Manycore Cross-Connect Architecture for Adaptive Application Load Sharing

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Solution Overview

Problem

Existing computing architectures face inefficiencies in balancing the execution speed of individual application programs with the utilization of computing resources, leading to sub-optimal resource utilization and compromised performance, particularly in high-performance computing and cloud computing environments, with significant overhead in input/output operations.

Innovation Solution

A multi-stage, application program load adaptive parallel data processing architecture that dynamically shares computing resources among software programs, utilizing a hardware-implemented data packet switching cross-connect and receive logic subsystem to enable efficient data connectivity and resource allocation across multiple processing cores without requiring software involvement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If computing resources are dynamically shared among multiple application programs, then resource utilization efficiency is improved, but application performance and security may be compromised

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidapplication performance and security

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The computing system is segmented into multiple isolated execution environments (VMs or containers) that can run different application programs simultaneously. Each segment has its own resource allocation and execution context, allowing secure isolation while enabling efficient resource sharing across the platform.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A hardware-based intermediary layer (such as a dedicated switching fabric or interconnect architecture) mediates between application programs and computing resources. This intermediary manages resource allocation and data transfer without requiring software involvement, ensuring deterministic performance and security while maximizing resource utilization.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If hardware-based resource allocation is implemented, then data processing throughput is improved, but device complexity increases

Engineering Contradiction:

Improvedata processing throughputVSAvoidhardware architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The hardware resource allocation mechanism operates autonomously without requiring software intervention. The system self-manages resource distribution based on application demands, using hardware-based scheduling and switching logic that automatically optimizes throughput while maintaining manageable complexity through dedicated hardware functions.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12493492B2Responding to application demand in a system that uses programmable logic components

Publication Date: 2025.12.09 THROUGHPUTER INC

AI summary

Systems and methods provide an extensible, multi-stage, realtime application program processing load adaptive, manycore data processing architecture shared dynamically among instances of parallelized and pipelined application software programs, according to processing load variations of said programs and their tasks and instances, as well as contractual policies. The invented techniques provide, at the same time, both application software development productivity, through presenting for software a simple, virtual static view of the actually dynamically allocated and assigned processing hardware resources, together with high program runtime performance, through scalable pipelined and parallelized program execution with minimized overhead, as well as high resource efficiency, through adaptively optimized processing resource allocation.