Demystifying the Performance of HPC Scientific Applications on NVM-based Memory Systems

The emergence of high-density byte-addressable non-volatile memory (NVM) is promising to accelerate data- and compute-intensive applications. Current NVM technologies have lower performance than DRAM and, thus, are often paired with DRAM in a heterogeneous main memory. Recently, byte-addressable NVM hardware becomes available. This work provides a timely evaluation of representative HPC applications from the "Seven Dwarfs" on NVM-based main memory. Our results quantify the effectiveness of DRAM-cached-NVM for accelerating HPC applications and enabling large problems beyond the DRAM capacity. On uncached-NVM, HPC applications exhibit three tiers of performance sensitivity, i.e., insensitive, scaled, and bottlenecked. We identify write throttling and concurrency control as the priorities in optimizing applications. We highlight that concurrency change may have a diverging effect on read and write accesses in applications. Based on these findings, we explore two optimization approaches. First, we provide a prediction model that uses datasets from a small set of configurations to estimate performance at various concurrency and data sizes to avoid exhaustive search in the configuration space. Second, we demonstrate that write-aware data placement on uncached-NVM could achieve $2$x performance improvement with a 60% reduction in DRAM usage.Comment: 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS2020

Characterizing Deep-Learning I/O Workloads in TensorFlow

The performance of Deep-Learning (DL) computing frameworks rely on the performance of data ingestion and checkpointing. In fact, during the training, a considerable high number of relatively small files are first loaded and pre-processed on CPUs and then moved to accelerator for computation. In addition, checkpointing and restart operations are carried out to allow DL computing frameworks to restart quickly from a checkpoint. Because of this, I/O affects the performance of DL applications. In this work, we characterize the I/O performance and scaling of TensorFlow, an open-source programming framework developed by Google and specifically designed for solving DL problems. To measure TensorFlow I/O performance, we first design a micro-benchmark to measure TensorFlow reads, and then use a TensorFlow mini-application based on AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow. To improve the checkpointing performance, we design and implement a burst buffer. We find that increasing the number of threads increases TensorFlow bandwidth by a maximum of 2.3x and 7.8x on our benchmark environments. The use of the tensorFlow prefetcher results in a complete overlap of computation on accelerator and input pipeline on CPU eliminating the effective cost of I/O on the overall performance. The use of a burst buffer to checkpoint to a fast small capacity storage and copy asynchronously the checkpoints to a slower large capacity storage resulted in a performance improvement of 2.6x with respect to checkpointing directly to slower storage on our benchmark environment.Comment: Accepted for publication at pdsw-DISCS 201

A Quantitative Approach for Adopting Disaggregated Memory in HPC Systems

Memory disaggregation has recently been adopted in data centers to improve resource utilization, motivated by cost and sustainability. Recent studies on large-scale HPC facilities have also highlighted memory underutilization. A promising and non-disruptive option for memory disaggregation is rack-scale memory pooling, where shared memory pools supplement node-local memory. This work outlines the prospects and requirements for adoption and clarifies several misconceptions. We propose a quantitative method for dissecting application requirements on the memory system from the top down in three levels, moving from general, to multi-tier memory systems, and then to memory pooling. We provide a multi-level profiling tool and LBench to facilitate the quantitative approach. We evaluate a set of representative HPC workloads on an emulated platform. Our results show that prefetching activities can significantly influence memory traffic profiles. Interference in memory pooling has varied impacts on applications, depending on their access ratios to memory tiers and arithmetic intensities. Finally, in two case studies, we show the benefits of our findings at the application and system levels, achieving 50% reduction in remote access and 13% speedup in BFS, and reducing performance variation of co-located workloads in interference-aware job scheduling.Comment: Accepted to SC23 (The International Conference for High Performance Computing, Networking, Storage, and Analysis 2023

An autoencoder compression approach for accelerating large-scale inverse problems

PDE-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes that are required to accurately compute the PDE solution introduce an enormous number of parameters and require large scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time dependent PDEs, the adjoint method that is often employed to efficiently compute gradients and higher order derivatives requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing both the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for both the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior to show how the DIAS method can be affordably extended to large scale problems without the need of checkpointing and large memory

