For a university project I am using bifacial_radiance v0.4.0 to run simulations of approx. 270 000 rows of data in an EWP file.
I have set up a scene with some panels in a module following a tutorial on the bifacial_radiance GitHub page.
I am running the python script for this on a high power computer with 64 cores. Since python natively only uses 1 processor I want to use multiprocessing, which is currently working. However it does not seem very fast, even when starting 64 processes it uses roughly 10 % of the CPU's capacity (according to the task manager).
The script will first create the scene with panels.
Then it will look at a result file (where I store results as csv), and compare it to the contents of the radObj.metdata object. Both metdata and my result file use dates, so all dates which exist in the metdata file but not in the result file are stored in a queue object from the multiprocessing package. I also initialize a result queue.
I want to send a lot of the work to other processors.
To do this I have written two function:
A file writer function which every 10 seconds gets all items from the result queue and writes them to the result file. This function is running in a single multiprocessing.Process process like so:
fileWriteProcess = Process(target=fileWriter,args=(resultQueue,resultFileName)).start()
A ray trace function with a unique ID which does the following:
Get an index ìdx from the index queue (described above)
Use this index in radObj.gendaylit(idx)
Create the octfile. For this I have modified the name which the octfile is saved with to use a prefix which is the name of the process. This is to avoid all the processes using the same octfile on the SSD. octfile = radObj.makeOct(prefix=name)
Run an analysis analysis = bifacial_radiance.AnalysisObj(octfile,radObj.basename)
frontscan, backscan = analysis.moduleAnalysis(scene)
frontDict, backDict = analysis.analysis(octfile, radObj.basename, frontscan, backscan)
Read the desired results from resultDict and put them in the resultQueue as a single line of comma-separated values.
This all works. The processes are running after being created in a for loop.
This speeds up the whole simulation process quite a bit (10 days down to 1½ day), but as said earlier the CPU is running at around 10 % capacity and the GPU is running around 25 % capacity. The computer has 512 GB ram which is not an issue. The only communication with the processes is through the resultQueue and indexQueue, which should not bottleneck the program. I can see that it is not synchronizing as the results are written slightly unsorted while the input EPW file is sorted.
My question is if there is a better way to do this, which might make it run faster? I can see in the source code that a boolean "hpc" is used to initiate some of the classes, and a comment in the code mentions that it is for multiprocessing, but I can't find any information about it elsewhere.
I'm working on a golang script who get fileInfo and owner from every file using go 1.10.3 on a 32 bit windows.
My script work well but im facing a problem.
The library advapi32 and more specificly GetNamedSecurityInfoW and LookupAccountNameW functions take around 1 millisecond since I've got a big amount of files the "scan" is obviously too long.
I just wanted too now if there's another way to do it faster ?
I'm open on any suggetion or help Thanks.
Thank you gonutz i managed to make it quite faster with goroutine, I aproach 2 500 000 files every 22minute. And my algorythm is still not optimised.
I am using wkhtmltopdf on my ubuntu server to generate pdfs out of html-templates.
wkhtmltopdf is therefore started from a php-script with shell_exec.
My problem is, that I want to create up to 200 pdfs at (almost) the same time, which makes the runtime of wkhtmltopdf kind of stack for every pdf. One file needs 0.6 seconds, 15 files need 9 seconds.
My idea was to start wkhtmltopdf in a screen-session to decrease the runtime, but I can't make it work from php plus this might not make that much sense, because I want to additionally summarize all pdfs in one after creation, so I would have to check if every session is terminated?!
Do you have any ideas how I can decrease the runtime for this amount of pdfs or can you give me advice how to realize this correctly and smart with screen?
My script looks like the following:
loop up to 200times {
- get data for html-template from database
- fill template-string and write .html-file
- create pdf out of html-template via shell_exec("wkhtmltopdf....")
- delete template-file
}
merge all generated pdfs together to one and send it via mail
Thank you in advance and sorry for my bad english.
best wishes
Just create a single large HTML file and convert it in one pass instead of merging multiple PDFs afterwards.
I have a vast quantity of data (>800Mb) that takes an age to load into Matlab mainly because it's split up into tiny files each <20kB. They are all in a proprietary format which I can read and load into Matlab, its just that it takes so long.
I am thinking of reading the data in and writing it out to some sort of binary file which should make it quicker for subsequent reads (of which there may be many, hence me needing a speed-up).
So, my question is, what would be the best format to write them to disk to make reading them back again as quick as possible?
I guess I have the option of writing using fwrite, or just saving the variables from matlab. I think I'd prefer the fwrite option so if needed, I could read them from another package/language...
Look in to the HDF5 data format, used by recent versions of MATLAB as the underlying format for .mat files. You can manually create your own HDF5 files using the hdf5write function, and this file can be accessed from any language that has HDF bindings (most common languages do, or at least offer a way to integrate C code that can call the HDF5 library).
If your data is numeric (and of the same datatype), you might find it hard to beat the performance of plain binary (fwrite).
Binary mat-files are the fastest. Just use
save myfile.mat <var_a> <var_b> ...
I achieved an amazing speed up in loading when I used the '-v6' option to save the .mat files like so:
save(matlabTrainingFile, 'Xtrain', 'ytrain', '-v6');
Here's the size of the matrices that I used in my test ...
Attr Name Size Bytes Class
==== ==== ==== ===== =====
g Xtest 1430x4000 45760000 double
g Xtrain 3411x4000 109152000 double
g Xval 1370x4000 43840000 double
g ytest 1430x1 11440 double
g ytrain 3411x1 27288 double
g yval 1370x1 10960 double
... and the performance improvements that we achieved:
Before the change:
time to load the training data: 78 SECONDS!!!
time to load validation data: 32
time to load the test data: 35
After the change:
time to load the training data: 0 SECONDS!!!
time to load validation data: 0
time to load the test data: 0
Apparently the reason the reason this works so well is that the old version 6 version used less compression the than new versions.
