Davide

Recent trends in the HPC field have introduced new CPU architectures with improved vectorization capabilities that require optimization to achieve peak performance and thus pose challenges for performance portability. The deployment of high-performing scientific applications for CPUs requires adapting the codebase and optimizing for performance. Evaluating these applications provides insights into the complex interactions between code, compilers, and hardware. We evaluate compiler auto-vectorization and explicit vectorization to achieve performance portability across modern CPUs with long vectors. We select a molecular docking application as a case study, as it represents computational patterns commonly found across HPC workloads. We report insights into the technical challenges, architectural trends, and optimization strategies relevant to the future development of scientific applications for HPC. Our results show which code transformations enable portable auto-vectorization, reaching performance similar to explicit vectorization. Experimental data confirms that x86 CPUs typically achieve higher execution performance than ARM CPUs, primarily due to their wider vectorization units. However, ARM architectures demonstrate competitive energy consumption and cost-effectiveness.

The social and economic impact of the COVID-19 pandemic demands a reduction of the time required to find a therapeutic cure. In this paper, we describe the EXSCALATE molecular docking platform capable to scale on an entire modern supercomputer for supporting extreme-scale virtual screening campaigns. Such virtual experiments can provide in short time information on which molecules to consider in the next stages of the drug discovery pipeline, and it is a key asset in case of a pandemic. The EXSCALATE platform has been designed to benefit from heterogeneous computation nodes and to reduce scaling issues. In particular, we maximized the accelerators’ usage, minimized the communications between nodes, and aggregated the I/O requests to serve them more efficiently. Moreover, we balanced the computation across the nodes by designing an ad-hoc workflow based on the execution time prediction of each molecule. We deployed the platform on two HPC supercomputers, with a combined computational power of 81 PFLOPS, to evaluate the interaction between 70 billion of small molecules and 15 binding-sites of 12 viral proteins of SARS-CoV-2. The experiment lasted 60 hours and it performed more than one trillion ligand-pocket evaluations, setting a new record on the virtual screening scale.

In the autonomic computing context, the system is perceived as a set of autonomous elements capable of self-management, where end-users define high-level goals and the system shall adapt to achieve the desired behaviour. Runtime adaptation creates several optimization opportunities, especially if we consider approximate computing applications, where it is possible to trade off the accuracy of the result and the performance. Given that modern systems are limited by the power dissipated, autonomic computing is an appealing approach to increase the computation efficiency. In this paper, we introduce mARGOt, a dynamic autotuning framework to enhance the target application with an adaptation layer to provide self-optimization capabilities. The framework is implemented as a C++ library that works at function-level and provides to the application a mechanism to adapt in a reactive and a proactive way. Moreover, the application is capable to change dynamically its requirements and to learn online the underlying application-knowledge. We evaluated the proposed framework in three real-life scenarios, ranging from embedded to HPC applications. In the three use cases, experimental results demonstrate how, thanks to mARGOt, it is possible to increase the computation efficiency by adapting the application at runtime with a limited overhead.