Understanding and Optimizing Serverless Workloads in CXL-Enabled Tiered Memory

Recent Serverless workloads tend to be largescaled/CPU-memory intensive, such as DL, graph applications, that require dynamic memory-to-compute resources provisioning. Meanwhile, recent solutions seek to design page management strategies for multi-tiered memory systems, to efficiently run heavy workloads. Compute Express Link (CXL) is an ideal platform for serverless workloads runtime that offers a holistic memory namespace thanks to its cache coherent feature and large memory capacity. However, naively offloading Serverless applications to CXL brings substantial latencies. In this work, we first quantify CXL impacts on various Serverless applications. Second, we argue the opportunity of provisioning DRAM and CXL in a fine-grained, application-specific manner to Serverless workloads, by creating a shim layer to identify, and naively place hot regions to DRAM, while leaving cold/warm regions to CXL. Based on the observation, we finally propose the prototype of Porter, a middleware in-between modern Serverless architecture and CXL-enabled tiered memory system, to efficiently utilize memory resources, while saving costs

Sustainable HPC: Modeling, Characterization, and Implications of Carbon Footprint in Modern HPC Systems

The rapid growth in demand for HPC systems has led to a rise in energy consumption and carbon emissions, which requires urgent intervention. In this work, we present a comprehensive framework for analyzing the carbon footprint of high-performance computing (HPC) systems, considering the carbon footprint during both the hardware production and system operational stages. Our work employs HPC hardware component carbon footprint modeling, regional carbon intensity analysis, and experimental characterization of the system life cycle to highlight the importance of quantifying the carbon footprint of an HPC system holistically

