Sparse Matrix Processing with Parallel Sparse-to-Dense Fetch
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
High-performance computing systems face inefficiencies when processing large matrices, as the central processing unit (CPU) spends significant time fetching sparse elements from distributed storage, leading to underutilization of computation bandwidth and increased latency due to the need to access disparate sparse elements of varying dimensions.
Innovation Solution
A system comprising specialized matrix processors that can independently fetch and transform sparse elements into a dense matrix, reducing the CPU's workload by distributing the data loading task and utilizing a sparse-dense transform unit to concatenate sparse elements from different data shards, thereby enhancing computation bandwidth and lowering latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the central processing unit fetches sparse elements from distributed storage, then the matrix can be loaded for processing, but the CPU computation bandwidth is underutilized and latency increases
Solution Approach 1:
The system segments the matrix processing task by dividing the sparse matrix into multiple shards distributed across different storage locations. Each shard is independently fetched and processed by dedicated matrix processors, allowing parallel execution and eliminating the CPU as a bottleneck. This segmentation enables simultaneous data fetching and computation across multiple processing units.
Solution Approach 2:
The patent introduces matrix processors as intermediary components between the distributed storage system and the CPU. These specialized processors handle sparse element fetching, transformation, and concatenation operations, freeing the CPU to focus on computation. The matrix processors act as mediators that manage the data movement and preprocessing tasks.
2Productivity
If the CPU handles sparse data fetching and transformation, then data can be processed, but processing costs increase and computation bandwidth is wasted
Solution Approach 1:
The system implements self-service by enabling matrix processors to autonomously fetch sparse elements from distributed storage and transform them into dense matrix format without CPU intervention. Each matrix processor independently manages its own data loading and preprocessing tasks, including identifying sparse elements, fetching them from storage, and concatenating results. This self-service capability eliminates the energy-wasting overhead of CPU-managed sparse data handling.
3Speed
If multiple processors fetch sparse elements in parallel, then processing speed increases, but coordination and concatenation complexity increases
Solution Approach 1:
The patent resolves coordination complexity by transforming the problem from a centralized coordination model to a distributed autonomous model. Instead of coordinating multiple processors through a central controller, each matrix processor independently operates in its own processing dimension, fetching and transforming data without requiring inter-processor coordination. The system architecture naturally handles concatenation through the distributed nature of the processing, eliminating the need for complex coordination protocols.
Data Source
AI summary
Methods, systems, and apparatus, including a system for transforming sparse elements into a dense matrix. The system includes a data fetch unit that includes a plurality of processors, the data fetch unit configured to determine, based on identifications of the subset of the particular sparse elements, a processor designation for fetching the subset of the particular sparse elements. The system includes a concatenation unit configured to generate an output dense matrix based on a transformation that is applied to the sparse elements fetched by the data fetch unit.