Scientific computing problems are frequently solved using data-graph computations -- algorithms that perform local updates on application-specific data associated with vertices of a graph, over many time steps. The data-graph in such computations is commonly a mesh graph, where vertices have positions in 3D space, and edges connect physically nearby vertices. A scheduler controls the parallel execution of the algorithm. Two classes of parallel schedulers exist: double-buffering and in-place. Double-buffering schedulers do not incur synchronization overheads due to an absence of read-write conflicts, but require two copies of the vertices, as well as a higher iteration count due to a slower convergence rate. Computations for which this difference in convergence rate is significant (e.g., multigrid method) are frequently performed using an in-place scheduler, which incurs synchronization overheads to avoid read-write conflicts on the single copy of vertex data. We present Laika, a deterministic in-place scheduler we created using a principled three-step design strategy for high-performance schedulers. Laika reorders the input graph using a Hilbert space-filling curve to improve cache locality and minimizes parallel coordination overhead by explicitly curbing excess execution parallelism. Consequently, Laika has significantly lower scheduling overhead than alternative in-place schedulers and is even faster per iteration than the parallel double-buffered implementation on a reordered input graph. We derive an improved bound on the expected number of cache misses incurred during a traversal of a graph reordered using a space-filling curve. We also prove that on a mesh graph G = (V, E), Laika performs O(|V| + |E|) total work and achieves linear expected speedup with P = O(|V| / log^2 |V|) workers. On 48 cores, Laika yields 38.4x parallel speedup and empirically fares well against comparably well-engineered alternatives: it runs 6.97--12.60 times faster in geometric mean over a suite of input graphs than other parallel schedulers and 222.57 times faster than the baseline serial implementation.

Tensor algebra is an important computational abstraction that is increasingly used in data analytics, machine learning, engineering, and the physical sciences. However, the number of tensor expressions is unbounded, which makes it hard to develop and optimize libraries. Furthermore, the tensors are often sparse (most components are zero), which means the code has to traverse compressed formats. To support programmers we have developed taco, a code generation tool that generates dense, sparse, and mixed kernels from tensor algebra expressions. This paper describes the taco web and command-line tools and discusses the benefits of a code generator over a traditional library. See also the demo video at tensor-compiler.org/ase2017.

Tensor algebra is a powerful tool with applications in machine learning, data analytics, engineering and the physical sciences. Tensors are often sparse and compound operations must frequently be computed in a single kernel for performance and to save memory. Programmers are left to write kernels for every operation of interest, with different mixes of dense and sparse tensors in different formats. The combinations are infinite, which makes it impossible to manually implement and optimize them all. This paper introduces the first compiler technique to automatically generate kernels for any compound tensor algebra operation on dense and sparse tensors. The technique is implemented in a C++ library called taco. Its performance is competitive with best-in-class hand-optimized kernels in popular libraries, while supporting far more tensor operations.

As Domain-Specific Modeling Languages (DSML) become more widespread, it is now possible for non-computer scientists to design complex systems. Not only do DSMLs make modeling more accessible to domain experts, they also improve their efficiency. Using such models, the problem of their maintenance and evolution arises, with common software engineering issues, such as reversibility and traceability of developments. In this article we present the integration of a collaborative edition process into a model-driven engineering (MDE) simulation platform defining its own DSML, which includes a serialization procedure. Our novel approach defines a collaborative work and version control process. This process uses the concrete textual syntax defined by this DSML, which allows us to use the features of existing Version Control Systems (VCS) to work with serialized models, and helps calculate the differences between two versions of a model, merge versions of these models, and detect merge conflicts.

In order to conduct operations research studies on complex systems, CEA/CESTA has been using and developing a new generation of simulator. The inherent complexity of such simulations ensues from the large spectrum of physical phenomena involved. As a consequence the description of a scenario and the analysis of parametric studies are time-consuming tasks. In this paper, we illustrate how the use of modeling techniques can help users construct and interpret simulation scenarios, especially users who are not computer scientists. To that end, we present a domain-specific modeling language for operations research. Moreover its associated tool, which is deployed in a large industrial context, is introduced along with model examples.

Test case generation and specification validation are essential concerns for the software industry in its continuous search for productivity improvement and quality mastering. We present the approach developed in the MUTATION project for functional test case synthesis in the THALES industrial context. A two-step approach is proposed, which automates most of the process in a continuous way. The first step consists in expressing the requirements in the requirements description language (RDL) textual formalism and completing, disambiguating and validating them through simulation. Test objectives are then automatically derived from the RDL formalisation. The second step synthesises functional test cases through combining the test objectives produced at step 1 and symbolic path computation on a UML detailed design model of the application, using the academic tool AGATHA (French acronym for toolset dedicated to automatic test generation for automata based specification). Test cases are produced through selecting symbolic behaviour paths that match the test objectives, applying a constraint solver to generate numeric tests. This approach is based on three innovations that are the core of this work: 1) definition of an appropriate language and a related methodology to define the test requirements; 2) symbolic execution on a UML action language; 3) combination of test objectives and symbolic execution to synthesize functional test cases.