as my requirements are different. So to test the file system I wish to simulate file access operation. To do this, I first use the ftw() function to walk through one f my existing file system(experimental) and list all the files and directories in a file.
Then I invoke a simulator to simulate file access by a number of processes. Thus, the simulator randomly starts a process i.e it forks a thread which does what a real process would have done. The thread randomly selects a file operation (read, write, rename etc) selects arguments to this operation from the list(generated by ftw()) . The thread does a number of such file operations and then exits marking the end of a process. The simulator continues to spawn threads; thread execution can overlap just as real processes do. Now, as operations are performed by threads, files get inserted, deleted, renamed and this is updated in the list of files.
I have not yet started coding. Does the plan seem sane? I am also not sure how to code the simulator...how will it spawn threads over a period of time. Should I be using some random delay to do this.
Thanks
Yep, that seems fairly reasonable to me. I would consider attempting to impose a statistical distribution over your file operations (and accesses to particular files) that is somehow matched to your expected workload. You might be able to find some statistics about typical filesystem workloads as a starting point.
That sounds about right for a decent test case just to make sure it's working. You could use sleep() to wait between spawning threads or just spawn them all at once and have them do an operation then wait a bit, then do another operation, etc... IMO if you hit it hard with a lot of requests and it works then there's a likely chance your filesystem will do just fine. Take an example from PostMark which all it does is append like crazy to different files and other benchmarks that do random access reads/writes in different locations to make sure that the page has to be read from disk.
I want to write a C program that will sample something every second (an extension to screen). I can't do it in a loop since screen waits for the program to terminate every time, and I have to access the previous sample in every execution. Is saving the value in a file really my best bet?
You could use a named pipe (if available), which might allow the data to remain "in flight", i.e. not actually hit disk. Still, the code isn't any simpler, and hitting disk twice a second won't break the bank.
You could also use a named shared memory region (again, if available). That might result in simpler code.
You're losing some portability either way.
Is saving the value in a file really my best bet?
Unless you want to write some complicated client/server model communicating with another instance of the program just for the heck of it. Reading and writing a file is the preferred method.
I need to write a sorting program in C and it would be nice if the file could be sorted in place to save disk space. The data is valuable, so I need to ensure that if the process is interrupted (ctrl-c) the file is not corrupted. I can guarantee the power cord on the machine will not be yanked.
Extra details: file is ~40GB, records are 128-bit, machine is 64-bit, OS is POSIX
Any hints on accomplishing this, or notes in general?
Thanks!
To clarify: I expect the user will want to ctrl-c the process. In this case, I want to exit gracefully and ensure that the data is safe. So this question is about handling interrupts and choosing a sort algorithm that can wrap up quickly if requested.
Following up (2 years later): Just for posterity, I have installed the SIGINT handler and it worked great. This does not protect me against power failure, but that is a risk I can handle. Code at https://code.google.com/p/pawnsbfs/source/browse/trunk/hsort.c and https://code.google.com/p/pawnsbfs/source/browse/trunk/qsort.c
Jerry's right, if it's just Ctrl-C you're worried about, you can ignore SIGINT for periods at a time. If you want to be proof against process death in general, you need some sort of minimal journalling. In order to swap two elements:
1) Add a record to a control structure at the end of the file or in a separate file, indicating which two elements of the file you are going to swap, A and B.
2) Copy A to the scratch space, record that you've done so, flush.
3) Copy B over A, then record in the scratch space that you have done so, flush
4) Copy from the scratch space over B.
5) Remove the record.
This is O(1) extra space for all practical purposes, so still counts as in-place under most definitions. In theory recording an index is O(log n) if n can be arbitrarily large: in reality it's a very small log n, and reasonable hardware / running time bounds it above at 64.
In all cases when I say "flush", I mean commit the changes "far enough". Sometimes your basic flush operation only flushes buffers within the process, but it doesn't actually sync the physical medium, because it doesn't flush buffers all the way through the OS/device driver/hardware levels. That's sufficient when all you're worried about is process death, but if you're worried about abrupt media dismounts then you'd have to flush past the driver. If you were worried about power failure, you'd have to sync the hardware, but you're not. With a UPS or if you think power cuts are so rare you don't mind losing data, that's fine.
