Project Partners

Organizations collaborating in the H4H ITEA2 project to advance execution analysis and optimization of parallel applications.

Partner Organizations

Bull S.A.S.

French IT company serving as project coordinator. Project leaders Jean-Marc Morel and François Verbeck represent Bull in the H4H initiative.

Rogue Wave Software

Industrial partner contributing expertise in performance analysis and software development tools for parallel computing environments.

UVSQ

Université de Versailles Saint-Quentin-en-Yvelines, an academic partner bringing research capabilities in high-performance computing.

TU Dresden

Technische Universität Dresden, contributing through its Center for Information Services and High Performance Computing (ZIH).

JSC

Jülich Supercomputing Centre, a leading research institution providing supercomputing resources and expertise in parallel application tuning.

The H4H ITEA2 project brings together a consortium of industrial and academic partners united by a shared goal: advancing the analysis and optimization of parallel applications. Collaborative research efforts focus on developing robust tools that help developers understand how their software behaves across distributed and multi-core systems. By combining deep industry experience with cutting-edge academic research, the partnership aims to address real-world performance bottlenecks that limit scalability and efficiency. Each partner contributes specialized expertise, from high-performance computing infrastructure to software instrumentation techniques, ensuring that the resulting solutions are both practical and innovative. This synergy between commercial and research institutions forms the backbone of the project's approach to tackling complex parallel computing challenges.

A core technical objective of the H4H initiative is to create comprehensive execution analysis tools that give developers unprecedented visibility into application behavior. These tools are designed to capture detailed runtime data without imposing excessive overhead, allowing for accurate profiling of even the most demanding workloads. By integrating static and dynamic analysis methods, the project enables teams to identify inefficiencies such as load imbalance, communication delays, and memory access patterns that degrade performance. The resulting insights guide targeted optimizations, helping applications make better use of available hardware resources. Ultimately, this work supports the broader goal of making parallel programming more accessible and efficient for developers working across diverse computing environments, from clusters to embedded systems.

The H4H project builds upon established website mirroring and offline browsing technologies to facilitate seamless sharing of research outputs and documentation among distributed teams. Just as HTTrack enables users to capture and navigate web content locally, the project's collaboration infrastructure ensures that partners can access critical resources regardless of network availability. This approach supports efficient knowledge transfer across organizational boundaries, allowing researchers and engineers to work with synchronized datasets and tool repositories. By applying the principles of local accessibility and recursive structure familiar from website copying utilities, the project fosters a collaborative environment where technical findings, performance benchmarks, and software builds remain available for ongoing analysis and refinement throughout the development lifecycle.