COURSE OBJECTIVES

• Introduce fundamental principles and good practices of HPC computation on distributed architecture.
• Understand the inherent stakes and difficulties involved in the design and integration of a decision support software through practical examples.
• Understand technologies used as well as their vocabulary.

TARGET AUDIENCE

• Architects, project managers, or technical experts aiming to grasp the specificities of quantitative computational function-based solutions (optimization, simulation, statistical learning).
• Decision support engineers aiming to expand their skills in computer science and software integration.
• Any developers involved in working on complex IT development projects.

Software Design
• Software quality factors.
• Code maintenance and maintainability.
• Unit tests and Test-Driven Development method.

Design Patterns
• General introduction.
• Gang of Four Design patterns in-depth study.
• Anti-Patterns.

Data structure options
• Introduction to computational complexity.
• Data collection and linkage structures.

Introduction to development tools
• Version control system, code review (examples: Git, Gerrit, Sonar).
• Continuous integration platform (Jenkins).
• Software integrator (Maven).
• Project manager (Redmine).
• Be able to use your IDE.
• Additional tools (Meld, unix/grep, etc.).

Designing an integrated decision support solution
• Fundamentals and issues.
• Technology options.
• Notion of highly cohesive low coupling.

Foundations and technologies of software architecture
• Architecture (client-serveur, SOA, etc.), SaaS solutions, remote computing.
• Data exchange methods and dedicated tools.
• Technical base of solutions, application containers, hosting.
• Interfacing a calculation engine in synchronous, asynchronous or hybrid mode.
• Achieve a downgraded mode.

Approach to conduct these projects
• V-cycle, agility, spiral or iterative development.
• Standard solutions, examples of architectures, and critical analyses.