My goal is to parse a large XML file (20 GB) with Swift. There are some performance issues with NSXMLParser and bridging to Swift objects, so I'm looking at multi-threading. Specifically the following division:
Main thread - parses data
Worker thread - casts ObjC types into Swift types and sends to 1. The casting of ObjC NSDictionary to [String: String] is the largest bottleneck. This is also the main reason for separating onto multiple threads.
Worker thread - parses XML into ObjC types - and sends to 2. NSXMLParser is a push-parser, once it starts parsing, you cannot pause it.
The data should be parsed sequentially, so the input ordering should be maintained. My idea is to run an NSRunLoop on both 1 and 2, allowing parallel processing without blocking. According to Apple's documentation, communication between the threads can be achieved by calling performSelector:onThread:withObject:waitUntilDone:. However this symbol is not available in Swift.
I don't think that GCD would fit as a solution. Both worker threads should be long-running processes with new work coming in at random intervals.
How can one achieve the above (e.g. NSRunLoops on multiple threads) using Swift?
I used NSOperation for the first time last month, and it's is a really easy object to subclass, you could either chain them together with completion blocks, or you can set operations to be dependencies of each other so that they're performed sequentially.
It's also pretty easy to communicate with NSOperations by passing in objects to them.
NSHipster: http://nshipster.com/nsoperation/
Related
I want to see the intrinsic difference between a thread and a long-running go block in Clojure. In particular, I want to figure out which one I should use in my context.
I understand if one creates a go-block, then it is managed to run in a so-called thread-pool, the default size is 8. But thread will create a new thread.
In my case, there is an input stream that takes values from somewhere and the value is taken as an input. Some calculations are performed and the result is inserted into a result channel. In short, we have input and out put channel, and the calculation is done in the loop. So as to achieve concurrency, I have two choices, either use a go-block or use thread.
I wonder what is the intrinsic difference between these two. (We may assume there is no I/O during the calculations.) The sample code looks like the following:
(go-loop []
(when-let [input (<! input-stream)]
... ; calculations here
(>! result-chan result))
(recur))
(thread
(loop []
(when-let [input (<!! input-stream)]
... ; calculations here
(put! result-chan result))
(recur)))
I realize the number of threads that can be run simultaneously is exactly the number of CPU cores. Then in this case, is go-block and thread showing no differences if I am creating more than 8 thread or go-blocks?
I might want to simulate the differences in performance in my own laptop, but the production environment is quite different from the simulated one. I could draw no conclusions.
By the way, the calculation is not so heavy. If the inputs are not so large, 8,000 loops can be run in 1 second.
Another consideration is whether go-block vs thread will have an impact on GC performance.
There's a few things to note here.
Firstly, the thread pool that threads are created on via clojure.core.async/thread is what is known as a cached thread pool, meaning although it will re-use recently used threads inside that pool, it's essentially unbounded. Which of course means it could potentially hog a lot of system resources if left unchecked.
But given that what you're doing inside each asynchronous process is very lightweight, threads to me seem a little overkill. Of course, it's also important to take into account the quantity of items you expect to hit the input stream, if this number is large you could potentially overwhelm core.async's thread pool for go macros, potentially to the point where we're waiting for a thread to become available.
You also didn't mention preciously where you're getting the input values from, are the inputs some fixed data-set that remains constant at the start of the program, or are inputs continuously feed into the input stream from some source over time?
If it's the former then I would suggest you lean more towards transducers and I would argue that a CSP model isn't a good fit for your problem since you aren't modelling communication between separate components in your program, rather you're just processing data in parallel.
If it's the latter then I presume you have some other process that's listening to the result channel and doing something important with those results, in which case I would say your usage of go-blocks is perfectly acceptable.
I have a multi step Spring Batch job and in one of steps I create Lucene indices for the data read in reader so subsequent steps can search in that Lucene index.
Based on read data in ItemReader, I spread indices to few separate directories.
