A Domain-Specific Language and Editor for Parallel Particle Methods

Domain-specific languages (DSLs) are of increasing importance in scientific high-performance computing to reduce development costs, raise the level of abstraction and, thus, ease scientific programming. However, designing and implementing DSLs is not an easy task, as it requires knowledge of the application domain and experience in language engineering and compilers. Consequently, many DSLs follow a weak approach using macros or text generators, which lack many of the features that make a DSL a comfortable for programmers. Some of these features---e.g., syntax highlighting, type inference, error reporting, and code completion---are easily provided by language workbenches, which combine language engineering techniques and tools in a common ecosystem. In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL and development environment for numerical simulations based on particle methods and hybrid particle-mesh methods. PPME uses the meta programming system (MPS), a projectional language workbench. PPME is the successor of the Parallel Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional implementation strategies. We analyze and compare both languages and demonstrate how the programmer's experience can be improved using static analyses and projectional editing. Furthermore, we present an explicit domain model for particle abstractions and the first formal type system for particle methods.Comment: Submitted to ACM Transactions on Mathematical Software on Dec. 25, 201

Trends in Programming Languages for Neuroscience Simulations

Neuroscience simulators allow scientists to express models in terms of biological concepts, without having to concern themselves with low-level computational details of their implementation. The expressiveness, power and ease-of-use of the simulator interface is critical in efficiently and accurately translating ideas into a working simulation. We review long-term trends in the development of programmable simulator interfaces, and examine the benefits of moving from proprietary, domain-specific languages to modern dynamic general-purpose languages, in particular Python, which provide neuroscientists with an interactive and expressive simulation development environment and easy access to state-of-the-art general-purpose tools for scientific computing

Hierarchical Combination of Internal and External Domain-Specific Languages for Scientific Computing

To adapt established methods of software engineering for scientific computing, we propose a software development approach for interdisciplinary teams of scientists called Sprat. The approach is organized around a hierarchical architecture that combines internal and external domain-specific languages (DSLs). For its evaluation, Sprat is employed in the implementation of a marine ecosystem model. We highlight what is to be observed while integrating the DSLs into the hierarchy in order to enable a successful cooperation of scientists in interdisciplinary teams as well as to achieve a maintainable code base

Improving Scalability and Maintenance of Software for High-Performance Scientific Computing by Combining MDE and Frameworks

International audienceIn recent years, numerical simulation has attracted increasing interest within industry and among academics. Paradoxically, the development and maintenance of high performance scientific computing software has become more complex due to the diversification of hardware architectures and their related programming languages and libraries. In this paper, we share our experience in using model-driven development for numerical simulation software. Our approach called MDE4HPC proposes to tackle development complexity by using a domain specific modeling language to describe abstract views of the software. We present and analyse the results obtained with its implementation when deriving this abstract model to target Arcane, a development framework for 2D and 3D numerical simulation software

Parallelizing Julia with a Non-Invasive DSL

Computational scientists often prototype software using productivity languages that offer high-level programming abstractions. When higher performance is needed, they are obliged to rewrite their code in a lower-level efficiency language. Different solutions have been proposed to address this trade-off between productivity and efficiency. One promising approach is to create embedded domain-specific languages that sacrifice generality for productivity and performance, but practical experience with DSLs points to some road blocks preventing widespread adoption. This paper proposes a non-invasive domain-specific language that makes as few visible changes to the host programming model as possible. We present ParallelAccelerator, a library and compiler for high-level, high-performance scientific computing in Julia. ParallelAccelerator\u27s programming model is aligned with existing Julia programming idioms. Our compiler exposes the implicit parallelism in high-level array-style programs and compiles them to fast, parallel native code. Programs can also run in "library-only" mode, letting users benefit from the full Julia environment and libraries. Our results show encouraging performance improvements with very few changes to source code required. In particular, few to no additional type annotations are necessary