On startup, check the scratch space for any "swap-in-progress" records. If you find one, work out how far you got and complete the swap from there to get the data back into a sound state. Then start your sort over again.
Obviously there's a performance issue here, since you're doing twice as much writing of records as before, and flushes/syncs may be astonishingly expensive. In practice your in-place sort might have some compound moving-stuff operations, involving many swaps, but which you can optimise to avoid every element hitting the scratch space. You just have to make sure that before you overwrite any data, you have a copy of it safe somewhere and a record of where that copy should go in order to get your file back to a state where it contains exactly one copy of each element.
Jerry's also right that true in-place sorting is too difficult and slow for most practical purposes. If you can spare some linear fraction of the original file size as scratch space, you'll have a much better time of it with a merge sort.
Based on your clarification, you wouldn't need any flush operations even with an in-place sort. You need scratch space in memory that works the same way, and that your SIGINT handler can access in order to get the data safe before exiting, rather than restoring on startup after an abnormal exit, and you need to access that memory in a signal-safe way (which technically means using a sig_atomic_t to flag which changes have been made). Even so, you're probably better off with a mergesort than a true in-place sort.
Install a handler for SIGINT that just sets a "process should exit soon" flag.
In your sort, check the flag after every swap of two records (or after every N swaps). If the flag is set, bail out.
The part for protecting against ctrl-c is pretty easy: signal(SIGINT, SIG_IGN);.
As far as the sorting itself goes, a merge sort generally works well for external sorting. The basic idea is to read as many records into memory as you can, sort them, then write them back out to disk. By far the easiest way to handle this is to write each run to a separate file on disk. Then you merge those back together -- read the first record from each run into memory, and write the smallest of those out to the original file; read another record from the run that supplied that record, and repeat until done. The last phase is the only time you're modifying the original file, so it's the only time you really need to assure against interruptions and such.
Another possibility is to use a selection sort. The bad point is that the sorting itself is quite slow. The good point is that it's pretty easy to write it to survive almost anything, without using much extra space. The general idea is pretty simple: find the smallest record in the file, and swap that into the first spot. Then find the smallest record of what's left, and swap that into the second spot, and so on until done. The good point of this is that journaling is trivial: before you do a swap, you record the values of the two records you're going to swap. Since the sort runs from the first record to the last, the only other thing you need to track is how many records are already sorted at any given time.
Use heap sort, and prevent interruptions (e.g. block signals) during each swap operation.
Backup whatever you plan to change. The put a flag that marks a successful sort. If everything is OK then keep the result, otherwise restore backup.
Assuming a 64-bit OS (you said it is a 64bit machine but could still be running 32bit OS), you could use mmap to map the file to an array then use qsort on the array.
Add a handler for SIGINT to call msync and munmap to allow the app to respond to Ctrl-C without losing data.
We have a UFS partition on solaris.
The volume becomes full. We're still trying to write to it - and naturally open() returns -1 immediately.
When a cronjob fires up which does a mass delete, it looks like open() doesn't return in a timely manner - it's taking at least six seconds, because that's how long before the watchdog kills the process.
Now, the obvious thought is that the deletes are keeping the file system busy and open() just takes forever...but is there any concrete knowledge out there about this behaviour?
Perhaps the program doing the 'mass delete' could be changed to operate more smoothly on a filesystem that's having problems. If it does queries to find the files to delete, it might not be the open call that's timing out. To test the theory, is there some way to set up a cron job which simply removes a single file with a known name during the disk-full state? How does the 'mass delete' program decide what 'open' call to make?
It's also possible to control the percentage of disk utilization before writes stop working. You could also try setting this to a lower percentage. If you are detecting the 'disk full' state by waiting until a file-creation step returns -1, then you should consider adding an explicit check to your code so that if the filesystem is over a certain percentage full, take corrective action.
Mass delete causes a storm of random IO which really hurts performance. And it makes as much of journal/log transactions to commit (try with the nologging option ?). Moreover, if your fs is nearly full, open would anyway takes some time to find space for a new inode.
Deleting files more often, fewer at a time, may help you to get lower response time. Or simply delete them slower, sleeping between rm.