SIMD Numerical Data Compression for Memory and Transfer Bottlenecks
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Solution Overview
Problem
High-performance computing systems face bottlenecks due to limited data transfer rates and memory resource challenges when handling vast amounts of numerical data, necessitating efficient compression methods that are computationally efficient to minimize resource demands.
Innovation Solution
The implementation of novel SIMD constructs and instructions for encoding and decoding numerical data, including determining a maximum exponent value, interleaving bits, selecting nibbles, and generating exponent tokens, to compress and decompress data efficiently using SIMD technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compression of numerical data is implemented to reduce data transfer demands and storage capacity, then data transfer efficiency and memory utilization are improved, but additional computations are required which increase computational overhead
Solution Approach 1:
The patent combines multiple compression operations (exponent determination, bit interleaving, nibble selection, packing) into a unified SIMD instruction pipeline that processes multiple data elements simultaneously. This merging of operations into parallel vector instructions reduces the total computational overhead compared to sequential scalar processing, thereby improving data transfer efficiency without proportionally increasing computational energy consumption
Solution Approach 2:
The patent changes the computational parameters by using SIMD vector operations instead of scalar operations, processing multiple numerical data elements in parallel. This parameter change from sequential to parallel processing improves compression throughput and data transfer efficiency while the computational overhead increases only linearly with data volume rather than super-linearly
2Speed
If SIMD instructions are used to accelerate compression and decompression operations, then processing speed is improved, but device complexity increases due to novel SIMD constructs and instructions
Solution Approach 1:
The patent segments the compression process into distinct SIMD operations: exponent determination, bit interleaving, nibble selection, and packing. Each segment is handled by a specialized SIMD instruction that operates on multiple data elements in parallel. This segmentation enables high processing speed through vectorization while managing device complexity by breaking down the overall complex task into manageable, optimized instruction segments
Solution Approach 2:
The patent creates universal SIMD instructions that can handle multiple compression operations (exponent extraction, bit manipulation, data packing) within a single instruction framework. These multi-functional SIMD instructions accelerate various compression stages simultaneously, improving overall processing speed while reducing the need for numerous separate instructions, thereby managing device complexity
3Productivity
If vast amounts of numerical data are transferred among processor cores and memory devices, then data processing capability is improved, but data transfer bottlenecks occur due to limited interface rates
Solution Approach 1:
The patent extracts the essential information from numerical data by compressing it to retain only the most significant bits and exponent information. This extraction of critical data elements reduces the volume of data that needs to be transferred between processor cores and memory devices, eliminating transfer bottlenecks while preserving data processing capability through efficient representation of numerical values
Data Source
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AI summary
Compression and decompression of numerical data utilizing single instruction, multiple data (SIMD) instructions is described. The numerical data includes integer and floating-point samples. Compression supports three encoding modes: lossless, fixed-rate, and fixed-quality. SIMD instructions for compression operations may include attenuation, derivative calculations, bit packing to form compressed packets, header generation for the packets, and packed array output operations. SIMD instructions for decompression may include packed array input operations, header recovery, decoder control, bit unpacking, integration, and amplification. Compression and decompression may be implemented in a microprocessor, digital signal processor, field-programmable gate array, application-specific integrated circuit, system-on-chip, or graphics processor, using SIMD instructions. Compression and decompression of numerical data can reduce memory, networking, and storage bottlenecks. This abstract does not limit the scope of the invention as described in the claims.