Develop Reference

Creating new objects versus encoding data in primitives

Let's assume I want to store (integer) x/y-values, what is considered more efficient: Storing it in a primitive value like long (which fits perfect, due sizeof(long) = 2*sizeof(int)) using bit-operations like shift, or and a mask, or creating a Point-Class?
Keep in mind that I want to create and store many(!) of these points (in a loop). Would be there a perfomance issue when using classes? The only reason I would prefer storing in primtives over storing in class is the garbage-collector. I guess generating new objects in a loop would trigger the gc way too much, is it correct?

Of course packing those as long[] is going to take less memory (though it is going to be contiguous). For each Object (a Point) you will pay at least 12 bytes more for the two headers.
On other hand, if you are creating them in a loop and thus escape analysis can prove they don't escape, it can apply an optimization called "scalar replacement" (thought is it very fragile); where your Objects will not be allocated at all. Instead those objects will be "desugared" to fields.
The general rule is that you should code the way it is the most easy to maintain and read that code. If and only if you see performance issues (via a profiler let's say or too many pauses), only then you should look at GC logs and potentially optimize code.
As an addendum, jdk code itself is full of such long where each bit means different things - so they do pack them. But then, me and I doubt you, are jdk developers. There such things matter, for us - I have serious doubts.

Which is faster: Array list or looping through all data combinations?

I'm programming something in Java, for context see this question: Markov Model descision process in Java
I have two options:
byte[MAX][4] mypatterns;
or
ArrayList mypatterns
I can use a Java ArrayList and append a new arrays whenever I create them, or use a static array by calculating all possible data combinations, then looping through to see which indexes are 'on or off'.
Essentially, I'm wondering if I should allocate a large block that may contain uninitialized values, or use the dynamic array.
I'm running in fps, so looping through 200 elements every frame could be very slow, especially because I will have multiple instances of this loop.
Based on theory and what I have heard, dynamic arrays are very inefficient
My question is: Would looping through an array of say, 200 elements be faster than appending an object to a dynamic array?
Edit>>>
More information:
I will know the maxlength of the array, if it is static.
The items in the array will frequently change, but their sizes are constant, therefore I can easily change them.
Allocating it statically will be the likeness of a memory pool
Other instances may have more or less of the data initialized than others

You right really, I should use a profiler first, but I'm also just curious about the question 'in theory'.
The "theory" is too complicated. There are too many alternatives (different ways to implement this) to analyse. On top of that, the actual performance for each alternative will depend on the the hardware, JIT compiler, the dimensions of the data structure, and the access and update patterns in your (real) application on (real) inputs.
And the chances are that it really doesn't matter.
In short, nobody can give you an answer that is well founded in theory. The best we can give is recommendations that are based on intuition about performance, and / or based on software engineering common sense:
simpler code is easier to write and to maintain,
a compiler is a more consistent1 optimizer than a human being,
time spent on optimizing code that doesn't need to be optimized is wasted time.
1 - Certainly over a large code-base. Given enough time and patience, human can do a better job for some problems, but that is not sustainable over a large code-base and it doesn't take account of the facts that 1) compilers are always being improved, 2) optimal code can depend on things that a human cannot take into account, and 3) a compiler doesn't get tired and make mistakes.

The fastest way to iterate over bytes is as a single arrays. A faster way to process these are as int or long types as process 4-8 bytes at a time is faster than process one byte at a time, however it rather depends on what you are doing. Note: a byte[4] is actually 24 bytes on a 64-bit JVM which means you are not making efficient use of your CPU cache. If you don't know the exact size you need you might be better off creating a buffer larger than you need even if you are not using all the buffer. i.e. in the case of the byte[][] you are using 6x time the memory you really need already.

Any performance difference will not be visible, when you set initialCapacity on ArrayList. You say that your collection's size can never change, but what if this logic changes?
Using ArrayList you get access to a lot of methods such as contains.
As other people have said already, use ArrayList unless performance benchmarks say it is a bottle neck.

Using structs/classes to increase cache spatial locality?

If one took say 1000 lines of computer code and instead of the variables being declared independently, they were grouped together (obviously this does depend on the variable sizes being used) into classes and structs, would this directly increase the cache spacial locality (and therefore reduce the cache miss rate)?
I was under the impression by associating the variables within a class/struct they would be assigned continuous memory addresses?

If you are talking about method-local variables, they are already contiguous on the stack, or strictly speaking in activation records which are all but invariably on the stack. If you are talking about references to Java objects, or pointers to dynamically allocated C++ objects, putting them into containing classes won't make any difference for another reason: the objects concerned will still be in arbitrary positions in the heap.

