Related
I'm currently writing my own graphics framework for DirectX12 (I've already written several DirectX 11 frameworks for personal game engines), and I'm currently trying to copy the methods used in the recent Hitman game for resource binding.
I'm confused about the best way to handle per-object resource binding for the SRV/CBV/UAV heap. I've watched several GDC presentations, and they all seem to gloss over this.
Only 1 SRV/CBV/UAV heap can be bound at a time, and switching the currently-bound heap in the middle of a command list can be bad for performance on some hardware by forcing a flush. Because of this, what is the best way to handle updating the heap with new descriptors? To me, it seems like each command list would:
Get a hold of a SRV/CBV/UAV heap for itself.
For each object in a subset of objects, create descriptors on the heap pointing to per-object data that was placed into a separate upload heap.
Afterwards, another command list takes this filled descriptor heap and binds it, then issues draw calls mixed with SetGraphicsRootDescriptorTable in order to move through the current descriptor heap.
This being said, several sources online (including another SO post) suggest using one large SRV/CBV/UAV heap and copying into it using CPU-visible heaps. I'm assuming they're not attempting to use the asynchronous CopyDescriptors, but rather CopyBufferRegion. I tried using CopyBufferRegion to update data per-object, but to me this seems under-performant with so many transitions between D3D12_RESOURCE_STATE_VERTEX_AND_CONSTANT_BUFFER and D3D12_RESOURCE_STATE_COPY_DEST. Am I misunderstanding something? Any clarity would be appreciated.
CopyDescriptors is not asynchronous, it is a CPU operation that is immediate on the CPU. It can happen anytime before a command list is executed for volatile descriptor ( after the command list operation using it is recorded ), or have to be ready at the usage for static descriptor ( root signature 1.1 ).
The usual approach is to have a large descriptor heap, keep a portion for static descriptors, then use the rest as a ring buffer, allocating descriptor table offset on demand to copy and use the needed descriptor for any draw/compute operation.
CopyBufferRegion has nothing to do here, remember that mapping buffers is also an immediate operation, so you also ring buffer a big chunk of memory for your per objet constant buffers, and you cycle into it. The only thing is that you need to make sure you do not overwrite memory or descriptor while they may still be in use, so you have to fence to prevent the case.
I have a couple of database management tasks that need to go through every record in the database. It was my understanding that with the CakePHP 3.x ORM, I could do something like this, and it would only ever have one record in memory at a time:
$records = TableRegistry::get('Whatever')->find();
foreach ($records as $record) {
// do some processing
}
However, this is eventually crashing with an "out of memory" exception. I've added a bit of logging of memory_get_peak_usage, and it's increasing with every iteration, even if there is nothing other than the logging happening inside the foreach loop. The delta is around 12K every time through the loop.
I'm running 3.2.7, and results are similar whether I have debugging and/or SQL logging enabled or not. Adding frequent calls to gc_collect_cycles() only slows the process down, it doesn't help with the memory usage.
Is this expected, or a bug? If the former, is there anything I can do differently in this code to prevent it? (Obviously, I could process it in smaller batches, but that's not an elegant solution.)
CakePHP 3.x ORM has built in query caching for the ResultSet object. When you iterator over the result set the entities are stored in an internal array. This is done so that you can rewind the iterator and loop again.
If you are going to iterate over a large result set only once, and you want to reduce memory usage then you have to disable result buffering.
$records = TableRegistry::get('Whatever')->find()->bufferResults(false);
foreach ($records as $record) {
// do some processing
}
With buffering turned off the entity is fetched from the result set and there should be no references to it afterwards.
Documentation for this feature is available in the CakePHP book: https://book.cakephp.org/3.0/en/orm/retrieving-data-and-resultsets.html#working-with-result-sets
Here's the API reference: https://api.cakephp.org/3.6/class-Cake.Database.Query.html#_bufferResults
From my understanding it is the expected behaviour, as you execute the query build with the ORM when you start iterating over the object($records). Thus all the data is loaded into memory, and you then iterate over each entry one by one.