Evaluation of STT-MRAM main memory for HPC and real-time systems

It is questionable whether DRAM will continue to scale and will meet the needs of next-generation systems. Therefore, significant effort is invested in research and development of novel memory technologies. One of the candidates for nextgeneration memory is Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM). STT-MRAM is an emerging non-volatile memory with a lot of potential that could be exploited for various requirements of different computing systems. Being a novel technology, STT-MRAM devices are already approaching DRAM in terms of capacity, frequency and device size. Special STT-MRAM features such as intrinsic radiation hardness, non-volatility, zero stand-by power and capability to function in extreme temperatures also make it particularly suitable for aerospace, avionics and automotive applications. Despite of being a conceivable alternative for main memory technology, to this day, academic research of STT-MRAM main memory remains marginal. This is mainly due to the unavailability of publicly available detailed timing parameters of this novel technology, which are required to perform a cycle accurate main memory simulation. Some researchers adopt simplistic memory models to simulate main memory, but such models can introduce significant errors in the analysis of the overall system performance. Therefore, detailed timing parameters are a must-have for any evaluation or architecture exploration study of STT-MRAM main memory. These detailed parameters are not publicly available because STT-MRAM manufacturers are reluctant to release any delicate information on the technology. This thesis demonstrates an approach to perform a cycle accurate simulation of STT-MRAM main memory, being the first to release detailed timing parameters of this technology from academia, essentially enabling researchers to conduct reliable system level simulation of STT-MRAM using widely accepted existing simulation infrastructure. Our results show that, in HPC domain STT-MRAM provide performance comparable to DRAM. Results from the power estimation indicates that STT-MRAM power consumption increases significantly for Activation/Precharge power while Burst power increases moderately and Background power does not deviate much from DRAM. The thesis includes detailed STT-MRAM main memory timing parameters to the main repositories of DramSim2 and Ramulator, two of the most widely used and accepted state-of-the-art main memory simulators. The STT-MRAM timing parameters that has been originated as a part of this thesis, are till date the only reliable and publicly available timing information on this memory technology published from academia. Finally, the thesis analyzes the feasibility of using STT-MRAM in real-time embedded systems by investigating STT-MRAM main memory impact on average system performance and WCET. STT-MRAM's suitability for the real-time embedded systems is validated on benchmarks provided by the European Space Agency (ESA), EEMBC Autobench and MediaBench suite by analyzing performance and WCET impact. In quantitative terms, our results show that STT-MRAM main memory in real-time embedded systems provides performance and WCET comparable to conventional DRAM, while opening up opportunities to exploit various advantages.Es cuestionable si DRAM continuará escalando y cumplirá con las necesidades de los sistemas de la próxima generación. Por lo tanto, se invierte un esfuerzo significativo en la investigación y el desarrollo de nuevas tecnologías de memoria. Uno de los candidatos para la memoria de próxima generación es la Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM). STT-MRAM es una memoria no volátil emergente con un gran potencial que podría ser explotada para diversos requisitos de diferentes sistemas informáticos. Al ser una tecnología novedosa, los dispositivos STT-MRAM ya se están acercando a la DRAM en términos de capacidad, frecuencia y tamaño del dispositivo. Las características especiales de STTMRAM, como la dureza intrínseca a la radiación, la no volatilidad, la potencia de reserva cero y la capacidad de funcionar en temperaturas extremas, también lo hacen especialmente adecuado para aplicaciones aeroespaciales, de aviónica y automotriz. A pesar de ser una alternativa concebible para la tecnología de memoria principal, hasta la fecha, la investigación académica de la memoria principal de STT-MRAM sigue siendo marginal. Esto se debe principalmente a la falta de disponibilidad de los parámetros de tiempo detallados públicamente disponibles de esta nueva tecnología, que se requieren para realizar un ciclo de simulación de memoria principal precisa. Algunos investigadores adoptan modelos de memoria simplistas para simular la memoria principal, pero tales modelos pueden introducir errores significativos en el análisis del rendimiento general del sistema. Por lo tanto, los parámetros de tiempo detallados son indispensables para cualquier evaluación o estudio de exploración de la arquitectura de la memoria principal de STT-MRAM. Estos parámetros detallados no están disponibles públicamente porque los fabricantes de STT-MRAM son reacios a divulgar información delicada sobre la tecnología. Esta tesis demuestra un enfoque para realizar un ciclo de simulación precisa de la memoria principal de STT-MRAM, siendo el primero en lanzar parámetros de tiempo detallados de esta tecnología desde la academia, lo que esencialmente permite a los investigadores realizar una simulación confiable a nivel de sistema de STT-MRAM utilizando una simulación existente ampliamente aceptada infraestructura. Nuestros resultados muestran que, en el dominio HPC, STT-MRAM proporciona un rendimiento comparable al de la DRAM. Los resultados de la estimación de potencia indican que el consumo de potencia de STT-MRAM aumenta significativamente para la activation/Precharge power, mientras que la Burst power aumenta moderadamente y la Background power no se desvía mucho de la DRAM. La tesis incluye parámetros detallados de temporización memoria principal de STT-MRAM a los repositorios principales de DramSim2 y Ramulator, dos de los simuladores de memoria principal más avanzados y más utilizados y aceptados. Los parámetros de tiempo de STT-MRAM que se han originado como parte de esta tesis, son hasta la fecha la única información de tiempo confiable y disponible al público sobre esta tecnología de memoria publicada desde la academia. Finalmente, la tesis analiza la viabilidad de usar STT-MRAM en real-time embedded systems mediante la investigación del impacto de la memoria principal de STT-MRAM en el rendimiento promedio del sistema y WCET. La idoneidad de STTMRAM para los real-time embedded systems se valida en los applicaciones proporcionados por la European Space Agency (ESA), EEMBC Autobench y MediaBench, al analizar el rendimiento y el impacto de WCET. En términos cuantitativos, nuestros resultados muestran que la memoria principal de STT-MRAM en real-time embedded systems proporciona un desempeño WCET comparable al de una memoria DRAM convencional, al tiempo que abre oportunidades para explotar varias ventajas

