Processor

The Problem

Given a blackbox of monolithic data (looking from the outside, but comprised of well-defined blocks internally), how do we process it efficiently using all compute power at our disposal?

The Steps

1. Split the data to chunk, [0, N1], [N1, N2], ... [Nn, Nn+1].

2. When starting to process each chunk, first lock on the previous/next block.

3. Process the chunk and store relevant information to enable monolithic context.

struct Chunk {
    start: u64,
    end: u64,
    first_block_offset: u64,
    last_block_offset: u64,
    results: Vec<Block>,
}

struct Block {
    relative_offset: u64,
    data: Vec<u8>,
}

4. In case our monolithic data allows efficient random access, we can traverse backwards to ensure each block covers the expected range. If not, post-processing will require to detect if some small boundary blocks are missing.

5. Combine the results using the offset information.