If you want to limit the memory usage I would suggest you look into limit and offset. With these you can extract subsets to work on, thus limiting memory usage.
I'm doing scientific research, processing through millions of combinations of multi-megabyte arrays.
For you to be capable of answering this question you will need to have knowledge/experience of all of the following
how HHVM is able to cache data structures in RAM between requests
how to tell HHVM data structures will be constant
how to declare array index and value types
I need to process the entire arrays, so it's a lot of data to be loaded and processed. (millions of requests within minutes on a LAN). The faster I can complete requests the quicker I can complete my work. If HHVM has to do work loading this data on each request, it accounts for a significant fraction of the time to complete the request (sometimes more than half, it depends on the complexity of the analysis I'm doing at the time).
I have found a method that has allowed me to keep these data structures cached in RAM (no loading from files, interpreting code, pushing to the array hundreds of thousands of times for no reason, no pointless repetitive unserialize etc), and thus I have eliminated this massive measurable delay.
I have 3 questions regarding how I can make this even faster:
Is the way I'm doing it now creating a global scope penalty?
How can I declare my arrays as constant and tell HHVM what data types to expect?
If I declare my arrays as constant is it even necessary to declare the types for HHVM?
Instead of using nested arrays, if I use 3 separate data structures ImmVector, PackedArray, or define a class would it be faster?
Keep in mind that anything that prevents HHVM from caching the data structure in RAM between requests should be regarded as unacceptable.
Lookuptable35543.php
<?php
$data = [
["uuid (20 chars)", 5336, 7373],
["uuid (20 chars)", 5336, 7373],
#more lines as above
];
?>
Some of these files are many MB in size and there are a lot of them
Main.php
<?php
function main() {
require /path/to/Lookuptable35543.php;
#(Do stuff with $data)
}
?>
This is working quite well, as Main.php gets thousands of requests, in a short period of time, HHVM keeps Lookuptable.php's data structure in memory. Avoiding pointless processing and IO, as it just sits in RAM, ready for use. (I have more than enough RAM)
Unfortunately, the only way I know how to make HHVM hold the lookup table in RAM is, I set $data in the global scope inside my lookup####.php file (then require the lookup file into a function in the data processing file: Main.php)? This way HHVM doesnt bother loading the file or re executing the code to create $data, because it can see that $data can be determined at compile time, and it will not ever change during runtime. This works but I dont know if there is a penalty from having the $data exist in the lookup###.php file's global scope. (Or maybe its not global at all because it is required into main.php's function?)
What if I return $data from a function inside Lookup.php and call that function from Main.php like this
Main.php
Would the HHVM JIT the result of getData() in RAM?
Somehow I associate functions with unpredictability... but maybe HHVM is clever enough to know that the functions result can be determined at compile time, and never changes?
I can't put the lookup table inside Main.php because I require different lookup tables based on the type of request.
Is there a way I can tell HHVM that my outer array will always have an integer index that never changes, and the values of the outer array will always be an array?
Perhaps I need to use ImmVector?
Then is there a way to tell HHVM that my inner array will always be a fixed length string followed by 2 integers, always, no extra elements, contents never changes?
I'd prefer not to use OO or create a class. How can I declare types, procedural style?
If a class is absolutely necessary can you please give example code suitable for my requirements above?
Will it be faster if I dont nest arrays?
I just realized I could have one array with integer index and values of fixed length string. Then a 2nd array with integer index and integer values, and a 3rd one with integer index and integer values.
If you're not familiar with this HHVM caching technique please do not waste mutual time suggesting a database, redis, APC, unserialize, etc. The fastest is for HHVM to just keep my various $data variables in RAM. Even unserializing $data from a ramdisk file is slow, because then the entire data structure must be parsed as a string and converted into a data structure in memory for every request. APC has the same problem as far as i know. I dont want to even have to copy $data. The lookup tables are immutable, read only. They must just stay fully structured in RAM. My current caching solution (at the top of this question) has already given me huge gains, but as per my 3 questions I think there may be more gains to be had?