HPC memory systems: Implications of system simulation and checkpointing

The memory system is a significant contributor for most of the current challenges in computer architecture: application performance bottlenecks and operational costs in large data-centers as HPC supercomputers. With the advent of emerging memory technologies, the exploration for novel designs on the memory hierarchy for HPC systems is an open invitation for computer architecture researchers to improve and optimize current designs and deployments. System simulation is the preferred approach to perform architectural explorations due to the low cost to prototype hardware systems, acceptable performance estimates, and accurate energy consumption predictions. Despite the broad presence and extensive usage of system simulators, their validation is not standardized; either because the main purpose of the simulator is not meant to mimic real hardware, or because the design assumptions are too narrow on a particular computer architecture topic. This thesis provides the first steps for a systematic methodology to validate system simulators when compared to real systems. We unveil real-machine´s micro-architectural parameters through a set of specially crafted micro-benchmarks. The unveiled parameters are used to upgrade the simulation infrastructure in order to obtain higher accuracy in the simulation domain. To evaluate the accuracy on the simulation domain, we propose the retirement factor, an extension to a well-known application´s performance methodology. Our proposal provides a new metric to measure the impact simulator´s parameter-tuning when looking for the most accurate configuration. We further present the delay queue, a modification to the memory controller that imposes a configurable delay for all memory transactions that reach the main memory devices; evaluated using the retirement factor, the delay queue allows us to identify the sources of deviations between the simulator infrastructure and the real system. Memory accesses directly affect application performance, both in the real-world machine as well as in the simulation accuracy. From single-read access to a unique memory location up to simultaneous read/write operations to a single or multiple memory locations, HPC applications memory usage differs from workload to workload. A property that allows to glimpse on the application´s memory usage is the workload´s memory footprint. In this work, we found a link between HPC workload´s memory footprint and simulation performance. Actual trends on HPC data-center memory deployments and current HPC application’s memory footprint led us to envision an opportunity for emerging memory technologies to include them as part of the reliability support on HPC systems. Emerging memory technologies such as 3D-stacked DRAM are getting deployed in current HPC systems but in limited quantities in comparison with standard DRAM storage making them suitable to use for low memory footprint HPC applications. We exploit and evaluate this characteristic enabling a Checkpoint-Restart library to support a heterogeneous memory system deployed with an emerging memory technology. Our implementation imposes negligible overhead while offering a simple interface to allocate, manage, and migrate data sets between heterogeneous memory systems. Moreover, we showed that the usage of an emerging memory technology it is not a direct solution to performance bottlenecks; correct data placement and crafted code implementation are critical when comes to obtain the best computing performance. Overall, this thesis provides a technique for validating main memory system simulators when integrated in a simulation infrastructure and compared to real systems. In addition, we explored a link between the workload´s memory footprint and simulation performance on current HPC workloads. Finally, we enabled low memory footprint HPC applications