SIMD Instructions for Bit Unpacking and RLE Decompression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current processors are not efficiently capable of handling specific data formats, leading to performance bottlenecks in processing and storage, as they lack direct support for application-specific instructions, relying on increasing instruction execution speed rather than hardware enhancements.
Innovation Solution
The development of SIMD instructions such as fixed-width bit unpack, variable-length byte unpack, burst for RLE decompression, onesidx for finding bit set indices, gatherb for fetching indexed bits, and BitVecCmpRes for comparing value-vectors, which operate on SIMD registers to enhance data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If scalar processors are used to process specific data formats, then general processing capability is maintained, but processing efficiency and throughput are insufficient
Solution Approach 1:
The processor divides data processing into multiple parallel lanes, where each lane can independently process different data elements simultaneously. This segmentation allows the processor to handle multiple data formats through specialized lanes while maintaining overall processing efficiency.
Solution Approach 2:
The SIMD processor is designed with multi-functional capability to handle various data formats (fixed-width bit values, variable-length byte values, RLE compressed data) through a single instruction stream. This universality allows one processor to perform multiple specialized functions without requiring separate hardware for each data format.
2Speed
If processor instruction execution speed is increased to improve performance, then processing speed increases, but hardware complexity and cost increase
Solution Approach 1:
The patent merges multiple processing functions into a single SIMD instruction stream, allowing parallel execution of operations on multiple data elements simultaneously. This combining approach achieves high processing speeds without proportionally increasing hardware complexity, as the same execution units are utilized for multiple data elements in parallel.
Solution Approach 2:
The processor transitions from scalar (single-data-stream) processing to vector (multi-data-stream) processing, adding a dimension of parallelism. This dimensional change allows multiple data elements to be processed simultaneously through the same instruction pipeline, increasing throughput without linearly increasing hardware complexity.
3Productivity
If SIMD processors are used for parallel processing, then throughput per instruction increases, but difficulty in processing particular data formats arises
Solution Approach 1:
The SIMD processor implements local quality by providing specialized handling for different data formats within specific processing lanes or through format-specific instruction variants. This allows each lane or instruction type to be optimized for particular data formats (e.g., fixed-width bit unpacking, variable-length byte unpacking, RLE decompression) while maintaining overall parallel processing capability.
Data Source
AI summary
A method and apparatus for efficiently processing data in various formats in a single instruction multiple data (“SIMD”) architecture is presented. Specifically, a method to unpack a fixed-width bit values in a bit stream to a fixed width byte stream in a SIMD architecture is presented. A method to unpack variable-length byte packed values in a byte stream in a SIMD architecture is presented. A method to decompress a run length encoded compressed bit-vector in a SIMD architecture is presented. A method to return the offset of each bit set to one in a bit-vector in a SIMD architecture is presented. A method to fetch bits from a bit-vector at specified offsets relative to a base in a SIMD architecture is presented. A method to compare values stored in two SIMD registers is presented.