Answering this question is not possible without making some quite unreasonable assumptions. Spatial locality is as much about algorithms as it is about data structures, so grouping logically related data elements together may be of no consequence or even worse based on an algorithm that you use.
For example, consider a representation of 100 points in 3D space. You could put them in three separate arrays, or create a 3-tuple struct/class, and make an array of these.
If your algorithm must get all three coordinates of each point at once on each step, the tuple representation wins. However, think what would happen if you wanted to build an algorithm that operates on each dimension independently, and paralelize it three-way among three independent threads. In this case three separate arrays would win hands down, because that layout would avoid false sharing, and improve spatial locality as far as the one-dimension-at-a-time algorithm is concerned.
This example shows that there is no "one size fits all" solution. Spatial locality should always be considered in the context of a specific algorithm; a good solution in one case could turn bad in other seemingly similar cases.

If you are asking whether to group local variables into explicitly defined structures, there is not going to be an advantage. Local variables are implemented in terms of activation records, which are usually closely related to the implementation of class structures, for any language that has both.
So, local variables should already have good spatial locality, unless the language implementation is doing something weird to screw it up.
You might improve locality by isolating large chunks of local state which isn't used during recursion into separate non-recursing functions. This would be a micro-optimization, so you need to inspect the machine code first to be sure it's not a waste of time. Anyway, it's unrelated to moving locals into a class.

Techniques for keeping data in the cache, locality?

For ultra-fast code it essential that we keep locality of reference- keep as much of the data which is closely used together, in CPU cache:
http://en.wikipedia.org/wiki/Locality_of_reference
What techniques are to achieve this? Could people give examples?
I interested in Java and C/C++ examples. Interesting to know of ways people use to stop lots of cache swapping.
Greetings

This is probably too generic to have clear answer. The approaches in C or C++ compared to Java will differ quite a bit (the way the language lays out objects differ).
The basic would be, keep data that will be access in close loops together. If your loop operates on type T, and it has members m1...mN, but only m1...m4 are used in the critical path, consider breaking T into T1 that contains m1...m4 and T2 that contains m4...mN. You might want to add to T1 a pointer that refers to T2. Try to avoid objects that are unaligned with respect to cache boundaries (very platform dependent).
Use contiguous containers (plain old array in C, vector in C++) and try to manage the iterations to go up or down, but not randomly jumping all over the container. Linked Lists are killers for locality, two consecutive nodes in a list might be at completely different random locations.
Object containers (and generics) in Java are also a killer, while in a Vector the references are contiguous, the actual objects are not (there is an extra level of indirection). In Java there are a lot of extra variables (if you new two objects one right after the other, the objects will probably end up being in almost contiguous memory locations, even though there will be some extra information (usually two or three pointers) of Object management data in between. GC will move objects around, but hopefully won't make things much worse than it was before it run.
If you are focusing in Java, create compact data structures, if you have an object that has a position, and that is to be accessed in a tight loop, consider holding an x and y primitive types inside your object rather than creating a Point and holding a reference to it. Reference types need to be newed, and that means a different allocation, an extra indirection and less locality.

Two common techniques include:
Minimalism (of data size and/or code size/paths)
Use cache oblivious techniques
Example for minimalism: In ray tracing (a 3d graphics rendering paradigm), it is a common approach to use 8 byte Kd-trees to store static scene data. The traversal algorithm fits in just a few lines of code. Then, the Kd-tree is often compiled in a manner that minimalizes the number of traversal steps by having large, empty nodes at the top of tree ("Surface Area Heuristics" by Havran).
Mispredictions typically have a probability of 50%, but are of minor costs, because really many nodes fit in a cache-line (consider that you get 128 nodes per KiB!), and one of the two child nodes is always a direct neighbour in memory.
Example for cache oblivious techniques: Morton array indexing, also known as Z-order-curve-indexing. This kind of indexing might be preferred if you usually access nearby array elements in unpredictable direction. This might be valuable for large image or voxel data where you might have 32 or even 64 bytes big pixels, and then millions of them (typical compact camera measure is Megapixels, right?) or even thousands of billions for scientific simulations.
However, both techniques have one thing in common: Keep most frequently accessed stuff nearby, the less frequently things can be further away, spanning the whole range of L1 cache over main memory to harddisk, then other computers in the same room, next room, same country, worldwide, other planets.

Some random tricks that come to my mind, and which some of them I used recently:
Rethink your algorithm. For example, you have an image with a shape and the processing algorithm that looks for corners of the shape. Instead of operating on the image data directly, you can preprocess it, save all the shape's pixel coordinates in a list and then operate on the list. You avoid random the jumping around the image
Shrink data types. Regular int will take 4 bytes, and if you manage to use e.g. uint16_t you will cache 2x more stuff
Sometimes you can use bitmaps, I used it for processing a binary image. I stored pixel per bit, so I could fit 8*32 pixels in a single cache line. It really boosted the performance
Form Java, you can use JNI (it's not difficult) and implement your critical code in C to control the memory

In the Java world the JIT is going to be working hard to achieve this, and trying to second guess this is likely to be counterproductive. This SO question addresses Java-specific issues more fully.

