DOI: 10.5281/zenodo.18100216
title: Matrix-Based Parallel Execution of Spring Batch Jobs for DNA Pattern Discovery

description: # 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)

 

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## 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.

 

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## 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.

 

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## 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

 

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## 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.

 

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## 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.

 

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## 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

 

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## 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.

 

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## 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.

 

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## 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.

 

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## 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.

 

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## 11. Author

 

**Wadï Mami**

GitHub: [https://github.com/didipostman](https://github.com/didipostman)

Email: [wmami@steg.com.tn](mailto:wmami@steg.com.tn) / [didipostman77@gmail.com](mailto:didipostman77@gmail.com)

 

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## 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)
description: mvn clean package

java -jar dna-batch.jar --partition.id=001

# For matrix mode:

parallel "java -jar dna-batch.jar --partition.id={}" ::: $(seq -f "%03g" 1 200)
description: /src Spring Batch source code

/partitioning Scripts & config for parallel runs

/results Output examples

/measurements Execution logs, metrics

/documentation TRL evidences and architectural notes