Pedro Alonso

Tools for Power-Energy Modelling and Analysis of Parallel Scientific Applications

Understanding power usage in parallel workloads is crucial to develop the energy-aware software that will run in future Exascale systems. In this paper, we contribute towards this goal by introducing an integrated framework to profile, monitor, model and analyze power dissipation in parallel MPI and multi-threaded scientific applications. The framework includes an own-designed device to measure internal DC power consumption and a package offering a simple interface to interact with this design as well as commercial power meters. Combined with the instrumentation package Extrae and the graphical analysis tool Paraver, the result is a useful environment to identify sources of power inefficiency directly in the source application code. For task-parallel codes, we also offer a statistical software module that inspects the execution trace of the application to calculate the parameters of an accurate model for the global energy consumption, which can be then decomposed into the average power usage per task or the nodal power dissipated per core.

High-performance computing: the essential tool and the essential challenge

High-performance computing (HPC) is nowadays an essential tool for the solution of many problems that arise in both scientific and engineering realms. HPC platforms are based on clusters of multicore nodes, and half of these facilities all around the world also include some type of accelerator device such as graphics processing units (GPUs) or the Intel Xeon Phi coprocessor. Many research interests are addressed to optimize applications that can get the most of these configurations. At the same time, research on the HPC ecosystem (hardware, software tools, applications, etc.) is in the spotlight. In particular, exascale computing is receiving a major interest. The White House Office of Science and Technology Policy highlights the importance of exascale computing for the maintenance of US leadership over the coming decades, and it is for this reason that the United States is doing strategic investments in HPC to meet increasing computing demands and emerging technological challenges. Current and future research faces the natural problems that arise when concurrent resources becomevery large: huge electrical consumption, heat dissipation, and probability of failure, among others. Many problems arise as long as we proceed in the way to developing exascale systems. One of them is the increase of failure rates. This special issue presents the

Backward error analysis of Neville elimination

Abstract Neville elimination is a useful alternative to Gauss elimination in order to study many properties of totally positive matrices. In this paper we perform a backward error analysis of that elimination procedure. In the case of totally positive matrices, the error bounds are similar to those obtained previously by other authors for Gauss elimination.

An entropy measure definition for finite interval-valued hesitant fuzzy sets

In this work, a definition of entropy is studied in an interval-valued hesitant fuzzy environment, instead of the classical fuzzy logic or the interval-valued one. As the properties of this kind of sets are more complex, the entropy is built by three different functions, where each one represents a different measure: fuzziness, lack of knowledge and hesitance. Using all, an entropy measure for interval-valued hesitant fuzzy sets is obtained, quantifying various types of uncertainty.From this definition, several results have been developed for each mapping that shapes the entropy measure in order to get such functions with ease, and as a consequence, allowing to obtain this new entropy in a simpler way.

DVFS-control techniques for dense linear algebra operations on multi-core processors

This paper analyzes the impact on power consumption of two DVFS-control strategies when applied to the execution of dense linear algebra operations on multi-core processors. The strategies considered here, prototyped as the Slack Reduction Algorithm (SRA) and the Race-to-Idle Algorithm (RIA), adjust the operation frequency of the cores during execution of a collection of tasks (in which many dense linear algebra algorithms can be decomposed) with a very different approach to save energy. A power-aware simulator, in charge of scheduling the execution of tasks to processor cores, is employed to evaluate the performance benefits of these power-control policies for two reference algorithms for the LU factorization, a key operation for the solution of linear systems of equations.

Solving Some Mysteries in Power Monitoring of Servers: Take Care of Your Wattmeters!

Large-scale distributed systems e.g., datacenters, HPC systems, clouds, large-scale networks, etc. consume and will consume enormous amounts of energy. Therefore, accurately monitoring the power and energy consumption of these systems is increasingly more unavoidable. The main novelty of this contribution is the analysis and evaluation of different external and internal power monitoring devices tested using two different computing systems, a server and a desktop machine. Furthermore, we also provide experimental results for a variety of benchmarks which exercise intensively the main components CPU, Memory, HDDs, and NICs of the target platforms to validate the accuracy of the equipment in terms of power dispersion and energy consumption. This paper highlights that external wattmeters do not offer the same measures as internal wattmeters. Thanks to the high sampling rate and to the different measured lines, the internal wattmeters allow an improved visualization of some power fluctuations. However, a high sampling rate is not always necessary to understand the evolution of the power consumption during the execution of a benchmark.

Conditioning and accurate computations with Pascal matrices

A result on the ill-conditioning of Pascal matrices is proved. However, it is shown that bidiagonal factorizations of Pascal matrices can be applied to perform computations with high relative accuracy.

Reducing Energy Consumption of Dense Linear Algebra Operations on Hybrid CPU-GPU Platforms

We investigate the balance between the time-to-solution and the energy consumption of a task-parallel execution of the Cholesky and LU factorizations on a hybrid platform, equipped with a multi-core processor and several GPUs. To improve energy efficiency, we incorporate two energy-saving techniques in the runtime in charge of scheduling the computations, to block idle threads and enable the transition to a more energy-friendly state of the general-purpose cores. Experiments on an Intel Xeon-based platform connected to an NVIDIA Tesla server report an average reduction of the energy consumption close to 9% (38% when only the consumption associated with the application is considered), for a minor increase in the execution time of the algorithm.

Neville elimination on multi- and many-core systems: OpenMP, MPI and CUDA

This paper describes several parallel algorithmic variations of the Neville elimination. This elimination solves a system of linear equations making zeros in a matrix column by adding to each row an adequate multiple of the preceding one. The parallel algorithms are run and compared on different multi- and many-core platforms using parallel programming techniques as MPI, OpenMP and CUDA.

Synthesis of Cyclic Alkenyl Triflates by a Cationic Cyclization Reaction and its Application in Biomimetic Polycyclizations and Synthesis of Terpenes.

Cyclic alkenyl triflates are useful intermediates in organic synthesis usually synthesized from ketones through a reaction involving enolization and trapping with a triflating agent. This sequence suffers from some stereochemical drawbacks owing to the basic conditions required. Herein, we describe a new acid-mediated cationic cyclization reaction of enyne derivatives (or alkynols) to access cyclic alkenyl triflates. This new atom-economical process is high yielding, scalable, technically very simple, proceeds without the need of any metallic reagent or catalyst, and more importantly, it complements and challenges conventional methodologies. We have also developed new biomimetic cationic cyclization reactions to yield interesting polycyclic compounds. As a demonstration of the potential of this method in the context of total synthesis, we have synthesized two terpenes: austrodoral and pallescensin A. Using the cationic cyclization in the key step of the synthetic routes allowed the synthesis of these natural products in a very simple, concise, scalable, and efficient way.