with resilience support while transparently profiting from the usage of emerging memory deployments.El sistema de memoria es el mayor contribuidor de los desafíos actuales en el campo de la arquitectura de ordenadores como lo son los cuellos de botella en el rendimiento de las aplicaciones, así como los costos operativos en los grandes centros de datos. Con la llegada de tecnologías emergentes de memoria, existe una invitación para que los investigadores mejoren y optimicen las implementaciones actuales con novedosos diseños en la jerarquía de memoria. La simulación de los ordenadores es el enfoque preferido para realizar exploraciones de arquitectura debido al bajo costo que representan frente a la realización de prototipos físicos, arrojando estimaciones de rendimiento aceptables con predicciones precisas. A pesar del amplio uso de simuladores de ordenadores, su validación no está estandarizada ya sea porque el propósito principal del simulador no es imitar al sistema real o porque las suposiciones de diseño son demasiado específicas. Esta tesis proporciona los primeros pasos hacia una metodología sistemática para validar simuladores de ordenadores cuando son comparados con sistemas reales. Primero se descubren los parámetros de microarquitectura en la máquina real a través de un conjunto de micro-pruebas diseñadas para actualizar la infraestructura de simulación con el fin de mejorar la precisión en el dominio de la simulación. Para evaluar la precisión de la simulación, proponemos "el factor de retiro", una extensión a una conocida herramienta para medir el rendimiento de las aplicaciones, pero enfocada al impacto del ajuste de parámetros en el simulador. Además, presentamos "la cola de retardo", una modificación virtual al controlador de memoria que agrega un retraso configurable a todas las transacciones de memoria que alcanzan la memoria principal. Usando el factor de retiro, la cola de retraso nos permite identificar el origen de las desviaciones entre la infraestructura del simulador y el sistema real. Todos los accesos de memoria afectan directamente el rendimiento de la aplicación. Desde el acceso de lectura a una única localidad memoria hasta operaciones simultáneas de lectura/escritura a una o varias localidades de memoria, una propiedad que permite reflejar el uso de memoria de la aplicación es su "huella de memoria". En esta tesis encontramos un vínculo entre la huella de memoria de las aplicaciones de alto desempeño y su rendimiento en simulación. Las tecnologías de memoria emergentes se están implementando en sistemas de alto desempeño en cantidades limitadas en comparación con la memoria principal haciéndolas adecuadas para su uso en aplicaciones con baja huella de memoria. En este trabajo, habilitamos y evaluamos el uso de un sistema de memoria heterogéneo basado en un sistema emergente de memoria. Nuestra implementación agrega una carga despreciable al mismo tiempo que ofrece una interfaz simple para ubicar, administrar y migrar datos entre sistemas de memoria heterogéneos. Además, demostramos que el uso de una tecnología de memoria emergente no es una solución directa a los cuellos de botella en el desempeño. La implementación es fundamental a la hora de obtener el mejor rendimiento ya sea ubicando correctamente los datos, o bien diseñando código especializado. En general, esta tesis proporciona una técnica para validar los simuladores respecto al sistema de memoria principal cuando se integra en una infraestructura de simulación y se compara con sistemas reales. Además, exploramos un vínculo entre la huella de memoria de la carga de trabajo y el rendimiento de la simulación en cargas de trabajo de aplicaciones de alto desempeño. Finalmente, habilitamos aplicaciones de alto desempeño con soporte de resiliencia mientras que se benefician de manera transparente con el uso de un sistema de memoria emergente.Postprint (published version

