Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained

The deal.II Library, Version 8.5

This paper provides an overview of the new features of the finite element library deal.II version 8.5

CRAFT: A library for easier application-level Checkpoint/Restart and Automatic Fault Tolerance

In order to efficiently use the future generations of supercomputers, fault tolerance and power consumption are two of the prime challenges anticipated by the High Performance Computing (HPC) community. Checkpoint/Restart (CR) has been and still is the most widely used technique to deal with hard failures. Application-level CR is the most effective CR technique in terms of overhead efficiency but it takes a lot of implementation effort. This work presents the implementation of our C++ based library CRAFT (Checkpoint-Restart and Automatic Fault Tolerance), which serves two purposes. First, it provides an extendable library that significantly eases the implementation of application-level checkpointing. The most basic and frequently used checkpoint data types are already part of CRAFT and can be directly used out of the box. The library can be easily extended to add more data types. As means of overhead reduction, the library offers a build-in asynchronous checkpointing mechanism and also supports the Scalable Checkpoint/Restart (SCR) library for node level checkpointing. Second, CRAFT provides an easier interface for User-Level Failure Mitigation (ULFM) based dynamic process recovery, which significantly reduces the complexity and effort of failure detection and communication recovery mechanism. By utilizing both functionalities together, applications can write application-level checkpoints and recover dynamically from process failures with very limited programming effort. This work presents the design and use of our library in detail. The associated overheads are thoroughly analyzed using several benchmarks

GGDML: icosahedral models language extensions

The optimization opportunities of a code base are not completely exploited by compilers. In fact, there are optimizations that must be done within the source code. Hence, if the code developers skip some details, some performance is lost. Thus, the use of a general-purpose language to develop a performance-demanding software -e.g. climate models- needs more care from the developers. They should take into account hardware details of the target machine. Besides, writing a high-performance code for one machine will have a lower performance on another one. The developers usually write multiple optimized sections or even code versions for the different target machines. Such codes are complex and hard to maintain. In this article we introduce a higher-level code development approach, where we develop a set of extensions to the language that is used to write a model’s code. Our extensions form a domain-specific language (DSL) that abstracts domain concepts and leaves the lower level details to a configurable source-to-source translation process. The purpose of the developed extensions is to support the icosahedral climate/atmospheric model development. We have started with the three icosahedral models: DYNAMICO, ICON, and NICAM. The collaboration with the scientists from the weather/climate sciences enabled agreed-upon extensions. When we have suggested an extension we kept in mind that it represents a higher-level domain-based concept, and that it carries no lower-level details. The introduced DSL (GGDML- General Grid Definition and Manipulation Language) hides optimization details like memory layout. It reduces code size of a model to less than one third its original size in terms of lines of code. The development costs of a model with GGDML are therefore reduced significantly