Incase you're wondering, I have measured the latency of various data loading or caching methods.
Now I basically want to keep the caching situation I have, but give the HHVM JIT maximum confidence about how to type my data, so it can save time not running type or even bound (array size) checks.
Edit
Ok so nobody has been able to give me any code examples yet, so I'm just trying stuff out.
Here's what I've found out so far.
const arrays don't work yet in HHVM. const foo = ['uuid1',43,43];
throws an error about HHVM only supporting constants with scalar values.
Vector with Array values: I don't know how it will perform yet... I expect it will be better than a normal array. This is valid HH code.
This is progress, because HHVM should be able to cache this in the same way, HHVM knows this whole structure is constant, and HHVM knows the indexes are all integers.
What I'm still not entirely happy about with this structure is this:
Consider this code
for ($n=0;$n<count($iv);++$n) if ($x > $iv[$n][1]) dosomething();
Will HHVM perform a type check on $if[$n][2] on every loop iteration?
In my definition of $iv above, there is nothing that says the 2nd element of the inner array will be an integer.
How can I improve on this?
Can disabling the type checker be of any use? Does this only hide errors from the external type checker, or does it prevent HHVM from constantly doing type checks? (I'm thinking it's the first thing)
Perhaps if I could make my own user-defined type that would solve the problem?
<?hh
#I don't know what mechanisms for UDT's exist, so this code is made-up
CreateUDT foo = <string,int,int>;
$iv = ImmVector<foo> {
['uuid1',425,244],
['uuid2',658,836]
};
print_r($iv);
I found a reference to this at Hack Collections Literal Syntax Vector<Foo> unfortunately it might not be available to use yet.
I'm a software engineer at Facebook working on HHVM.
This entire question reeks of premature optimization to me. Have you done profiling and determined that loading this array is actually a bottleneck for your app? (Not just microbenchmarks, but how it actually affects the performance, latency, RPS, etc of realistic pageloads.) And also isolated from other effects, e.g., if this array is a cache or some sort of precomputed data, you need to isolate the win of precomputing the data from the actual time to load it by caching it in various different ways.
In general, HHVM is very good at dealing with arrays, since they are so hot in nearly every codepath -- and in particular at constant arrays like this one. To your questions about how to inform it of the shape and types of things in the arrays, HHVM can figure that all out for itself, and is very good at doing so on constant arrays composed entirely of constants. (And the ways it thinks about arrays aren't quite the ways you think about arrays, so it can probably do a better job anyway!) Basically, unless profiling says this is actually a hotspot -- which I'm pretty skeptical of -- I wouldn't worry too much about it. A couple general notes to be aware of:
Measure every performance diff. Don't prematurely optimize -- use profiling to guide. The developer productivity lost by premature optimizations getting in the way can be lethal.
Get things out of toplevel ("pseudomains") as much as possible. A function which returns a static or constant array should be just fine, and will in general help HHVM optimize code even better.
Avoid references as much as possible, especially in this array if you care about performance so much.
You probably should look into repo authoritiative mode which can help HHVM optimize lots of things even more -- but in particular for this case, the more aggressive inlining that repo auth mode can do might be a win.
Edit, aside:
because then the entire data structure must be parsed as a string and converted into a data structure in memory for every request. APC has the same problem as far as i know
This is exactly what I mean by premature optimization: you're rejecting APC without even trying it, even if it might be a cleaner way of doing what you want. It turns out that, in most cases, HHVM actually can optimize away the serialization/deserialization of storing arrays in APC, particularly if they are constant arrays that are never modified. As above, HHVM is very good at optimizing lots of common patterns. Just write code that's clean, profile it, and fix the hotspots.
Okay I've solved my first question.
I don't have any global scope issues. My require is being done from inside function main(), so it's as if the code from lookuptable####.php is being inserted into function main().
HHVM docs: "If the include occurs inside a function..."
