Optimal datafile format loading on a game console - c++

Question

I need to load large models and other structured binary data on an older CD-based game console as efficiently as possible. What's the best way to do it? The data will be exported from a Python application. This is a pretty elaborate hobby project.
Requierements:
no reliance on fully standard compliant STL - i might use uSTL though.
as little overhead as possible. Aim for a solution so good. that it could be used on the original Playstation, and yet as modern and elegant as possible.
no backward/forward compatibility necessary.
no copying of large chunks around - preferably files get loaded into RAM in background, and all large chunks accessed directly from there later.
should not rely on the target having the same endianness and alignment, i.e. a C plugin in Python which dumps its structs to disc would not be a very good idea.
should allow to move the loaded data around, as with individual files 1/3 the RAM size, fragmentation might be an issue. No MMU to abuse.
robustness is a great bonus, as my attention span is very short, i.e. i'd change saving part of the code and forget the loading one or vice versa, so at least a dumb safeguard would be nice.
exchangeability between loaded data and runtime-generated data without runtime overhead and without severe memory management issues would be a nice bonus.
I kind of have a semi-plan of parsing in Python trivial, limited-syntax C headers which would use structs with offsets instead of pointers, and convenience wrapper structs/classes in the main app with getters which would convert offsets to properly typed pointers/references, but i'd like to hear your suggestions.
Clarification: the request is primarily about data loading framework and memory management issues.

Answer 1

On platforms like the Nintendo GameCube and DS, 3D models are usually stored in a very simple custom format:
A brief header, containing a magic number identifying the file, the number of vertices, normals, etc., and optionally a checksum of the data following the header (Adler-32, CRC-16, etc).
A possibly compressed list of 32-bit floating-point 3-tuples for each vector and normal.
A possibly compressed list of edges or faces.
All of the data is in the native endian format of the target platform.
The compression format is often trivial (Huffman), simple (Arithmetic), or standard (gzip). All of these require very little memory or computational power.
You could take formats like that as a cue: it's quite a compact representation.
My suggestion is to use a format most similar to your in-memory data structures, to minimize post-processing and copying. If that means you create the format yourself, so be it. You have extreme needs, so extreme measures are needed.

Answer 2

This is a common game development pattern.
The usual approach is to cook the data in an offline pre-process step. The resulting blobs can be streamed in with minimal overhead. The blobs are platform dependent and should contain the proper alignment & endian-ness of the target platform.
At runtime, you can simply cast a pointer to the in-memory blob file. You can deal with nested structures as well. If you keep a table of contents with offsets to all the pointer values within the blob, you can then fix-up the pointers to point to the proper address. This is similar to how dll loading works.
I've been working on a ruby library, bbq, that I use to cook data for my iphone game.
Here's the memory layout I use for the blob header:
// Memory layout
//
// p begining of file in memory.
// p + 0 : num_pointers
// p + 4 : offset 0
// p + 8 : offset 1
// ...
// p + ((num_pointers - 1) * 4) : offset n-1
// p + (num_pointers * 4) : num_pointers // again so we can figure out
// what memory to free.
// p + ((num_pointers + 1) * 4) : start of cooked data
//
Here's how I load binary blob file and fix up pointers:
void* bbq_load(const char* filename)
{
unsigned char* p;
int size = LoadFileToMemory(filename, &p);
if(size <= 0)
return 0;
// get the start of the pointer table
unsigned int* ptr_table = (unsigned int*)p;
unsigned int num_ptrs = *ptr_table;
ptr_table++;
// get the start of the actual data
// the 2 is to skip past both num_pointer values
unsigned char* base = p + ((num_ptrs + 2) * sizeof(unsigned int));
// fix up the pointers
while ((ptr_table + 1) < (unsigned int*)base)
{
unsigned int* ptr = (unsigned int*)(base + *ptr_table);
*ptr = (unsigned int)((unsigned char*)ptr + *ptr);
ptr_table++;
}
return base;
}
My bbq library isn't quite ready for prime time, but it could give you some ideas on how to write one yourself in python.
Good Luck!

Answer 3

I note that nowhere in your description do you ask for "ease of programming". :-)
Thus, here's what comes to mind for me as a way of creating this:
The data should be in the same on-disk format as it would be in the target's memory, such that it can simply pull blobs from disk into memory with no reformatting it. Depending on how much freedom you want in putting things into memory, the "blobs" could be the whole file, or could be smaller bits within it; I don't understand your data well enough to suggest how to subdivide it but presumably you can. Because we can't rely on the same endianness and alignment on the host, you'll need to be somewhat clever about translating things when writing the files on the host-side, but at least this way you only need the cleverness on one side of the transfer rather than on both.
In order to provide a bit of assurance that the target-side and host-side code matches, you should write this in a form where you provide a single data description and have some generation code that will generate both the target-side C code and the host-side Python code from it. You could even have your generator generate a small random "version" number in the process, and have the host-side code write this into the file header and the target-side check it, and give you an error if they don't match. (The point of using a random value is that the only information bit you care about is whether they match, and you don't want to have to increment it manually.)

Answer 4

Consider storing your data as BLOBs in a SQLite DB. SQLite is extremely portable and lighweight, ANSI C, has both C++ and Python interfaces. This will take care of large files, no fragmentation, variable-length records with fast access, and so on. The rest is just serialization of structs to these BLOBs.