In this paper, we present OmpSs, a programming model based on OpenMP and StarSs, that can also incorporate the use of OpenCL or CUDA kernels. We evaluate the proposal on different architectures, SMP, GPUs, and hybrid SMP/GPU environments, showing the wide usefulness of the approach. The evaluation is done with six different benchmarks, Matrix Multiply, BlackScholes, Perlin Noise, Julia Set, PBPI and FixedGrid. We compare the results obtained with the execution of the same benchmarks written in OpenCL or OpenMP, on the same architectures. The results show that OmpSs greatly outperforms both environments. With the use of OmpSs the programming environment is more flexible than traditional approaches to exploit multiple accelerators, and due to the simplicity of the annotations, it increases programmer's productivity.

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Context. Open clusters are key targets for both Galaxy structure and evolution and stellar physics studies. Since Gaia DR2 publication, the discovery of undetected clusters has proven that our samples were not complete. Aims. Our aim is to exploit the Big Data capabilities of machine learning to detect new open clusters in Gaia DR2, and to complete the open cluster sample to enable further studies on the Galactic disc. Methods. We use a machine learning based methodology to systematically search in the Galactic disc, looking for overdensities in the astrometric space and identifying them as open clusters using photometric information. First, we use an unsupervised clustering algorithm, DBSCAN, to blindly search for these overdensities in Gaia DR2 (l, b, , µ α * , µ δ ). After that, we use a deep learning artificial neural network trained on colour-magnitude diagrams to identify isochrone patterns in these overdensities, and to confirm them as open clusters. Results. We find 582 new open clusters distributed along the Galactic disc, in the region |b| < 20 • . We can detect substructure in complex regions, and identify the tidal tails of a disrupting cluster UBC 274 of ∼ 3 Gyr located at ∼ 2 kpc. Conclusions. Adapting the methodology into a Big Data environment allows us to target the search driven by physical properties of the open clusters, instead of being driven by its computational requirements. This blind search for open clusters in the Galactic disc increases in a 45% the number of known open clusters.

In this work we present Cell superscalar (CellSs) which addresses the automatic exploitation of the functional parallelism of a sequential program through the different processing elements of the Cell BE architecture. The focus in on the simplicity and flexibility of the programming model. Based on a simple annotation of the source code, a source to source compiler generates the necessary code and a runtime library exploits the existing parallelism by building at runtime a task dependency graph. The runtime takes care of the task scheduling and data handling between the different processors of this heterogeneous architecture. Besides, a locality-aware task scheduling has been implemented to reduce the overhead of data transfers. The approach has been implemented and tested with a set of examples and the results obtained since now are promising.

Cycle-accurate simulation is far too slow for modeling the expected performance of full parallel applications on large HPC systems. And just running an application on a system and observing wallclock time tells you nothing about why the application performs as it does (and is anyway impossible on yet-to-be-built systems). Here we present a framework for performance modeling and prediction that is faster than cycle-accurate simulation, more informative than simple benchmarking, and is shown useful for performance investigations in several dimensions.

Abstract-Parallel programming on SMP and multi-core architectures is hard. In this paper we present a programming model for those environments based on automatic function level parallelism that strives to be easy, flexible, portable, and performant. Its main trait is its ability to exploit task level parallelism by analyzing task dependencies at run time. We present the programming environment in the context of algorithms from several domains and pinpoint its benefits compared to other approaches. We discuss its execution model and its scheduler. Finally we analyze its performance and demonstrate that it offers reasonable performance without tuning, and that it can rival highly tuned libraries with minimal tuning effort.

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Programming models for multicore and many-core systems are listed as one of the main challenges in the near future for computing research. These programming models should be able to exploit the underlying platform, but also should have good programmability to enable programmer productivity. With respect to the heterogeneity and hierarchy of the underlying platforms, the programming models should take them into account but they should also enable the programmer to be unaware of the complexity of the hardware. In this paper we present an extension of the StarSs syntax to support task hierarchy. A motivation for such a hierarchical approach is presented through experimentation with CellSs. A prototype implementation of such a hierarchical task-based programming model that combines a first task level with SMPSs and a second task level with CellSs is presented. The preliminary results obtained when executing a matrix multiplication and a Cholesky factorization show the viability and potential of the approach and the current issues raised.