So your file sizes will be bigger, but they will load WAY faster.
Previously, I asked the question.
The problem is the demands of our file structure are very high.
For instance, we're trying to create a container with up to 4500 files and 500mb data.
The file structure of this container consists of
SQLite DB (under 1mb)
Text based xml-like file
Images inside a dynamic folder structure that make up the rest of the 4,500ish files
After the initial creation the images files are read only with the exception of deletion.
The small db is used regularly when the container is accessed.
Tar, Zip and the likes are all too slow (even with 0 compression). Slow is subjective I know, but to untar a container of this size is over 20 seconds.
Any thoughts?
As you seem to be doing arbitrary file system operations on your container (say, creation, deletion of new files in the container, overwriting existing files, appending), I think you should go for some kind of file system. Allocate a large file, then create a file system structure in it.
There are several options for the file system available: for both Berkeley UFS and Linux ext2/ext3, there are user-mode libraries available. It might also be possible that you find a FAT implementation somewhere. Make sure you understand the structure of the file system, and pick one that allows for extending - I know that ext2 is fairly easy to extend (by another block group), and FAT is difficult to extend (need to append to the FAT).
Alternatively, you can put a virtual disk format yet below the file system, allowing arbitrary remapping of blocks. Then "free" blocks of the file system don't need to appear on disk, and you can allocate the virtual disk much larger than the real container file will be.
Three things.
1) What Timothy Walters said is right on, I'll go in to more detail.
2) 4500 files and 500Mb of data is simply a lot of data and disk writes. If you're operating on the entire dataset, it's going to be slow. Just I/O truth.
3) As others have mentioned, there's no detail on the use case.
If we assume a read only, random access scenario, then what Timothy says is pretty much dead on, and implementation is straightforward.
In a nutshell, here is what you do.
You concatenate all of the files in to a single blob. While you are concatenating them, you track their filename, the file length, and the offset that the file starts within the blob. You write that information out in to a block of data, sorted by name. We'll call this the Table of Contents, or TOC block.
Next, then, you concatenate the two files together. In the simple case, you have the TOC block first, then the data block.
When you wish to get data from this format, search the TOC for the file name, grab the offset from the begining of the data block, add in the TOC block size, and read FILE_LENGTH bytes of data. Simple.
If you want to be clever, you can put the TOC at the END of the blob file. Then, append at the very end, the offset to the start of the TOC. Then you lseek to the end of the file, back up 4 or 8 bytes (depending on your number size), take THAT value and lseek even farther back to the start of your TOC. Then you're back to square one. You do this so you don't have to rebuild the archive twice at the beginning.
If you lay out your TOC in blocks (say 1K byte in size), then you can easily perform a binary search on the TOC. Simply fill each block with the File information entries, and when you run out of room, write a marker, pad with zeroes and advance to the next block. To do the binary search, you already know the size of the TOC, start in the middle, read the first file name, and go from there. Soon, you'll find the block, and then you read in the block and scan it for the file. This makes it efficient for reading without having the entire TOC in RAM. The other benefit is that the blocking requires less disk activity than a chained scheme like TAR (where you have to crawl the archive to find something).
I suggest you pad the files to block sizes as well, disks like work with regular sized blocks of data, this isn't difficult either.
Updating this without rebuilding the entire thing is difficult. If you want an updatable container system, then you may as well look in to some of the simpler file system designs, because that's what you're really looking for in that case.
As for portability, I suggest you store your binary numbers in network order, as most standard libraries have routines to handle those details for you.
Working on the assumption that you're only going to need read-only access to the files why not just merge them all together and have a second "index" file (or an index in the header) that tells you the file name, start position and length. All you need to do is seek to the start point and read the correct number of bytes. The method will vary depending on your language but it's pretty straight forward in most of them.
The hardest part then becomes creating your data file + index, and even that is pretty basic!
An ISO disk image might do the trick. It should be able to hold that many files easily, and is supported by many pieces of software on all the major operating systems.
First, thank-you for expanding your question, it helps a lot in providing better answers.
Given that you're going to need a SQLite database anyway, have you looked at the performance of putting it all into the database? My experience is based around SQL Server 2000/2005/2008 so I'm not positive of the capabilities of SQLite but I'm sure it's going to be a pretty fast option for looking up records and getting the data, while still allowing for delete and/or update options.
Usually I would not recommend to put files inside the database, but given that the total size of all images is around 500MB for 4500 images you're looking at a little over 100K per image right? If you're using a dynamic path to store the images then in a slightly more normalized database you could have a "ImagePaths" table that maps each path to an ID, then you can look for images with that PathID and load the data from the BLOB column as needed.
The XML file(s) could also be in the SQLite database, which gives you a single 'data file' for your app that can move between Windows and OSX without issue. You can simply rely on your SQLite engine to provide the performance and compatability you need.
How you optimize it depends on your usage, for example if you're frequently needing to get all images at a certain path then having a PathID (as an integer for performance) would be fast, but if you're showing all images that start with "A" and simply show the path as a property then an index on the ImageName column would be of more use.
I am a little concerned though that this sounds like premature optimization, as you really need to find a solution that works 'fast enough', abstract the mechanics of it so your application (or both apps if you have both Mac and PC versions) use a simple repository or similar and then you can change the storage/retrieval method at will without any implication to your application.
Check Solid File System - it seems to be what you need.