Basically if you were to open lookuptable####.php it looks like the code is in global scope, but that's not the file that is being requested from hhvm. main.php is the one being requested, thus there is no code in global scope.
I think I've answered my 2nd question, it's currently at the bottom of my question. I'm not 100% convinced, but I'm pretty happy to move ahead and test it.
I am testing some databases components such as SDAC and others and I found out something interesting:
When I execute a query with TADOQuery and this query has a lot of blob fields and I get all rows (fetchall) the memory of my application gets close to 1.8GB and everything works fine.
Using other components, the same query executed on the same database trows an Out of Memory exception because it exceeds 1.8GB of memory usage.
I know I should not return all those rows, i should use pagination and blablabla. But i am curious how can ADO manage to get all rows and other components cant.
I think that ADO is compressing the blobs in memory, but this is only a guess.
Does anyone knows why memory usage in ADO is so good?
I cannot say about SDAC, but will say about AnyDAC TADQuery:
if exclude fiBlobs from FetchOptions.Items, then AnyDAC will not fetch BLOB values immediately. But will defer fetching until the application will really need a BLOB value;
setting FormatOptions.InlineDataSize to more small value, will reduce memory usage on fetching large result set with multiple character fields;
specifying FormatOptions.MapRules, application may choose more compact data type representation.
Also there are few other techniques, allowing to reduce memory usage when fetching large result sets. To use them properly a developer should know what kind of data will be returned. The price of some options usage may be a slightly reduced fetch performance.
I have a choice.
I have a number of already ordered strings that I need to store and access. It looks like I can choose between using:
A TStringList
A Dynamic Array of strings, and
A Linked List of strings (singly linked)
and Alan in his comment suggested I also add to the choices:
TList<string>
In what circumstances is each of these better than the others?
Which is best for small lists (under 10 items)?
Which is best for large lists (over 1000 items)?
Which is best for huge lists (over 1,000,000 items)?
Which is best to minimize memory use?
Which is best to minimize loading time to add extra items on the end?
Which is best to minimize access time for accessing the entire list from first to last?
On this basis (or any others), which data structure would be preferable?
For reference, I am using Delphi 2009.
Dimitry in a comment said:
Describe your task and data access pattern, then it will be possible to give you an exact answer
Okay. I've got a genealogy program with lots of data.
For each person I have a number of events and attributes. I am storing them as short text strings but there are many of them for each person, ranging from 0 to a few hundred. And I've got thousands of people. I don't need random access to them. I only need them associated as a number of strings in a known order attached to each person. This is my case of thousands of "small lists". They take time to load and use memory, and take time to access if I need them all (e.g. to export the entire generated report).
Then I have a few larger lists, e.g. all the names of the sections of my "virtual" treeview, which can have hundreds of thousands of names. Again I only need a list that I can access by index. These are stored separately from the treeview for efficiency, and the treeview retrieves them only as needed. This takes a while to load and is very expensive memory-wise for my program. But I don't have to worry about access time, because only a few are accessed at a time.
Hopefully this gives you an idea of what I'm trying to accomplish.
p.s. I've posted a lot of questions about optimizing Delphi here at StackOverflow. My program reads 25 MB files with 100,000 people and creates data structures and a report and treeview for them in 8 seconds but uses 175 MB of RAM to do so. I'm working to reduce that because I'm aiming to load files with several million people in 32-bit Windows.
I've just found some excellent suggestions for optimizing a TList at this StackOverflow question:
Is there a faster TList implementation?
Unless you have special needs, a TStringList is hard to beat because it provides the TStrings interface that many components can use directly. With TStringList.Sorted := True, binary search will be used which means that search will be very quick. You also get object mapping for free, each item can also be associated with a pointer, and you get all the existing methods for marshalling, stream interfaces, comma-text, delimited-text, and so on.
