GridBatch Parallel Processing Framework for Cluster Bandwidth Optimization
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Solution Overview
Problem
Current systems face challenges in efficiently processing large volumes of data due to the disparity between growing data storage capacity and stagnant computational performance of uniprocessor systems, leading to high computational time and network bandwidth limitations in parallel processing.
Innovation Solution
The GridBatch system provides a framework for parallelizing applications by using a library of operators to manage data partitioning and processing across multiple processors, minimizing network bandwidth requirements and hiding parallel programming complexities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If applications run sequentially on uniprocessor machines, then implementation complexity is low, but computational performance is insufficient for large data volumes
Solution Approach 1:
The patent introduces an intermediary layer (parallel processing framework) between the sequential application code and the parallel hardware execution. This framework automatically handles task decomposition, thread management, and coordination, allowing developers to write sequential code that executes in parallel without directly managing parallel programming complexity.
Solution Approach 2:
The patent segments the computational workload into independent tasks or threads that can execute concurrently on multiple processors. By dividing the large data processing workload into smaller, manageable units, the system achieves parallel execution while maintaining simplicity in the overall application structure.
2Productivity
If additional machines are introduced to increase computing power, then computational capacity increases, but network bandwidth requirements exceed available bandwidth
Solution Approach 1:
The patent implements local data processing where each processor or node works on locally stored data partitions rather than requiring continuous data transfer over the network. This localizes the computational work to where the data resides, minimizing network bandwidth requirements while maximizing computational capacity utilization.
3Speed
If data is processed across multiple CPUs in parallel, then computational speed increases, but coordination and synchronization complexity increases
Solution Approach 1:
The patent introduces a coordination framework that acts as an intermediary between multiple CPUs, automatically handling synchronization, task assignment, and result aggregation. This mediator abstracts the complexity of inter-processor coordination from the application logic, enabling fast parallel execution without manual synchronization management.
4Adaptability or versatility
If uniprocessor systems are used, then programming is simple, but the gap between storage capacity growth and processing speed widens
Solution Approach 1:
The patent implements a dynamic parallel processing architecture that can adaptively scale from single-processor to multi-processor configurations based on data volume and processing requirements. The system dynamically adjusts the degree of parallelization and task distribution, providing scalability to match data growth while maintaining programming simplicity through a unified interface.
Data Source
AI summary
GridBatch provides an infrastructure framework that hides the complexities and burdens of developing logic and programming application that implement detail parallelized computations from programmers. A programmer may use GridBatch to implement parallelized computational operations that minimize network bandwidth requirements, and efficiently partition and coordinate computational processing in a multiprocessor configuration. GridBatch provides an effective and lightweight approach to rapidly build parallelized applications using economically viable multiprocessor configurations that achieve the highest performance results.