Matrix-Based Parallel Execution of Spring Batch Jobs for DNA Pattern Discovery

# Matrix-Based Parallel Execution of Spring Batch Jobs for DNA Pattern Discovery **Technology Readiness Level (TRL): 5** **Framework:** Dual Engine IA Framework 3.0 (ESA–NASA–EIIS Edition) **DOI (framework reference):** [https://doi.org/10.5281/zenodo.17823427](https://doi.org/10.5281/zenodo.17823427) --- ## 1. Overview This repository documents a research and experimental system for **parallel DNA pattern discovery** using a **matrix-based execution of independent Spring Batch jobs**. The approach targets large-scale genome scans by exploiting *process-level parallelism* through multiple isolated JVM executions rather than in-JVM multithreading or tightly coupled grid partition handlers. The system is validated at **TRL 5**, meaning it has been **successfully tested in a relevant execution environment** with realistic data and measurable performance. --- ## 2. Scientific Objective The objective is to accelerate whole-genome (or large dataset) pattern discovery by: * Partitioning DNA data into independent segments * Executing one Spring Batch instance per partition * Running many JVM processes in parallel on PCs or servers * Merging results deterministically This model is well-suited for *embarrassingly parallel* problems such as motif search, signature scanning, and large-scale data inspection. --- ## 3. Architectural Principle ### Core Concepts * **One partition = one Spring Batch job** * **One job = one JVM process** * No shared memory between jobs * External orchestration via OS-level scripts ### Rationale This design avoids known concurrency and state-coherence issues encountered in certain distributed Spring Batch partition handlers by: * Using strict process isolation * Relying on deterministic parameters * Removing inter-job synchronization dependencies --- ## 4. Execution Environment (TRL 5) The system has been validated in a *relevant environment* as defined by ESA/NASA TRL standards: * Real PC or server hardware * Multi-core CPU * Limited but realistic RAM * Local or network storage * Realistic DNA datasets No industrial orchestrator (Kubernetes, Hadoop, Slurm) is required at this maturity level. --- ## 5. Execution Model ### Step 1 – Partitioning A genome file or database table is partitioned deterministically in advance. Each partition is assigned a unique identifier and parameter set. ### Step 2 – Parallel Launch Multiple JVM processes are launched in parallel, each executing: * The same Spring Batch application * Different partition parameters This can be achieved using: * Windows batch scripts (`start` command) * GNU Parallel on Linux ### Step 3 – Result Collection Each job produces an isolated output file or record set. ### Step 4 – Deterministic Merge Results are merged after completion into a single consolidated output. --- ## 6. Verification & Validation Summary ### Verification * Reproducible build (Maven) * Unique parameters per job * No shared mutable state * Independent job logs ### Validation * Correct detection of known DNA patterns * Identical results across repeated runs * Independence from execution order ### Robustness Test * Forced termination of a running JVM * Successful relaunch of the affected partition * No corruption or duplication of results --- ## 7. Performance Indicators (Example) Measured indicators typically include: * Total execution time * Speedup vs sequential execution * CPU utilization * RAM usage per JVM * IO wait behavior These measurements confirm scalability until hardware limits (IO or memory) are reached. --- ## 8. TRL 5 Declaration This system is declared compliant with **Technology Readiness Level 5**: > *Technology validated in a relevant environment with integrated components and realistic data.* The validation follows the **Dual Engine IA Framework 3.0**, ensuring traceability, robustness, and scientific reproducibility. --- ## 9. Limitations * Orchestration remains script-based * No dynamic resource scheduler * Result merging performed outside Spring Batch These limitations are acceptable and expected at TRL 5. --- ## 10. Reuse and Applicability The approach is applicable to: * Bioinformatics and genomics * Signature or pattern search * Cybersecurity scanning * Large-scale data inspection Any workload that can be cleanly partitioned without inter-task dependencies can benefit from this model. --- ## 11. Author **Wadï Mami** GitHub: [https://github.com/didipostman](https://github.com/didipostman) Email: [[e-mail]](mailto:[e-mail]) / [[e-mail]](mailto:[e-mail]) --- ## 12. License This work is released for **scientifi c and research use**. Please cite the framework DOI when reusing or extending this work: [https://doi.org/10.5281/zenodo.17823427](https://doi.org/10.5281/zenodo.17823427)