On the other hand, for special needs purposes, if you need to do many inserts and deletions, then something more approaching a linked list would be better. But then search becomes slower, and it is a rare collection of strings indeed that never needs searching. In such situations, some type of hash is often used where a hash is created out of, say, the first 2 bytes of a string (preallocate an array with length 65536, and the first 2 bytes of a string is converted directly into a hash index within that range), and then at that hash location, a linked list is stored with each item key consisting of the remaining bytes in the strings (to save space---the hash index already contains the first two bytes). Then, the initial hash lookup is O(1), and the subsequent insertions and deletions are linked-list-fast. This is a trade-off that can be manipulated, and the levers should be clear.
A TStringList. Pros: has extended functionality, allowing to dynamically grow, sort, save, load, search, etc. Cons: on large amount of access to the items by the index, Strings[Index] is introducing sensible performance lost (few percents), comparing to access to an array, memory overhead for each item cell.
A Dynamic Array of strings. Pros: combines ability to dynamically grow, as a TStrings, with the fastest access by the index, minimal memory usage from others. Cons: limited standard "string list" functionality.
A Linked List of strings (singly linked). Pros: the linear speed of addition of an item to the list end. Cons: slowest access by the index and searching, limited standard "string list" functionality, memory overhead for "next item" pointer, spead overhead for each item memory allocation.
TList< string >. As above.
TStringBuilder. I does not have a good idea, how to use TStringBuilder as a storage for multiple strings.
Actually, there are much more approaches:
linked list of dynamic arrays
hash tables
databases
binary trees
etc
The best approach will depend on the task.
Which is best for small lists (under
10 items)?
Anyone, may be even static array with total items count variable.
Which is best for large lists (over 1000 items)?
Which is best for huge lists (over 1,000,000 items)?
For large lists I will choose:
- dynamic array, if I need a lot of access by the index or search for specific item
- hash table, if I need to search by the key
- linked list of dynamic arrays, if I need many item appends and no access by the index
Which is best to minimize memory use?
dynamic array will eat less memory. But the question is not about overhead, but about on which number of items this overhead become sensible. And then how to properly handle this number of items.
Which is best to minimize loading time to add extra items on the end?
dynamic array may dynamically grow, but on really large number of items, memory manager may not found a continous memory area. While linked list will work until there is a memory for at least a cell, but for cost of memory allocation for each item. The mixed approach - linked list of dynamic arrays should work.
Which is best to minimize access time for accessing the entire list from first to last?
dynamic array.
On this basis (or any others), which data structure would be preferable?
For which task ?
If your stated goal is to improve your program to the point that it can load genealogy files with millions of persons in it, then deciding between the four data structures in your question isn't really going to get you there.
Do the math - you are currently loading a 25 MB file with about 100000 persons in it, which causes your application to consume 175 MB of memory. If you wish to load files with several millions of persons in it you can estimate that without drastic changes to your program you will need to multiply your memory needs by n * 10 as well. There's no way to do that in a 32 bit process while keeping everything in memory the way you currently do.
You basically have two options:
Not keeping everything in memory at once, instead using a database, or a file-based solution which you load data from when you need it. I remember you had other questions about this already, and probably decided against it, so I'll leave it at that.
Keep everything in memory, but in the most space-efficient way possible. As long as there is no 64 bit Delphi this should allow for a few million persons, depending on how much data there will be for each person. Recompiling this for 64 bit will do away with that limit as well.
If you go for the second option then you need to minimize memory consumption much more aggressively:
Use string interning. Every loaded data element in your program that contains the same data but is contained in different strings is basically wasted memory. I understand that your program is a viewer, not an editor, so you can probably get away with only ever adding strings to your pool of interned strings. Doing string interning with millions of string is still difficult, the "Optimizing Memory Consumption with String Pools" blog postings on the SmartInspect blog may give you some good ideas. These guys deal regularly with huge data files and had to make it work with the same constraints you are facing.
This should also connect this answer to your question - if you use string interning you would not need to keep lists of strings in your data structures, but lists of string pool indexes.
It may also be beneficial to use multiple string pools, like one for names, but a different one for locations like cities or countries. This should speed up insertion into the pools.