Evaluation of STT-MRAM main memory for HPC and real-time systems

It is questionable whether DRAM will continue to scale and will meet the needs of next-generation systems. Therefore, significant effort is invested in research and development of novel memory technologies. One of the candidates for nextgeneration memory is Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM). STT-MRAM is an emerging non-volatile memory with a lot of potential that could be exploited for various requirements of different computing systems. Being a novel technology, STT-MRAM devices are already approaching DRAM in terms of capacity, frequency and device size. Special STT-MRAM features such as intrinsic radiation hardness, non-volatility, zero stand-by power and capability to function in extreme temperatures also make it particularly suitable for aerospace, avionics and automotive applications. Despite of being a conceivable alternative for main memory technology, to this day, academic research of STT-MRAM main memory remains marginal. This is mainly due to the unavailability of publicly available detailed timing parameters of this novel technology, which are required to perform a cycle accurate main memory simulation. Some researchers adopt simplistic memory models to simulate main memory, but such models can introduce significant errors in the analysis of the overall system performance. Therefore, detailed timing parameters are a must-have for any evaluation or architecture exploration study of STT-MRAM main memory. These detailed parameters are not publicly available because STT-MRAM manufacturers are reluctant to release any delicate information on the technology. This thesis demonstrates an approach to perform a cycle accurate simulation of STT-MRAM main memory, being the first to release detailed timing parameters of this technology from academia, essentially enabling researchers to conduct reliable system level simulation of STT-MRAM using widely accepted existing simulation infrastructure. Our results show that, in HPC domain STT-MRAM provide performance comparable to DRAM. Results from the power estimation indicates that STT-MRAM power consumption increases significantly for Activation/Precharge power while Burst power increases moderately and Background power does not deviate much from DRAM. The thesis includes detailed STT-MRAM main memory timing parameters to the main repositories of DramSim2 and Ramulator, two of the most widely used and accepted state-of-the-art main memory simulators. The STT-MRAM timing parameters that has been originated as a part of this thesis, are till date the only reliable and publicly available timing information on this memory technology published from academia. Finally, the thesis analyzes the feasibility of using STT-MRAM in real-time embedded systems by investigating STT-MRAM main memory impact on average system performance and WCET. STT-MRAM's suitability for the real-time embedded systems is validated on benchmarks provided by the European Space Agency (ESA), EEMBC Autobench and MediaBench suite by analyzing performance and WCET impact. In quantitative terms, our results show that STT-MRAM main memory in real-time embedded systems provides performance and WCET comparable to conventional DRAM, while opening up opportunities to exploit various advantages.Es cuestionable si DRAM continuará escalando y cumplirá con las necesidades de los sistemas de la próxima generación. Por lo tanto, se invierte un esfuerzo significativo en la investigación y el desarrollo de nuevas tecnologías de memoria. Uno de los candidatos para la memoria de próxima generación es la Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM). STT-MRAM es una memoria no volátil emergente con un gran potencial que podría ser explotada para diversos requisitos de diferentes sistemas informáticos. Al ser una tecnología novedosa, los dispositivos STT-MRAM ya se están acercando a la DRAM en términos de capacidad, frecuencia y tamaño del dispositivo. Las características especiales de STTMRAM, como la dureza intrínseca a la radiación, la no volatilidad, la potencia de reserva cero y la capacidad de funcionar en temperaturas extremas, también lo hacen especialmente adecuado para aplicaciones aeroespaciales, de aviónica y automotriz. A pesar de ser una alternativa concebible para la tecnología de memoria principal, hasta la fecha, la investigación académica de la memoria principal de STT-MRAM sigue siendo marginal. Esto se debe principalmente a la falta de disponibilidad de los parámetros de tiempo detallados públicamente disponibles de esta nueva tecnología, que se requieren para realizar un ciclo de simulación de memoria principal precisa. Algunos investigadores adoptan modelos de memoria simplistas para simular la memoria principal, pero tales modelos pueden introducir errores significativos en el análisis del rendimiento general del sistema. Por lo tanto, los parámetros de tiempo detallados son indispensables para cualquier evaluación o estudio de exploración de la arquitectura de la memoria principal de STT-MRAM. Estos parámetros detallados no están disponibles públicamente porque los fabricantes de STT-MRAM son reacios a divulgar información delicada sobre la tecnología. Esta tesis demuestra un enfoque para realizar un ciclo de simulación precisa de la memoria principal de STT-MRAM, siendo el primero en lanzar parámetros de tiempo detallados de esta tecnología desde la academia, lo que esencialmente permite a los investigadores realizar una simulación confiable a nivel de sistema de STT-MRAM utilizando una simulación existente ampliamente aceptada infraestructura. Nuestros resultados muestran que, en el dominio HPC, STT-MRAM proporciona un rendimiento comparable al de la DRAM. Los resultados de la estimación de potencia indican que el consumo de potencia de STT-MRAM aumenta significativamente para la activation/Precharge power, mientras que la Burst power aumenta moderadamente y la Background power no se desvía mucho de la DRAM. La tesis incluye parámetros detallados de temporización memoria principal de STT-MRAM a los repositorios principales de DramSim2 y Ramulator, dos de los simuladores de memoria principal más avanzados y más utilizados y aceptados. Los parámetros de tiempo de STT-MRAM que se han originado como parte de esta tesis, son hasta la fecha la única información de tiempo confiable y disponible al público sobre esta tecnología de memoria publicada desde la academia. Finalmente, la tesis analiza la viabilidad de usar STT-MRAM en real-time embedded systems mediante la investigación del impacto de la memoria principal de STT-MRAM en el rendimiento promedio del sistema y WCET. La idoneidad de STTMRAM para los real-time embedded systems se valida en los applicaciones proporcionados por la European Space Agency (ESA), EEMBC Autobench y MediaBench, al analizar el rendimiento y el impacto de WCET. En términos cuantitativos, nuestros resultados muestran que la memoria principal de STT-MRAM en real-time embedded systems proporciona un desempeño WCET comparable al de una memoria DRAM convencional, al tiempo que abre oportunidades para explotar varias ventajas.Postprint (published version