Computational fluid and particle dynamics (CFPD) simulations are of paramount importance for studying and improving drug effectiveness. Computational requirements of CFPD codes demand high-performance computing (HPC) resources. For these reasons, we introduce and evaluate in this article system software techniques for improving performance and tolerating load imbalance on a state-of-the-art production CFPD code. We demonstrate benefits of these techniques on Intel-, IBM- and Arm-based HPC technologies ranked in the Top500 supercomputers, showing the importance of using mechanisms applied at runtime to improve the performance independently of the underlying architecture. We run a real CFPD simulation of particle tracking on the human respiratory system, showing performance improvements of up to 2×, across different architectures, while applying runtime techniques and keeping constant the computational resources.
In this paper, we analyze the performance and energy consumption of an Arm-based high-performance computing (HPC) system developed within the European project Mont-Blanc. This system, called Dibona, has been integrated by ATOS/Bull, and it is powered by the latest Marvell's CPU, ThunderX. This CPU is the same one that powers the Astra supercomputer, the rst Arm-based supercomputer entering the Top in November. We study from microbenchmarks up to large production codes. We include an interdisciplinary evaluation of three scienti c applications (a nite-element uid dynamics code, a smoothed particle hydrodynamics code, and a lattice Boltzmann code) and the Graph benchmark, focusing on parallel and energy e ciency as well as studying their scalability up to thousands of Armv cores. For comparison, we run the same tests on state-of-the-art x nodes included in Dibona and the Tier-supercomputer MareNostrum. Our experiments show that the ThunderX has a lower performance on average, mainly due to its small vector unit yet somewhat compensated by its wider links between the CPU and the main memory. We found that the software ecosystem of the Armv architecture is comparable to the one available for Intel. Our results also show that ThunderX delivers similar or better energy-to-solution and scalability, proving that Arm-based chips are legitimate contenders in the market of next-generation HPC systems.
Computational fluid and particle dynamics simulations (CFPD) are of paramount importance for studying and improving drug effectiveness. Computational requirements of CFPD codes involves high-performance computing (HPC) resources. For these reasons we introduce and evaluate in this paper system software techniques for improving performance and tolerate load imbalance on a stateof-the-art production CFPD code. We demonstrate benefits of these techniques on both Intel-and Arm-based HPC clusters showing the importance of using mechanisms applied at runtime to improve the performance independently of the underlying architecture. We run a real CFPD simulation of particle tracking on the human respiratory system, showing performance improvements of up to 2×, keeping the computational resources constant. CCS CONCEPTS • Computing methodologies → Parallel programming languages; • Applied computing → Systems biology; • Computer systems organization → Multicore architectures; • Hardware → Emerging architectures;
Biological ontologies, such as the Human Phenotype Ontology (HPO) and the Gene Ontology (GO), are extensively used in biomedical research to investigate the complex relationship that exists between the phenome and the genome. The interpretation of the encoded information requires methods that efficiently interoperate between multiple ontologies providing molecular details of disease-related features. To this aim, we present GenOtype PHenotype ExplOrer (GOPHER), a framework to infer associations between HPO and GO terms harnessing machine learning and large-scale parallelism and scalability in High-Performance Computing. The method enables to map genotypic features to phenotypic features thus providing a valid tool for bridging functional and pathological annotations. GOPHER can improve the interpretation of molecular processes involved in pathological conditions, displaying a vast range of applications in biomedicine.
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