Use the string encoding that gives the smallest in-memory representation. Storing everything as a native Windows Unicode string will probably consume much more space than storing strings in UTF-8, unless you deal regularly with strings that contain mostly characters which need three or more bytes in the UTF-8 encoding.
Due to the necessary character set conversion your program will need more CPU cycles for displaying strings, but with that amount of data it's a worthy trade-off, as memory access will be the bottleneck, and smaller data size helps with decreasing memory access load.
One question: How do you query: do you match the strings or query on an ID or position in the list?
Best for small # strings:
Whatever makes your program easy to understand. Program readability is very important and you should only sacrifice it in real hotspots in your application for speed.
Best for memory (if that is the largest constrained) and load times:
Keep all strings in a single memory buffer (or memory mapped file) and only keep pointers to the strings (or offsets). Whenever you need a string you can clip-out a string using two pointers and return it as a Delphi string. This way you avoid the overhead of the string structure itself (refcount, length int, codepage int and the memory manager structures for each string allocation.
This only works fine if the strings are static and don't change.
TList, TList<>, array of string and the solution above have a "list" overhead of one pointer per string. A linked list has an overhead of at least 2 pointers (single linked list) or 3 pointers (double linked list). The linked list solution does not have fast random access but allows for O(1) resizes where trhe other options have O(lgN) (using a factor for resize) or O(N) using a fixed resize.
What I would do:
If < 1000 items and performance is not utmost important: use TStringList or a dyn array whatever is easiest for you.
else if static: use the trick above. This will give you O(lgN) query time, least used memory and very fast load times (just gulp it in or use a memory mapped file)
All mentioned structures in your question will fail when using large amounts of data 1M+ strings that needs to be dynamically chaned in code. At that Time I would use a balances binary tree or a hash table depending on the type of queries I need to maken.
From your description, I'm not entirely sure if it could fit in your design but one way you could improve on memory usage without suffering a huge performance penalty is by using a trie.
Advantages relative to binary search tree
The following are the main advantages
of tries over binary search trees
(BSTs):
Looking up keys is faster. Looking up a key of length m takes worst case
O(m) time. A BST performs O(log(n))
comparisons of keys, where n is the
number of elements in the tree,
because lookups depend on the depth of
the tree, which is logarithmic in the
number of keys if the tree is
balanced. Hence in the worst case, a
BST takes O(m log n) time. Moreover,
in the worst case log(n) will approach
m. Also, the simple operations tries
use during lookup, such as array
indexing using a character, are fast
on real machines.
Tries can require less space when they contain a large number of short
strings, because the keys are not
stored explicitly and nodes are shared
between keys with common initial
subsequences.
Tries facilitate longest-prefix matching, helping to find the key
sharing the longest possible prefix of
characters all unique.
Possible alternative:
I've recently discovered SynBigTable (http://blog.synopse.info/post/2010/03/16/Synopse-Big-Table) which has a TSynBigTableString class for storing large amounts of data using a string index.
Very simple, single layer bigtable implementation, and it mainly uses disc storage, to consumes a lot less memory than expected when storing hundreds of thousands of records.
As simple as:
aId := UTF8String(Format('%s.%s', [name, surname]));
bigtable.Add(data, aId)
and
bigtable.Get(aId, data)
One catch, indexes must be unique, and the cost of update is a bit high (first delete, then re-insert)
TStringList stores an array of pointer to (string, TObject) records.
TList stores an array of pointers.
TStringBuilder cannot store a collection of strings. It is similar to .NET's StringBuilder and should only be used to concatenate (many) strings.
Resizing dynamic arrays is slow, so do not even consider it as an option.
I would use Delphi's generic TList<string> in all your scenarios. It stores an array of strings (not string pointers). It should have faster access in all cases due to no (un)boxing.
You may be able to find or implement a slightly better linked-list solution if you only want sequential access. See Delphi Algorithms and Data Structures.
Delphi promotes its TList and TList<>. The internal array implementation is highly optimized and I have never experienced performance/memory issues when using it. See Efficiency of TList and TStringList