The FastLanes Unified Transport Layout

Delta encoding normally forces each output value to depend on the previous one, which makes SIMD acceleration difficult because each lane would need to read from a neighboring lane. UTL tackles this by breaking the input into multiple independent streams and arranging them so that each stream occupies a separate SIMD lane. The result is that the delta computation for each lane can proceed without cross-lane dependencies, turning a sequential bottleneck into a data-parallel workload.

The layout assumes a virtual 1024-bit SIMD register, larger than any current instruction set, but the design deliberately maps each wide operation onto a series of narrower operations that existing 128-, 256- or 512-bit registers can execute. This means the same algorithm runs efficiently on current CPUs without requiring new hardware, as long as the software splits the wide operation into the appropriate number of narrower steps. However, trying to run a narrow-only algorithm on a wide register would re-introduce lane dependencies and hurt performance.

To achieve the needed alignment, UTL visualizes the data as a 16-row by 64-column matrix for 64-bit values, then transposes it so each column becomes a contiguous block that fits exactly into a 1024-bit register. Processing proceeds column-wise, allowing a single SIMD load to fetch all 16 values for a lane and decode them in parallel. This transposition step is the core of the layout and replaces the naïve row-wise storage that would scatter the needed values across memory.

The design also addresses the challenge of varying element sizes. Instead of creating separate transpositions for 64-, 32-, 16- and 8-bit values, UTL defines a universal permutation that keeps the data in a consistent order regardless of width. This width-agnostic approach prevents the fragmentation of query pipelines that would otherwise have to handle different memory orders for each type, simplifying both filtering and result stitching.

Engineers adopting UTL must modify their data ingestion and storage layers to emit the transposed matrix and add runtime code that performs the transposition before SIMD decoding. The cost includes extra CPU cycles for the transpose and potentially additional temporary buffers for base values. The approach ceases to provide benefits on platforms that cannot efficiently emulate the 1024-bit register or when the overhead of transposition outweighs the parallel decoding gains, such as for very small data sets.