Task Distribution & Parallel Execution

Task Distribution and Parallel Execution

TasQ uses a parallelized compute model to break down large workloads into smaller, verifiable segments, each assigned to the most suitable compute node based on benchmarking scores, hardware capabilities, and availability.

Once a task is submitted through the TasQ dApp, the TasQ Ledger evaluates:

• Complexity of the workload

• Resource requirements (CPU, GPU, RAM, storage)

• Network latency for optimal execution speed

The ledger’s scheduling algorithm uses deterministic routing with adaptive heuristics, enabling real-time workload balancing and eliminating single-node bottlenecks.

Each sub-task runs in parallel across multiple nodes, with progress tracked and outputs validated using Merkle-proof-based integrity checks before final assembly.

Flow:

textCopyEdit[Task Submitted] → [Ledger Analysis] → [Sub-Task Assignment]  
→ [Parallel Execution] → [Proof Validation] → [Final Aggregation]

This architecture ensures scalability, faster processing times, and high fault tolerance, while maintaining security and proof-backed verifiability across all executions.