Parallel Two-Stage Least Squares algorithms for Simultaneous Equations Models on GPU

Today it is usual to have computational systems formed by a multicore together with one or more GPUs. These systems are heterogeneous, due to the di erent types of memory in the GPUs and to the di erent speeds of computation of the cores in the CPU and the GPU. To accelerate the solution of complex problems it is necessary to combine the two basic components (CPU and GPU) in the heterogeneous system. This paper analyzes the use of a multicore+multiGPU system for solving Simultaneous Equations Models by the Two-Stage Least Squares method with QR decomposition. The combination of CPU and GPU allows us to reduce the execution time in the solution of large SEM.Ramiro Sánchez, C.; López-Espín, JJ.; Giménez, D.; Vidal, AM. (2012). Parallel Two-Stage Least Squares algorithms for Simultaneous Equations Models on GPU. http://hdl.handle.net/10251/1496

Exploiting Heterogeneous Parallelism on Hybrid Metaheuristics for Vector Autoregression Models

In the last years, the huge amount of data available in many disciplines makes the mathematical modeling, and, more concretely, econometric models, a very important technique to explain those data. One of the most used of those econometric techniques is the Vector Autoregression Models (VAR) which are multi-equation models that linearly describe the interactions and behavior of a group of variables by using their past. Traditionally, Ordinary Least Squares and Maximum likelihood estimators have been used in the estimation of VAR models. These techniques are consistent and asymptotically efficient under ideal conditions of the data and the identification problem. Otherwise, these techniques would yield inconsistent parameter estimations. This paper considers the estimation of a VAR model by minimizing the difference between the dependent variables in a certain time, and the expression of their own past and the exogenous variables of the model (in this case denoted as VARX model). The solution of this optimization problem is approached through hybrid metaheuristics. The high computational cost due to the huge amount of data makes it necessary to exploit High-Performance Computing for the acceleration of methods to obtain the models. The parameterized, parallel implementation of the metaheuristics and the matrix formulation ease the simultaneous exploitation of parallelism for groups of hybrid metaheuristics. Multilevel and heterogeneous parallelism are exploited in multicore CPU plus multiGPU nodes, with the optimum combination of the different parallelism parameters depending on the particular metaheuristic and the problem it is applied to.This work was supported by the Spanish MICINN and AEI, as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53 and grant TIN2016-80565-R

Structural determinants in ApoA-I amyloidogenic variants explain improved cholesterol metabolism despite low HDL levels.

Twenty Apolipoprotein A-I (ApoA-I) variants are responsible for a systemic hereditary amyloidosis in which protein fibrils can accumulate in different organs, leading to their failure. Several ApoA-I amyloidogenic mutations are also associated with hypoalphalipoproteinemia, low ApoA-I and high-density lipoprotein (HDL)-cholesterol plasma levels; however, subjects affected by ApoA-I-related amyloidosis do not show a higher risk of cardiovascular diseases (CVD). The structural features, the lipid binding properties and the functionality of four ApoA-I amyloidogenic variants were therefore inspected in order to clarify the paradox observed in the clinical phenotype of the affected subjects. Our results show that ApoA-I amyloidogenic variants are characterized by a different oligomerization pattern and that the position of the mutation in the ApoA-I sequence affects the molecular structure of the formed HDL particles. Although lipidation increases ApoA-I proteins stability, all the amyloidogenic variants analyzed show a lower affinity for lipids, both in vitro and in ex vivo mouse serum. Interestingly, the lower efficiency at forming HDL particles is compensated by a higher efficiency at catalysing cholesterol efflux from macrophages. The decreased affinity of ApoA-I amyloidogenic variants for lipids, together with the increased efficiency in the cholesterol efflux process, could explain why, despite the unfavourable lipid profile, patients affected by ApoA-I related amyloidosis do not show a higher CVD risk

Contemporary Management of Locally Advanced and Recurrent Rectal Cancer: Views from the PelvEx Collaborative

Pelvic exenteration is a complex operation performed for locally advanced and recurrent pelvic cancers. The goal of surgery is to achieve clear margins, therefore identifying adjacent or involved organs, bone, muscle, nerves and/or vascular structures that may need resection. While these extensive resections are potentially curative, they can be associated with substantial morbidity. Recently, there has been a move to centralize care to specialized units, as this facilitates better multi-disciplinary care input. Advancements in pelvic oncology and surgical innovation have redefined the boundaries of pelvic exenterative surgery. Combined with improved neoadjuvant therapies, advances in diagnostics, and better reconstructive techniques have provided quicker recovery and better quality of life outcomes, with improved survival This article provides highlights of the current management of advanced pelvic cancers in terms of surgical strategy and potential future developments

ApoAI-derived peptide increases glucose tolerance and prevents formation of atherosclerosis in mice

AIMS/HYPOTHESIS: Finding new treatment alternatives for individuals with diabetes with severe insulin resistance is highly desired. To identify novel mechanisms that improve glucose uptake in skeletal muscle, independently from insulin levels and signalling, we have explored the therapeutic potential of a short peptide sequence, RG54, derived from apolipoprotein A-I (ApoA-I).METHODS: INS-1E rat clonal beta cells, C2C12 rat muscle myotubes and J774 mouse macrophages were used to study the impact of RG54 peptide on glucose-stimulated insulin secretion, glucose uptake and cholesterol efflux, respectively. GTTs were carried out on diet-induced insulin-resistant and Leprdb diabetic mouse models treated with RG54 peptide, and the impact of RG54 peptide on atherosclerosis was evaluated in Apoe-/- mice. Control mice received ApoA-I protein, liraglutide or NaCl.RESULTS: The synthetic RG54 peptide induced glucose uptake in cultured muscle myotubes by a similar amount as insulin, and also primed pancreatic beta cells for improved glucose-stimulated insulin secretion. The findings were verified in diet-induced insulin-resistant and Leprdb diabetic mice, jointly confirming the physiological effect. The RG54 peptide also efficiently catalysed cholesterol efflux from macrophages and prevented the formation of atherosclerotic plaques in Apoe-/- mice.CONCLUSIONS/INTERPRETATION: The RG54 peptide exhibits good prospects for providing glucose control and reducing the risk of cardiovascular disease in individuals with severe insulin resistance

Two-stage least squares and indirect least squares algorithms for simultaneous equations models

This paper analyzes the solution of simultaneous equations models. Efficient algorithms for the two-stage least squares method using QR-decomposition are developed and studied. The reduction of the execution time when the structure of the matrices in each equation is exploited is analyzed theoretically and experimentally. An efficient algorithm for the indirect least squares method is developed. Some techniques are used to accelerate the solution of the problem: parallel versions for multicore systems, and extensive use of the MKL library, thus obtaining efficient, portable versions of the algorithms. © 2011 Elsevier B.V. All rights reserved.López Espín, JJ.; Vidal Maciá, AM.; Giménez ., D. (2012). Two-stage least squares and indirect least squares algorithms for simultaneous equations models. Journal of Computational and Applied Mathematics. 236(15):3676-3684. doi:10.1016/j.cam.2011.07.005S367636842361