How do you make your Java application memory efficient?

How do you optimize the heap size usage of an application that has a lot (millions) of long-lived objects? (big cache, loading lots of records from a db)
Use the right data type
Avoid java.lang.String to represent other data types
Avoid duplicated objects
Use enums if the values are known in advance
Use object pools
String.intern() (good idea?)
Load/keep only the objects you need
I am looking for general programming or Java specific answers. No funky compiler switch.
Edit:
Optimize the memory representation of a POJO that can appear millions of times in the heap.
Use cases
Load a huge csv file in memory (converted into POJOs)
Use hibernate to retrieve million of records from a database
Resume of answers:
Use flyweight pattern
Copy on write
Instead of loading 10M objects with 3 properties, is it more efficient to have 3 arrays (or other data structure) of size 10M? (Could be a pain to manipulate data but if you are really short on memory...)

I suggest you use a memory profiler, see where the memory is being consumed and optimise that. Without quantitative information you could end up changing thing which either have no effect or actually make things worse.
You could look at changing the representation of your data, esp if your objects are small.
For example, you could represent a table of data as a series of columns with object arrays for each column, rather than one object per row. This can save a significant amount of overhead for each object if you don't need to represent an individual row. e.g. a table with 12 columns and 10,000,000 rows could use 12 objects (one per column) rather than 10 million (one per row)

You don't say what sort of objects you're looking to store, so it's a little difficult to offer detailed advice. However some (not exclusive) approaches, in no particular order, are:
Use a flyweight pattern wherever
possible.
Caching to disc. There are
numerous cache solutions for
Java.
There is some debate as to whether
String.intern is a good idea. See
here for a question re.
String.intern(), and the amount of
debate around its suitability.
Make use of soft or weak
references to store data that you can
recreate/reload on demand. See
here for how to use soft
references with caching techniques.
Knowing more about the internals and lifetime of the objects you're storing would result in a more detailed answer.

Ensure good normalization of your object model, don't duplicate values.
Ahem, and, if it's only millions of objects I think I'd just go for a decent 64 bit VM and lots of ram ;)

Normal "profilers" won't help you much, because you need an overview of all your "live" objects. You need heap dump analyzer. I recommend the Eclipse Memory analyzer.
Check for duplicated objects, starting with Strings.
Check whether you can apply patterns like flightweight, copyonwrite, lazy initialization (google will be your friend).

Take a look at this presentation linked from here. It lays out the memory use of common java object and primitives and helps you understand where all the extra memory goes.
Building Memory-efficient Java Applications: Practices and Challenges

You could just store fewer objects in memory. :) Use a cache that spills to disk or use Terracotta to cluster your heap (which is virtual) allowing unused parts to be flushed out of memory and transparently faulted back in.

I want to add something to the point Peter alredy made(can't comment on his answer :() it's always better to use a memory profiler(check java memory profiler) than to go by intution.80% of time it's routine that we ignore has some problem in it.also collection classes are more prone to memory leaks.

If you have millions of Integers and Floats etc. then see if your algorithms allow for representing the data in arrays of primitives. That means fewer references and lower CPU cost of each garbage collection.

A fancy one: keep most data compressed in ram. Only expand the current working set. If your data has good locality that can work nicely.
Use better data structures. The standard collections in java are rather memory intensive.
[what is a better data structure]
If you take a look at the source for the collections, you'll see that if you restrict yourself in how you access the collection, you can save space per element.
The way the collection handle growing is no good for large collections. Too much copying. For large collections, you need some block-based algorithm, like btree.

Spend some time getting acquainted with and tuning the VM command line options, especially those concerning garbage collection. While this won't change the memory used by your objects, it can have a big impact on performance with memory-intensive apps on machines with a lot of RAM.

Assign null value to all the variables which are no longer used. Thus make it available for Garbage collection.
De-reference the collections once usage is over, otherwise GC won't sweep those.

1) Use right dataTypes wherever possible
Class Person {
int age;
int status;
}
Here we can use below variables to save memory while sending Person object
class Person{
short age;
byte status;
}
2) Instead of returning new ArrayList<>(); from method , you can use Collection.emptyList() which will only contain only one element instead of default 10;
For e.g
public ArrayList getResults(){
.....
if(failedOperation)
return new ArrayList<>();
}
//Use this
public ArrayList getResults(){
if(failedOperation)
return Collections.emptyList();
}
3 ) Move creation of objects in methods instead of static declaration wherever possible as fields of objects will be stored on stack instead of heap
4) Using binary formats like protobuf,thrift,avro,messagepack for reducing intercommunication instead of json or XML