A Framework for Model-Driven Scientific Workflow Engineering

So-called scientific workflows are one important means in the context of data-intensive science for reliable and efficient scientific data processing in distributed computing infrastructures such as Grids. Scientific Workflow Management Systems (SWfMS) help scientists model and run scientific workflows, whereas a domain-specific layer for workflow modeling by a scientist and a technical layer for automated workflow execution can be distinguished. Initially, many SWfMS were developed from scratch using custom workflow technologies languages without application of already existing and established business workflow technologies. Among the reasons were different life cycles for scientific and business workflows as well as incompatible interfaces and communication protocols of the respective execution infrastructures. Meanwhile, several business IT infrastructures have evolved to serviceoriented architectures (SOAs), for which many Web service standards and technologies have been developed. The Web Services Business Process Execution Language (BPEL), for example, is a well-accepted standard for the implementation and execution of business workflows in SOAs. The SOA architecture pattern has been adopted in scientific IT infrastructures by so-called Service Grids based on existing standards and technologies. Due to this development, BPEL is also suitable for the execution of scientific workflows at the technical layer, which has been elaborated on in many publications and projects. However, BPEL is a workflow language for IT experts and is originally not suited for scientific workflow modeling by a scientist at the domain-specific layer. A domain-specific abstraction of BPEL is therefore required that can be specifically tailored for scientific workflow modeling as well as a corresponding mapping to the technical layer. These challenges of the domain-specific abstraction and the mapping are addressed in this thesis with the help of the Business Process Model and Notation (BPMN) standard and technologies from Model-Driven Software Development (MDSD). Therefore, the MoDFlow approach for Model-Driven Scientific WorkFlow Engineering is presented to map domain-specific scientific workflow models via a BPMN-based intermediate layer to an executable workflow model. The intermediate layer is specified by MoDFlow.BPMN, which is a BPMN metamodel subset with custom extensions for the scientific domain. MoDFlow.BPMN2BPEL defines three consecutive transformation steps to map MoDFlow.BPMN to BPEL for workflow execution. Furthermore, different methods to utilize and extend MoDFlow.BPMN and MoDFlow.BPMN2BPEL are described in the MoDFlow approach, in which the definition of so-called domain-specific languages (DSLs) for the modeling of scientific workflows at the domain-specific layer is focused. The MoDFlow framework is an implementation of the MoDFlow approach, which is based on the Eclipse Modeling Framework (EMF). The MoDFlow framework is evaluated in three application scenarios, in which different utilization and extension mechanisms are examined. The first two application scenarios investigate the technical feasibility of the approach and support scientific workflows with parameter sweeps that are executed on a Grid infrastructure. The third application scenario has been conducted in collaboration with the PubFlow project, which aims to create an infrastructure to model and execute data publication workflows. Based on the Xtext framework, a textual DSL and a corresponding language infrastructure is defined for this purpose that supports developers in creating data publication workflows. This scenario aims to illustrate the practicability of the MoDFlow framework. PubFlow currently plans to implement an additional graphical DSL based on the BPMN notation and a corresponding workflow editor for scientists

A Language and Hardware Independent Approach to Quantum-Classical Computing

Heterogeneous high-performance computing (HPC) systems offer novel architectures which accelerate specific workloads through judicious use of specialized coprocessors. A promising architectural approach for future scientific computations is provided by heterogeneous HPC systems integrating quantum processing units (QPUs). To this end, we present XACC (eXtreme-scale ACCelerator) --- a programming model and software framework that enables quantum acceleration within standard or HPC software workflows. XACC follows a coprocessor machine model that is independent of the underlying quantum computing hardware, thereby enabling quantum programs to be defined and executed on a variety of QPUs types through a unified application programming interface. Moreover, XACC defines a polymorphic low-level intermediate representation, and an extensible compiler frontend that enables language independent quantum programming, thus promoting integration and interoperability across the quantum programming landscape. In this work we define the software architecture enabling our hardware and language independent approach, and demonstrate its usefulness across a range of quantum computing models through illustrative examples involving the compilation and execution of gate and annealing-based quantum programs

Knowledge-based Expressive Technologies within Cloud Computing Environments

Presented paper describes the development of comprehensive approach for knowledge processing within e-Sceince tasks. Considering the task solving within a simulation-driven approach a set of knowledge-based procedures for task definition and composite application processing can be identified. This procedures could be supported by the use of domain-specific knowledge being formalized and used for automation purpose. Within this work the developed conceptual and technological knowledge-based toolbox for complex multidisciplinary task solv-ing support is proposed. Using CLAVIRE cloud computing environment as a core platform a set of interconnected expressive technologies were developed.Comment: Proceedings of the 8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE2013). 201