Software portability

About: Software portability is a research topic. Over the lifetime, 8987 publications have been published within this topic receiving 164922 citations. The topic is also known as: portability.

Simit: A Language for Physical Simulation

Abstract: With existing programming tools, writing high-performance simulation code is labor intensive and requires sacrificing readability and portability The alternative is to prototype simulations in a high-level language like Matlab, thereby sacrificing performance The Matlab programming model naturally describes the behavior of an entire physical system using the language of linear algebra However, simulations also manipulate individual geometric elements, which are best represented using linked data structures like meshes Translating between the linked data structures and linear algebra comes at significant cost, both to the programmer and to the machine High-performance implementations avoid the cost by rephrasing the computation in terms of linked or index data structures, leaving the code complicated and monolithic, often increasing its size by an order of magnitudeIn this article, we present Simit, a new language for physical simulations that lets the programmer view the system both as a linked data structure in the form of a hypergraph and as a set of global vectors, matrices, and tensors depending on what is convenient at any given time Simit provides a novel assembly construct that makes it conceptually easy and computationally efficient to move between the two abstractions Using the information provided by the assembly construct, the compiler generates efficient in-place computation on the graph We demonstrate that Simit is easy to use: a Simit program is typically shorter than a Matlab program; that it is high performance: a Simit program running sequentially on a CPU performs comparably to hand-optimized simulations; and that it is portable: Simit programs can be compiled for GPUs with no change to the program, delivering 4 to 20× speedups over our optimized CPU code

A Pi-Calculus Based Approach to Software Composition

Abstract: Present-day applications are increasingly required to be flexible, or "open" in a variety of ways. By flexibility we mean that these applications have to be portable (to different hardware and software platforms), interoperable (with other applications), extendible (to new functionality), configurable (to individual users' or clients' needs), and maintainable. These kinds of flexibility are currently best supported by component-oriented software technology: components, by means of abstraction, support portability, interoperability, and maintainability. Extendibility and configurability are supported by different forms of binding technology, or "glue": application parts, or even whole applications can be created by composing software components; applications stay flexible by allowing components to be replaced or reconfigured, possibly at runtime. This thesis develops a formal language for software composition that is based on the Pi-calculus. More precisely, we present the L-calculus, a variant of the Pi-calculus in which agents communicate by passing extensible, labeled records, or so-called "forms", rather than tuples. This approach makes it much easier to model compositional abstractions than it is possible in the plain Pi-calculus, since the contents of communication are now independent of position, agents are more naturally polymorphic since communication forms can be easily extended, and environmental arguments can be passed implicitly. The L-calculus is developed in three stages: (i) we analyse whether the Pi-calculus is suitable to model composition abstractions, (ii) driven by the insights we got using the Pi-calculus, we de ne a new calculus that has better support for software composition (e.g., provides support for inherently extensible software construction), and (iii), we de ne a first-order type system with subtype polymorphism and sound record concatenation that allows us to check statically an agent system in order to prevent the occurrences of run-time errors. We conclude with defining a first Java-based composition system and Piccola, a prototype composition language based on the L-calculus. The composition system provides support for integrating arbitrary compositional abstractions using both Piccola and standard bridging technologies like RMI and CORBA. Furthermore, the composition systems maintains a composition library that provides components in a uniform way.

Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware system.

Abstract: Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated.

OpenACC to FPGA: A Framework for Directive-Based High-Performance Reconfigurable Computing

Abstract: This paper presents a directive-based, high-level programming framework for high-performance reconfigurable computing. It takes a standard, portable OpenACC C program as input and generates a hardware configuration file for execution on FPGAs. We implemented this prototype system using our open-source OpenARC compiler, it performs source-to-source translation and optimization of the input OpenACC program into an OpenCL code, which is further compiled into a FPGA program by the backend Altera Offline OpenCL compiler. Internally, the design of OpenARC uses a high-level intermediate representation that separates concerns of program representation from underlying architectures, which facilitates portability of OpenARC. In fact, this design allowed us to create the OpenACC-to-FPGA translation framework with minimal extensions to our existing system. In addition, we show that our proposed FPGA-specific compiler optimizations and novel OpenACC pragma extensions assist the compiler in generating more efficient FPGA hardware configuration files. Our empirical evaluation on an Altera Stratix V FPGA with eight OpenACC benchmarks demonstrate the benefits of our strategy. To demonstrate the portability of OpenARC, we show results for the same benchmarks executing on other heterogeneous platforms, including NVIDIA GPUs, AMD GPUs, and Intel Xeon Phis. This initial evidence helps support the goal of using a directive-based, high-level programming strategy for performance portability across heterogeneous HPC architectures.