If I specify, Step Task Executor to be a SimpleAsyncTaskExecutor , I don't get any issue as long as indices are always written to different directories but sometimes I get a locking exception. I guess, two threads tried to write to same Index.
If I remove SimpleAsyncTaskExecutor, I don't get any issues but write becomes sequential and slow.
Is it possible to use multi threading for a Lucene Index writer if indices are being written to a single directory?
Do I need to make index creator code to be thread safe to use SimpleAsyncTaskExecutor?
index creator code is in step processor.
I am using Lucene 6.0.0 and as per IndexWriter API Doc,
NOTE: IndexWriter instances are completely thread safe, meaning
multiple threads can call any of its methods, concurrently. If your
application requires external synchronization, you should not
synchronize on the IndexWriter instance as this may cause deadlock;
use your own (non-Lucene) objects instead.
I was creating multiple instances of writer and that was causing problems. Single writer instance can be passed to as many threads as you like provided rest of the code around that writer is thread safe.
I used a single writer instance and parallelized chunks. Each parallel chunk wrote to same directory without any issues.
To parallelize chunks, I had to made my chunk components - reader , processor and writer to be thread safe.
Consider that the sequental version of the program already exists and implements a sequence of "read-compute-write" operations on a single input file and other single output file. "Read" and "write" operations are performed by the 3rd-party library functions which are hard (but possible) to modify, while the "compute" function is performed by the program itself. Read-write library functions seems to be not thread-safe, since they operate with internal flags and internal memory buffers.
It was discovered that the program is CPU-bounded, and it is planned to improve the program by taking advantage of multiple CPUs (up to 80) by designing the multi-processor version of the program and using OpenMP for that purpose. The idea is to instantiate multiple "compute" functions with same single input and single output.
It is obvious that something nedds to be done in insuring the consistent access to reads, data transfers, computations and data storages. Possible solutions are: (hard) rewrite the IO library functions in thread-safe manner, (moderate) write a thread-safe wrapper for IO functions that would also serve as a data cacher.
Is there any general patterns that cover the subject of converting, wrapping or rewriting the single-threaded code to comply with OpenMP thread-safety assumptions?
EDIT1: The program is fresh enough for changes to make it multi-threaded (or, generally a parallel one, implemented either by multi-threading, multi-processing or other ways).
As a quick response, if you are processing a single file and writing to another, with openMP its easy to convert the sequential version of the program to a multi-thread version without taking too much care about the IO part, provided that the compute algorithm itself can be parallelized.
This is true because usually the main thread, takes care of the IO. If this cannot be achieved because the chunks of data are too big to read at once, and the compute algorithm cannot process smaller chunks, you can use the openMP API to synchronize the IO in each thread. This does not mean that the whole application will stop or wait until the other threads finish computing so new data can be read or written, it means that only the read and write parts need to be done atomically.
For example, if the flow of your sequencial application is as follows:
1) Read
2) compute
3) Write
Given that it truly can be parallelized, and each chunk of data needs to be read from within each thread, each thread could follow the next design:
1) Synchronized read of chunk from input (only one thread at the time could execute this section)
2) Compute chunk of data (done in parallel)
3) Synchronized write of computed chunk to output (only one thread at the time could execute this section)
if you need to write the chunks in the same order you have read them, you need to buffer first, or adopt a different strategy like fseek to the correct position, but that really depends if the output file size is known from the start, ...
Take special attention to the openMP scheduling strategy, because the default may not be the best to your compute algorithm. And if you need to share results between threads, like the offset of the input file you have read, you may use reduction operations provided by the openMP API, which is way more efficient than making a single part of your code run atomically between all threads, just to update a global variable, openMP knows when its safe to write.
