The application of graph databases to different domains is gaining momentum. The Resource Description Framework (RDF) is one of the data models supported by graph databases, and SPARQL is the standard query language for RDF graphs. These databases are also known as RDF triplestores. Many triplestores are implemented over the relational data model, using tables to store graphs and translating SPARQL queries into SQL queries, and this approach can lead to unnecessary overheads. On the other hand, in the context of High- Performance Computing (HPC), implementations over hybrid hardware platforms using Numerical Linear Algebra (NLA) operations have become an effective and efficient computing strategy in the last decade. In particular, Graphics Processing Units (GPUs) have been adopted to perform general-purpose computations due to their high performance, reasonable prices, and an attractive relationship between computing capacity and energy consumption. In the context described above, this paper presents an initial study on the efficient implementation of a set of SPARQL queries in terms of NLA operations. Additionally, we evaluate the performance of implementing these operations on GPUs.

Since the beginning of 2020, COVID-19 has had a strong impact on the health of the world population. Tracing the contacts of infected people is one of the main strategies for controlling the pandemic. Given the high rates of contagion, which makes difficult an effective manual tracing, multiple initiatives arose for developing digital proximity tracing technologies. In this paper, we discuss in depth the security and personal data protection requirements that these technologies must satisfy, and we present an exhaustive and detailed list of the various applications that have been deployed globally. In particular, we identify potential threats that could undermine the satisfaction of the analyzed requirements, violating hegemonic personal data protection regulations.

The solution of sparse triangular linear systems is an important building block for a large number of numerical methods used in science and engineering. It is then crucial to count with implementations of this operation that can execute efficiently in the most recent hardware platforms. In the case of GPUs, several methods have been proposed in the last years. These methods belong to two main categories. On the one hand, there are the methods that rely on a previous analysis of the sparse matrix to determine a better execution schedule and, on the other hand, there are methods that decide this scheduling dynamically. The experimental results in the literature are not conclussive in favour of any of these strategies. However, the experimental evaluations usually focus on the use case where many systems have to be solved with the same sparse matrix, where the analysis phase needs to be performed only once and its cost is not important in relation with the total runtime. In this work we are interested in determining which is the best strategy, according to the degree of parallelism of the problem, when only one sytem is to be solved. The experimental evaluation performed on NVIDIA P100 accelerators shows that the self-scheduled routines present important advantages when the degree of parallelism of the problem allows it.

The solution of sparse linear systems of large dimension is a important stage in problems that span a diverse kind of applications. For this reason, a number of iterative solvers have been developed, among which ILUPACK integrates an inverse-based multilevel ILU preconditioner with appealing numerical properties. In this work we extend the iterative methods available in ILUPACK. Concretely, we develop a data-parallel implementation of the BiCGStab method for GPUs hardware platforms that completes the functionality of ILUPACK-preconditioned solvers for general linear systems. The experimental evaluation carried out in a hybrid hardware platform, including a multicore CPU and a Nvidia GPU, shows that our novel proposal reaches speedups values between 5 and 10× when is compared with the CPU counterpart and values of up to 8.2× runtime reduction over other GPU solvers.

In this paper we address the use of high performance computing techniques with the aim of accelerating the runtime of a numerical model for the South Atlantic Ocean. This numerical model is a component of a larger system, that includes higher precision models to compute the hidrodynamics of the Río de la Plata river. Our work includes, in a first stage, a thorough study of the use of traditional HPC techniques on the CPU, and in a second stage we perform a preliminar study about the use of GPUs to accelerate the optimized version of the model. Specifically, for the first stage we made a profiling of the original model to identify the routines with larger computational cost. After that, we design and implement some variants for the three most expensive ones. Finally, we validate the proposals with an experimental evaluation. The results obtained show important acceleration values for the routines (up to 11 x), and these accelerations impact in the whole model with a runtime reduction of more than three times. Finally, we migrated one of the most costly routines of the optimized version to the GPU as a proof of concept.

This work addresses the resolution of the transport (advection-diffusion) equation in 3D using an explicit scheme for the finite d ifference method. Our initative is motivated by the advantages offered by this scheme for parallel processing. We propose three implementations, a sequential code (in C) and two parallel versions (C-CUDA and C with OpenMP). The experimental comparison is focused on the performance of each implementation using different grid sizes, and in the case of the OpenMP implementation, several number of threads. Additionally, we measured the accurancy of this scheme when the detail of the discretization grows. The results show that the parallel implementations reach significant speed up compared with the sequential counterpart. In addition, the GPU variant offers an further runtime reduction of up to 10x.

This work studies the use of architectures that include GPUs to accelerate the most popular benchmarks in the high performance computing field, namely HPL, HPCG and the one used for the Graph500 ranking. Specifically, the installation and configuration of implementations of these benchmarks that are able to exploit the computer power of different massively parallel hardware platforms are discussed in order to provide a helpful insight on these topics to other researchers. The results obtained in the experimental evaluation show the benefits of the use of this kind of hardware architectures in computation-bounded and memory-bounded algorithms.

The necessity of reducing the energy consumption while improving the computational performance has encouraged the development of new hardware platforms. In this line, hybrid architectures that integrate ARM processors with graphics accelerators offer a positive balance between computing capabilities and energy requirements. However, in order to make an efficient use of this hardware, it is necessary to develop new methods and computational kernels, as well as to adapt existing ones. The solution of linear systems of equations is a basic operation in the solution of different problems. Its relevance and computational cost has motivated an important amount of work, and in consequence, it is possible to find high performance solvers for most hardware platforms. In this work we study the solution of dense linear systems of equations in an NVIDIA Jetson TK1 device via the Gauss-Huard method. The experimental evaluation shows that the new solvers outperform the ones available in the MAGMA library for systems of dimesion n ≤ 6,000.

In this paper, we leverage the intrinsic data-parallelism of the band matrix-matrix product to accelerate this operation on Graphics Processing Units (GPUs). In particular, we propose a Level-3 BLAS style algorithm to tackle the band matrix-matrix product and implement two GPU-based versions that off-load the most expensive computations - i.e., general dense matrix-matrix multiplication, triangular matrixmatrix multiplication and matrix addition - to the hardware accelerator. Results collected using GPUs for the two most recent generations of NVIDIA (“Fermi” and “Kepler”) and a complete set of benchmark cases (which differ in the matrix dimensions and bandwidth) show that the GPU-enabled implementations deliver a notable reduction of the execution time.

Widely used for the resolution of radiative heat transfer calculations, the radiosity method entails the use of view factors which may require significant calculation efforts in complex geometries. In this work, we study the heat transfer of the filament of an incandescent light bulb using the radiosity method. Due to the high computational cost of the Monte Carlo method used for computing the view factors, two high-performance computing (HPC) techniques, namely, a parallel multi-core approach based on OpenMP and a massively parallel implementation over two different graphics processing units (GPUs) in CUDA were study. The use of such techniques enabled to reduce the calculation time up to 10× in two Quad Core INTEL Xeon processors, 112× in an NVIDIA Tesla C1060 GPU and 199× in an NVIDIA Tesla C2070.