Loo.py: From Fortran to performance via transformation and substitution rules

A large amount of numerically-oriented code is written and is being written in legacy languages. Much of this code could, in principle, make good use of data-parallel throughput-oriented computer architectures. Loo.py, a transformation-based programming system targeted at GPUs and general data-parallel architectures, provides a mechanism for user-controlled transformation of array programs. This transformation capability is designed to not just apply to programs written specifically for Loo.py, but also those imported from other languages such as Fortran. It eases the trade-off between achieving high performance, portability, and programmability by allowing the user to apply a large and growing family of transformations to an input program. These transformations are expressed in and used from Python and may be applied from a variety of settings, including a pragma-like manner from other languages.Comment: ARRAY 2015 - 2nd ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming (ARRAY 2015

First steps towards the certification of an ARM simulator using Compcert

The simulation of Systems-on-Chip (SoC) is nowadays a hot topic because, beyond providing many debugging facilities, it allows the development of dedicated software before the hardware is available. Low-consumption CPUs such as ARM play a central role in SoC. However, the effectiveness of simulation depends on the faithfulness of the simulator. To this effect, we propose here to prove significant parts of such a simulator, SimSoC. Basically, on one hand, we develop a Coq formal model of the ARM architecture while on the other hand, we consider a version of the simulator including components written in Compcert-C. Then we prove that the simulation of ARM operations, according to Compcert-C formal semantics, conforms to the expected formal model of ARM. Size issues are partly dealt with using automatic generation of significant parts of the Coq model and of SimSoC from the official textual definition of ARM. However, this is still a long-term project. We report here the current stage of our efforts and discuss in particular the use of Compcert-C in this framework.Comment: First International Conference on Certified Programs and Proofs 7086 (2011

Loo.py: transformation-based code generation for GPUs and CPUs

Today's highly heterogeneous computing landscape places a burden on programmers wanting to achieve high performance on a reasonably broad cross-section of machines. To do so, computations need to be expressed in many different but mathematically equivalent ways, with, in the worst case, one variant per target machine. Loo.py, a programming system embedded in Python, meets this challenge by defining a data model for array-style computations and a library of transformations that operate on this model. Offering transformations such as loop tiling, vectorization, storage management, unrolling, instruction-level parallelism, change of data layout, and many more, it provides a convenient way to capture, parametrize, and re-unify the growth among code variants. Optional, deep integration with numpy and PyOpenCL provides a convenient computing environment where the transition from prototype to high-performance implementation can occur in a gradual, machine-assisted form

Compilation of Heterogeneous Models: Motivations and Challenges

International audienceThe widespread use of model driven engineering in the development of software-intensive systems, including high-integrity embedded systems, gave rise to a "Tower of Babel" of modeling languages. System architects may use languages such as OMG SysML and MARTE, SAE AADL or EAST-ADL; control and command engineers tend to use graphical tools such as MathWorks Simulink/Stateflow or Esterel Technologies SCADE, or textual languages such as MathWorks Embedded Matlab; software engineers usually rely on OMG UML; and, of course, many in-house domain specific languages are equally used at any step of the development process. This heterogeneity of modeling formalisms raises several questions on the verification and code generation for systems described using heterogeneous models: How can we ensure consistency across multiple modeling views? How can we generate code, which is optimized with respect to multiple modeling views? How can we ensure model-level verification is consistent with the run-time behavior of the generated executable application?In this position paper we describe the motivations and challenges of analysis and code generation from heterogeneous models when intra-view consistency, optimization and safety are major concerns. We will then introduce Project P 2 and Hi-MoCo 3-respectively FUI and Eurostars-funded collaborative projects tackling the challenges above. This work continues and extends, in a wider context, the work carried out by the Gene-Auto 4 project [1], [2]. Hereby we will present the key elements of Project P and Hi-MoCo, in particular: (i) the philosophy for the identification of safe and minimal practical subsets of input modeling languages; (ii) the overall architecture of the toolsets, the supported analysis techniques and the target languages for code generation; and finally, (iii) the approach to cross-domain qualification for an open-source, community-driven toolset

Toward model-based engineering for space embedded systems and software

International audienceEmbedded systems development suffers from difficulties to reach cost, delay and safety requirements. The continuous increase of system complexity requires a corresponding increase in the capability of design fault-free systems. Model-based engineering aims to make complexity management easier with the construction of a virtual representation of systems enabling early prediction of behaviour and performance. In this context, Space industry has specific needs to deal with remote systems that can not be maintained on ground. In such systems, fault management includes complex detection, localisation and recovery automatic procedures that can not be performed without confidence on safety. In this way, only simulation and formal proofs can support the validation of all the possible configurations. Thus, formal description of both functional and non-functional properties with temporal logic formulae is expected to analyse and to early predict system characteristics at execution. This paper is based on various studies and experiences that are carried out in space domain on the support provided by model-based engineering in terms of: • support to needs capture and requirements analysis, • support to design, • support to early verification and validation, • down to automatic generation of code