The primary goal of the EuroHPC JU project SCALABLE is to develop an industrial Lattice Boltzmann Method (LBM)-based computational fluid dynamics (CFD) solver capable of exploiting current and future extreme scale architectures, expanding current capabilities of existing industrial LBM solvers by at least two orders of magnitude in terms of processor cores and lattice cells, while preserving its accessibility from both the end-user and software developer's point of view. This is accomplished by transferring technology and knowledge between an academic code (waLBerla) and an industrial code (LaBS). This paper briefly introduces the characteristics and main features of both software packages involved in the process. We also highlight some of the performance achievements in scales of up to tens of thousand of cores presented on one academic and one industrial benchmark case.

Summary Profiling and tuning of parallel applications is an essential part of HPC. Analysis and elimination of application hot spots can be performed using many available tools, which also provides resource consumption measurements for instrumented parts of the code. Since complex applications show different behavior in each part of the code, it is essential to be able to insert instrumentation to analyse these parts. Because each performance analysis or autotuning tool can bring different insights into an application behavior, it is valuable to analyze and optimize an application using a variety of them. We present our on request inserted shared C/C++ API for the most common open‐source HPC performance analysis tools, which simplify the process of the manual instrumentation. Besides manual instrumentation, profiling libraries provide different methods for instrumentation. Of these, the binary patching is the most universal mechanism, and highly improves the user‐friendliness and robustness of the tool. We provide an overview of the most commonly used binary patching tools, and describe a workflow for how to use them to implement a binary instrumentation tool for any profiler or autotuner. We have also evaluated the minimum overhead of the manual and binary instrumentation.

Efficiently utilizing procured power and optimizing performance of scientific applications under power and energy constraints are challenging. The HPC PowerStack defines a software stack to manage power and energy of high-performance computing systems and standardizes the interfaces between different components of the stack. This survey paper presents the findings of a working group focused on the end-to-end tuning of the PowerStack. First, we provide a background on the PowerStack layer-specific tuning efforts in terms of their high-level objectives, the constraints and optimization goals, layer-specific telemetry, and control parameters, and we list the existing software solutions that address those challenges. Second, we propose the PowerStack end-to-end auto-tuning framework, identify the opportunities in co-tuning different layers in the PowerStack, and present specific use cases and solutions. Third, we discuss the research opportunities and challenges for collective auto-tuning of two or more management layers (or domains) in the PowerStack. This paper takes the first steps in identifying and aggregating the important R&D challenges in streamlining the optimization efforts across the layers of the PowerStack.

Summary To overcome the challenges of energy consumption of HPC systems, the European Union Horizon 2020 READEX (Runtime Exploitation of Application Dynamism for Energy‐efficient Exascale computing) project uses an online auto‐tuning approach to improve energy efficiency of HPC applications. The READEX methodology pre‐computes optimal system configurations at design‐time, such as the CPU frequency, for instances of program regions and switches at runtime to the configuration given in the tuning model when the region is executed. READEX goes beyond previous approaches by exploiting dynamic changes of a region's characteristics by leveraging region and characteristic specific system configurations. While the tool suite supports an automatic approach, specifying domain knowledge such as the structure and characteristics of the application and application tuning parameters can significantly help to create a more refined tuning model. This paper presents the means available for an application expert to provide domain knowledge and presents tuning results for some benchmarks.