Dynamic GPU Resource Allocation

FLOPS employs an advanced and adaptive framework to optimize GPU resource utilization within its decentralized compute marketplace. Below is a detailed explanation tailored for inclusion in the FLOPS whitepaper:

Token-Based Dynamic Reward Mechanism

FLOPS ensures fair and efficient allocation of incentives through a dynamic reward formula:

• Ri​: Reward for GPU node iii.

• Ci: Contribution value based on task execution (e.g., uptime, throughput).

• Wi​: Task weight depending on complexity or priority.

• N: Total active GPU nodes in the network.

This approach aligns rewards directly with contributions, minimizing inefficiencies caused by idle or underutilized resources.

Distributed Task Management

Tasks within the FLOPS ecosystem are broken down into smaller, manageable units:

T={t 1 ​ ,t 2 ​ ,…,t n ​ }

Each subtask is distributed across GPU nodes with conditions to:

1. Prioritize nodes with lower workload (Li≤Lavg)(L_i \leq L_{avg})(Li​≤Lavg​).

2. Maintain inter-task independence (Dk(ti,tj)=0)(D_k(t_i, t_j) = 0)(Dk​(ti​,tj​)=0) for parallel execution.

Real-Time Load Balancing

GPU workload is dynamically monitored to optimize performance:

• Li​: Current workload of GPU node iii.

• Ui​: Node utilization rate.

• Rmax​: Maximum computational capacity of the node.

Tasks are directed to nodes with workloads below a predefined threshold (Li≤θ)(L_i \leq \theta)(Li​≤θ), preventing overuse and resource bottlenecks.

Reputation-Based Node Scoring

FLOPS incorporates a reputation evaluation model for nodes:

• Si​: Reputation score of node iii.

• AiA_iAi​: Accuracy of task execution.

• RiR_iRi​: Responsiveness during operations.

• SprevS_{prev}Sprev​: Historical reputation.

• α,β,γ\alpha, \beta, \gammaα,β,γ: Weight coefficients reflecting scoring priorities.

Nodes with low scores receive fewer tasks or lower rewards, ensuring consistent network quality.