Distributed Supercomputing Teams for Affordable Self-Healing HPC
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Solution Overview
Problem
Current supercomputers and business servers are expensive, inflexible, and lack affordability, making high-performance computing (HPC) inaccessible to many due to high acquisition and operational costs, and they fail to address critical needs such as survivability, security, and energy efficiency.
Innovation Solution
A self-healing, adaptive, and distributed supercomputing system designed for affordability, trustworthiness, and fault tolerance, utilizing commodity components, renewable energy, and advanced security features to create a scalable, low-maintenance, and highly secure infrastructure for processing and protecting information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If supercomputers are designed to provide high-performance computing power, then processing capability is improved, but acquisition cost and operational expense increase significantly
Solution Approach 1:
The patent divides the supercomputing system into multiple independent nodes that can be distributed across different locations. Each node operates autonomously but contributes to the overall computing power through networked coordination. This segmentation allows businesses to access HPC capabilities through smaller, more affordable investments rather than requiring a single massive supercomputer facility.
Solution Approach 2:
The patent creates a multi-functional platform that serves both traditional supercomputing workloads and general business computing needs. The system can dynamically allocate resources between different types of computations, making the infrastructure useful for a broader range of applications and justifying the investment through higher utilization rates.
2Productivity
If supercomputers are designed to provide high-performance computing power, then processing capability is improved, but operational expense increases significantly
Solution Approach 1:
By segmenting the computing workload across multiple distributed nodes, the system can optimize energy consumption at each location based on local conditions. Nodes can be powered down or throttled when not needed, and the system can leverage idle computing resources during off-peak hours, reducing overall operational expenses.
Solution Approach 2:
The system incorporates automated resource management and load balancing that dynamically adjusts computing resource allocation based on demand. This self-service capability ensures that computational power is available when needed while minimizing energy consumption during low-utilization periods, reducing operational expenses without sacrificing processing capability.
3Productivity
If datacenters are designed to store large amounts of data and run continuous operations, then computing power is improved, but power consumption increases
Solution Approach 1:
The patent implements periodic data collection and processing cycles that can be scheduled during off-peak electricity hours. Non-critical computations are deferred to periods when power consumption is lower, while critical real-time operations maintain their timing requirements. This periodic approach allows the system to maintain computing power while reducing overall power consumption costs.
4Productivity
If traditional supercomputers are designed for specialized compute-bound problems, then processing capability is improved, but adaptability to general-purpose computing decreases
Solution Approach 1:
The patent designs a universal computing platform that can handle both specialized scientific computations and general business applications. The system uses virtualization and containerization technologies to dynamically configure computing environments, allowing the same infrastructure to serve diverse workloads from weather modeling to enterprise data processing, thereby improving both processing capability and adaptability.
Data Source
AI summary
An affordable, highly trustworthy, survivable and available, operationally efficient distributed supercomputing infrastructure for processing, sharing and protecting both structured and unstructured information. A primary objective of the SHADOWS infrastructure is to establish a highly survivable, essentially maintenance-free shared platform for extremely high-performance computing (i.e., supercomputing)—with “high performance” defined both in terms of total throughput, but also in terms of very low-latency (although not every problem or customer necessarily requires very low latency)—while achieving unprecedented levels of affordability at its simplest, the idea is to use distributed “teams” of nodes in a self-healing network as the basis for managing and coordinating both the work to be accomplished and the resources available to do the work. The SHADOWS concept of “teams” is responsible for its ability to “self-heal” and “adapt” its distributed resources in an “organic” manner. Furthermore, the “teams” themselves are at the heart of decision-making, processing, and storage in the SHADOWS infrastructure. Everything that's important is handled under the auspices and stewardship of a team.