Speeding up Scientific Codes in HPC Architectures by Code Modernization: Lessons Learned

The computational resources required in scientific research for key areas, such as medicine, physics, bioinformatics or climate modelling, are continuously increasing every year. To meet this demand, high-performance computing (HPC) systems keep growing in scale and complexity. However, the race for higher processor frequency is (temporally) over. The most profitable way to use the available silicon resources is offered by new-generation parallel multi/many core architectures, such as NVIDIA GPUs, or Intel Xeon Scalable or Intel Xeon Phi. These processors put forward new opportunities to enhance scientific computations, also increasing the performance per watt, but shifting to a different programming model to exploit the parallelism (task, data and thread-level). However, both developers and users are usually reluctant to modify their working codes to adapt to new systems. While the benefits of migrating codes to new systems are clear, it is important to evaluate how to do so in a simple and general way. ‘‘Code modernization’’ is a new paradigm that aims to provide both code and performance portability. In this talk, we identify the key issues that determine performance in modernized code, such as: (a) the ability to scale as with core count, (b) ensure a proper usage of the vectorization capabilities of the system and (c) the exploitation of data locality. We show three use cases from key applications used in computational science: 1) 3-D Stencil-based codes as they are the basis for solving partial differential equations (PDEs), which are widely used as a mathematical model in many applications from a wide variety of fields of science and engineering, 2) A population based metaheuristic for solving NP-hard optimization problems, such as the Traveling Salesman Problem, called Ant Colony Optimization (ACO), which is a bio-inspired method, based on ant’s foraging process and, 3) A Semantic Web Integration Tool (SWIT) that transforms and integrates heterogeneous biomedical data for generating open semantic repositories defining mapping rules between an input schema and an OWL ontology. The talk will shed light on modernizing these use cases to new Intel architectures.    BIOGRAPHY OF JOSÉ M. GARCÍA José M. García is a full professor of Computer Architecture at the Department of Computer Engineering at the University of Murcia (Spain). He served as Director of the Computer Engineering Department from 1998 till 2004, and later, served as the Dean of the School of Computer Science for seven years (2006-2012). He has developed several courses on Computer Structure, Computer Architecture, Parallel Computer Architecture, Peripheral Devices, and Multicomputer Design. He was involved in the “EA-Grid: Euro-Asia United Establishment of Double Degree Master Programme in Grid Computing”, which was an Education and Research Network in Grid Computing between the EU and Asia funded by the European Commission. Prof. García is the Head of the Research Group on Parallel Computer Architecture. His current research interests lie in the design of power-efficient heterogeneous systems, the code development and modernization of data-intensive applications for those systems (especially bioinspired evolutionary algorithms, and bioinformatics apps for new drug discovery), and HPC architectures for Deep Learning algorithms. He has supervised seventeen doctoral Theses and has published more than 170 refereed papers in different journals and conferences in these fields. Prof. García is member of HiPEAC, the European Network of Excellence on High Performance and Embedded Architecture and Compilation, and also member of several international associations such as the IEEE and ACM.