Recently funded by the European High-Performance Computing Joint Undertaking (EuroHPC JU), the highly ranked SparCity just launched. With three years of duration and a total budget of 2.6M€, this collaborative project between 6 partners in 4 countries aims to build a sustainable exascale ecosystem and increase Europe’s competitiveness. SparCity’s main goal is to create a supercomputing framework that will provide efficient algorithms and coherent tools specifically designed for maximizing the performance and energy efficiency of sparse computations on emerging High-Performance Computing systems, while also opening up new usage areas for sparse computations in data analytics and deep learning. To demonstrate the effectiveness, societal impact, and usability of the framework, the SparCity project will enhance the computing scale and energy efficiency of four challenging real-life applications that come from drastically different domains, namely, computational cardiology, social networks, bioinformatics, and autonomous driving. By targeting this collection of challenging applications, SparCity will develop world-class, extreme-scale, and energy-efficient HPC technologies. Sabancı University will take part in the development and implementation of high-performance data processing and machine-learning algorithms for SparCity.
SparCity involves partnerships with Sabanci Universitesi (Turkey), Simula Research Laboratory (Norway), INESC-ID (Portugal), Ludwig-Maximilians-Universitaet Muenchen (Germany) and Graphcore AS (Norway), with the coordination of Koç University (Turkey).
The SparCity project aims at creating a supercomputing framework that will provide efficient algorithms and coherent tools specifically designed for maximizing the performance and energy efficiency of sparse computations on emerging HPC systems, while also opening up new usage areas for sparse computations in data analytics and deep learning.
The objectives of the project`s are:
1. Develop a comprehensive application and data characterization mechanism and orchestrate an advanced and synergistic software optimization process, based on the state-of-the-art analytical and machine-learning-based performance and energy models;
2. Develop advanced node-level static and dynamic code optimizations designed for massive and heterogeneous parallel architectures with complex memory hierarchy and exploit mixed-precision opportunities for sparse computation;
3. Devise topology-aware partitioning algorithms and optimizations to minimize the communication overhead and boost the efficiency of system-level parallelism;
4. Create digital SuperTwins of supercomputers to evaluate and simulate what-if hardware scenarios and to gather real-time performance and energy intel from node- and system-level components for application optimization on the current and future hardware;
5. Demonstrate the effectiveness and usability of the SparCity framework by enhancing the computing scale and energy efficiency of four challenging real-life applications, namely, computational cardiology, social network analysis, bioinformatics and computer vision applications;
6. Deliver a robust, well-supported and documented SparCity framework into the hands of computational scientists, data analysts, and deep learning end-users from industry and academia.