EDIT:
In regards of the "read, process, write" operation, as long as you keep each read and write atomic between every worker, I can't think any reason you'll find any trouble. Even when the data read is being stored in a internal buffer, having every worker accessing it atomically, that data is acquired in the exact same order. You only need to keep special attention when saving that chunk to the output file, because you don't know the order each worker will finish processing its attributed chunk, so, you could have a chunk ready to be saved that was read after others that are still being processed. You just need each worker to keep track of the position of each chunk and you can keep a list of pointers to chunks that need to be saved, until you have a sequence of finished chunks since the last one saved to the output file. Some additional care may need to be taken here.
If you are worried about the internal buffer itself (and keeping in mind I don't know the library you are talking about, so I can be wrong) if you make a request to some chunk of data, that internal buffer should only be modified after you requested that data and before the data is returned to you; and as you made that request atomically (meaning that every other worker will need to keep in line for its turn) when the next worker asks for his piece of data, that internal buffer should be in the same state as when the last worker received its chunk. Even in the case that the library particularly says it returns a pointer to a position of the internal buffer and not a copy of the chunk itself, you can make a copy to the worker's memory before releasing the lock on the whole atomic read operation.
If the pattern I suggested is followed correctly, I really don't think you would find any problem you wouldn't find in the same sequential version of the algorithm.
with a little of synchronisation you can go even further. Consider something like this:
#pragma omp parallel sections num_threads
{
#pragma omp section
{
input();
notify_read_complete();
}
#pragma omp section
{
wait_read_complete();
#pragma omp parallel num_threads(N)
{
do_compute_with_threads();
}
notify_compute_complete();
}
#pragma omp section
{
wait_compute_complete();
output();
}
}
So, the basic idea would be that input() and output() read/write chunks of data. The compute part then would work on a chunk of data while the other threads are reading/writing. It will take a bit of manual synchronization work in notify*() and wait*(), but that's not magic.
Cheers,
-michael
How can you use Core Data and GCD when the methods that get called within the background thread need many different NSManagedObjects? You as the caller might not have insight which objects will be needed in the sub-calls?
Think of a complex download, parsing and saving procedure with many managed objects, helper methods for dates, statuses and so on. When you start your background thread with GCD, a new NSManagedObjectContext will be needed, that's for sure. But you are not able to tell what managed objects will be needed by every sub-method. So, do you need to pass the context to every single helper method, e.g. for just telling a NSDate difference?
Is there an easy approach that doesn't blow up the lines of code?
One approach that could fit the bill of not blowing up the code could be to receive your data in your various background threads and code it all into dictionaries. If you use JSON as the transfer format, that would be very few lines of code.
Then you could pass it all to a block on the main thread to create the managed object and insert it into the managed object context. Again, that would not carry much overhead, compared to a single-threaded solution.
I have four threads in a C++/CLI GUI I'm developing:
Collects raw data
The GUI itself
A background processing thread which takes chunks of raw data and produces useful information
Acts as a controller which joins the other three threads
I've got the raw data collector working and posting results to the controller, but the next step is to store all of those results so that the GUI and background processor have access to them.
New raw data is fed in one result at a time at regular (frequent) intervals. The GUI will access each new item as it arrives (the controller announces new data and the GUI then accesses the shared buffer). The data processor will periodically read a chunk of the buffer (a seconds worth for example) and produce a new result. So effectively, there's one producer and two consumers which need access.
I've hunted around, but none of the CLI-supplied stuff sounds all that useful, so I'm considering rolling my own. A shared circular buffer which allows write-locks for the collector and read locks for the gui and data processor. This will allow multiple threads to read the data as long as those sections of the buffer are not being written to.
So my question is: Are there any simple solutions in the .net libraries which could achieve this? Am I mad for considering rolling my own? Is there a better way of doing this?
Is it possible to rephrase the problem so that:
The Collector collects a new data point ...
... which it passes to the Controller.
The Controller fires a GUI "NewDataPointEvent" ...
... and stores the data point in an array.
If the array is full (or otherwise ready for processing), the Controller sends the array to the Processor ...
... and starts a new array.
If the values passed between threads are not modified after they are shared, this might save you from needing the custom thread-safe collection class, and reduce the amount of locking required.