Una modelización de los años de vida ajustados por la calidad como utilidades esperadas

En el presente trabajo, los a\~nos de vida ajustados por la calidad (AVAC), son caracterizados como funciones de utilidad von Newman--Morgenstern. Esta caracterizaci\'on se efect\'ua para dos problemas de elecci\'on: la elecci\'on entre loter{\'\i}as definidas sobre estados de salud cr\'onicos y la elecci\'on entre loter{\'\i}as definidas sobre estados de salud temporales. En el primer caso, deducimos las mismas condiciones que Pliskin et al. (1980), s\'olo que siguiendo un camino m\'as directo. En el segundo caso, una vez establecida una condici\'on de independencia aditiva basada en Fishburn (1970), inferimos una nueva condici\'on en la literatura sobre AVAC que denominamos \underline{condici\'on de simetr{\'\i}a}.QALYs, expected utility, medical decision making

Climate Matching Drives Spread Rate but Not Establishment Success in Recent Unintentional Bird Introductions

Understanding factors driving successful invasions is one of the cornerstones of invasion biology. Bird invasions have been frequently used as study models, and the foundation of current knowledge largely relies on species purposefully introduced during the 19th and early 20th centuries in countries colonized by Europeans. However, the profile of exotic bird species has changed radically in the last decades, as birds are now mostly introduced into the invasion process through unplanned releases from the worldwide pet and avicultural trade. Here we assessed the role of the three main drivers of invasion success (i.e., event-, species-, and location-level factors) on the establishment and spatial spread of exotic birds using an unprecedented dataset recorded throughout the last 100 y in the Iberian Peninsula. Our multimodel inference phylogenetic approach showed that the barriers that need to be overcome by a species to successfully establish or spread are not the same. Whereas establishment is largely related to event-level factors, apparently stochastic features of the introduction (time since first introduction and propagule pressure) and to the origin of introduced species (wild-caught species show higher invasiveness than captive-bred ones), the spread across the invaded region seems to be determined by the extent to which climatic conditions in the new region resemble those of the species’ native range. Overall, these results contrast with what we learned from successful deliberate introductions and highlight that different management interventions should apply at different invasion stages, the most efficient strategies being related to event-level factors

METADOCK 2: a high-throughput parallel metaheuristic scheme for molecular docking

[EN] Motivation Molecular docking methods are extensively used to predict the interaction between protein-ligand systems in terms of structure and binding affinity, through the optimization of a physics-based scoring function. However, the computational requirements of these simulations grow exponentially with: (i) the global optimization procedure, (ii) the number and degrees of freedom of molecular conformations generated and (iii) the mathematical complexity of the scoring function. Results In this work, we introduce a novel molecular docking method named METADOCK 2, which incorporates several novel features, such as (i) a ligand-dependent blind docking approach that exhaustively scans the whole protein surface to detect novel allosteric sites, (ii) an optimization method to enable the use of a wide branch of metaheuristics and (iii) a heterogeneous implementation based on multicore CPUs and multiple graphics processing units. Two representative scoring functions implemented in METADOCK 2 are extensively evaluated in terms of computational performance and accuracy using several benchmarks (such as the well-known DUD) against AutoDock 4.2 and AutoDock Vina. Results place METADOCK 2 as an efficient and accurate docking methodology able to deal with complex systems where computational demands are staggering and which outperforms both AutoDock Vina and AutoDock 4.This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia [Projects 20813/PI/ 18, 20988/PI/18, 20524/PDC/18] and by the Spanish Ministry of Science, Innovation and Universities [TIN2016-78799-P (AEI/FEDER, UE), CTQ2017-87974-R]. The authors thankfully acknowledge the computer resources at CTE-POWER and the technical support provided by Barcelona Supercomputing Center - Centro Nacional de Supercomputación [RES-BCV2018-3-0008].Imbernón, B.; Serrano, A.; Bueno-Crespo, A.; Abellán, JL.; Pérez-Sánchez, H.; Cecilia-Canales, JM. (2020). METADOCK 2: a high-throughput parallel metaheuristic scheme for molecular docking. Bioinformatics. 1-6. https://doi.org/10.1093/bioinformatics/btz958S16Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239-287. doi:10.1007/s11047-008-9098-4Cecilia, J. M., Llanes, A., Abellán, J. L., Gómez-Luna, J., Chang, L.-W., & Hwu, W.-M. W. (2018). High-throughput Ant Colony Optimization on graphics processing units. Journal of Parallel and Distributed Computing, 113, 261-274. doi:10.1016/j.jpdc.2017.12.002Desiraju, G., & Steiner, T. (2001). 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LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance. Journal of Chemical Information and Modeling, 56(1), 188-200. doi:10.1021/acs.jcim.5b00234Llanes, A., Muñoz, A., Bueno-Crespo, A., García-Valverde, T., Sánchez, A., Arcas-Túnez, F., … M. Cecilia, J. (2016). Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures. Current Drug Targets, 17(14), 1626-1648. doi:10.2174/1389450117666160201114028McIntosh-Smith, S., Price, J., Sessions, R. B., & Ibarra, A. A. (2014). High performance in silico virtual drug screening on many-core processors. The International Journal of High Performance Computing Applications, 29(2), 119-134. doi:10.1177/1094342014528252Mehler, E. L., & Solmajer, T. (1991). Electrostatic effects in proteins: comparison of dielectric and charge models. «Protein Engineering, Design and Selection», 4(8), 903-910. doi:10.1093/protein/4.8.903Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. 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Design Space Exploration of Accelerators and End-to-End DNN Evaluation with TFLITE-SOC

Recently there has been a rapidly growing demand for faster machine learning (ML) processing in data centers and migration of ML inference applications to edge devices. These developments have prompted both industry and academia to explore custom accelerators to optimize ML executions for performance and power. However, identifying which accelerator is best equipped for performing a particular ML task is challenging, especially given the growing range of ML tasks, the number of target environments, and the limited number of integrated modeling tools. To tackle this issue, it is of paramount importance to provide the computer architecture research community with a common framework capable of performing a comprehensive, uniform, and fair comparison across different accelerator designs targeting a particular ML task. To this aim, we propose a new framework named TFLITESOC (System On Chip) that integrates a lightweight system modeling library (SystemC) for fast design space exploration of custom ML accelerators into the build/execution environment of Tensorflow Lite (TFLite), a highly popular ML framework for ML inference. Using this approach, we are able to model and evaluate new accelerators developed in SystemC by leveraging the language’s hierarchical design capabilities, resulting in faster design prototyping. Furthermore, any accelerator designed using TFLITE-SOC can be benchmarked for inference with any DNN model compatible with TFLite, which enables end-to-end DNN processing and detailed (i.e., per DNN layer) performance analysis. In addition to providing rapid prototyping, integrated benchmarking, and a range of platform configurations, TFLITESOC offers comprehensive performance analysis of accelerator occupancy and execution time breakdown as well as a rich set of modules that can be used by new accelerators to implement scaling up studies and optimized memory transfer protocols. We present our framework and demonstrate its utility by considering the design space of a TPU-like systolic array and describing possible directions for optimization. Using a compression technique, we implement an optimization targeting reducing the memory traffic between DRAM and on-device buffers. Compared to the baseline accelerator, our optimized design shows up to 1.26x speedup on accelerated operations and up to 1.19x speedup on end-to-end DNN execution

GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption

Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure outsourcing of data processing to remote cloud services. Despite its promise of strong data privacy and security guarantees, FHE introduces a slowdown of up to five orders of magnitude as compared to the same computation using plaintext data. This overhead is presently a major barrier to the commercial adoption of FHE. While prior efforts recommend moving to custom accelerators to accelerate FHE computing, these solutions lack cost-effectiveness and scalability. In this work, we leverage GPUs to accelerate FHE, capitalizing on a well-established GPU ecosystem that is available in the cloud. We propose GME, which combines three key microarchitectural extensions along with a compile-time optimization to the current AMD CDNA GPU architecture. First, GME integrates a lightweight on-chip compute unit (CU)-side hierarchical interconnect to retain ciphertext in cache across FHE kernels, thus eliminating redundant memory transactions and improving performance. Second, to tackle compute bottlenecks, GME introduces special MOD-units that provide native custom hardware support for modular reduction operations, one of the most commonly executed sets of operations in FHE. Third, by integrating the MOD-unit with our novel pipelined 64-bit integer arithmetic cores (WMAC-units), GME further accelerates FHE workloads by 19%. Finally, we propose a Locality-Aware Block Scheduler (LABS) that improves FHE workload performance, exploiting the temporal locality available in FHE primitive blocks. Incorporating these microarchitectural features and compiler optimizations, we create a synergistic approach achieving average speedups of 796×, 14.2×, and 2.3× over Intel Xeon CPU, NVIDIA V100 GPU, and Xilinx FPGA implementations, respectively

GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption

Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure outsourcing of data processing to remote cloud services. Despite its promise of strong data privacy and security guarantees, FHE introduces a slowdown of up to five orders of magnitude as compared to the same computation using plaintext data. This overhead is presently a major barrier to the commercial adoption of FHE. In this work, we leverage GPUs to accelerate FHE, capitalizing on a well-established GPU ecosystem available in the cloud. We propose GME, which combines three key microarchitectural extensions along with a compile-time optimization to the current AMD CDNA GPU architecture. First, GME integrates a lightweight on-chip compute unit (CU)-side hierarchical interconnect to retain ciphertext in cache across FHE kernels, thus eliminating redundant memory transactions. Second, to tackle compute bottlenecks, GME introduces special MOD-units that provide native custom hardware support for modular reduction operations, one of the most commonly executed sets of operations in FHE. Third, by integrating the MOD-unit with our novel pipelined $64$-bit integer arithmetic cores (WMAC-units), GME further accelerates FHE workloads by $19\%$. Finally, we propose a Locality-Aware Block Scheduler (LABS) that exploits the temporal locality available in FHE primitive blocks. Incorporating these microarchitectural features and compiler optimizations, we create a synergistic approach achieving average speedups of $796\times$, $14.2\times$, and $2.3\times$ over Intel Xeon CPU, NVIDIA V100 GPU, and Xilinx FPGA implementations, respectively