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As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. In this work, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based on different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work. ; This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award Number 66905, program manager Lucy Nowell. Pacific Northwest National Laboratory is operated by Battelle for DOE under Contract DE-AC05-76RL01830. In addition, this material is based upon work supported by the National Science Foundation under Grant No. 1619253, and also by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, program manager Lucy Nowell, under contract number DE-AC02-06CH11357 (DOE Catalog project) and in part by the European Union FEDER funds under contract TIN2015-65316-P. ; Peer Reviewed ; Postprint (author's final draft)

Large scale time-dependent particle simulations can generate massive amounts of data, making it so that storing the results is often the slowest phase and the primary time bottleneck of the simulation. Furthermore, analysing this amount of data with traditional tools has become increasingly challenging, and it is often virtually impossible to have a visual representation of the full set. We propose a novel architecture that integrates an HPC-based multi-physics simulation code, a NoSQL database, and a data analysis and visualisation application. The goals are two: On the one hand, we aim to speed up the simulations taking advantage of the scalability of key-value data stores, while at the same time enabling real-time approximated data visualisation and interactive exploration. On the other hand, we want to make it efficient to explore and analyse the large data base of results produced. Therefore, this work represents a clear example of integrating High Performance Computing with High Performance Data Analytics. Our prototype proves the validity of our approach and shows great performance improvements. Indeed, we reduced by 67.5% the time to store the simulation while we made real-time queries run 52 times faster than alternative solutions. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1). It is also partially supported by the grant SEV-2011-00067 of Severo Ochoa Program, the TIN2015-65316-P project, with funding from the Spanish Ministry of Economy and Competitivity, the European Union FEDER funds, and the SGR 2014-SGR-1051. ; Peer Reviewed ; Postprint (published version)

As performance and energy efficiency have become the main challenges for next-generation high-performance computing, asymmetric multi-core architectures can provide solutions to tackle these issues. Parallel programming models need to be able to suit the needs of such systems and keep on increasing the application's portability and efficiency. This paper proposes two task scheduling approaches that target asymmetric systems. These dynamic scheduling policies reduce total execution time either by detecting the longest or the critical path of the dynamic task dependency graph of the application, or by finding the earliest executor of a task. They use dynamic scheduling and information discoverable during execution, fact that makes them implementable and functional without the need of off-line profiling. In our evaluation we compare these scheduling approaches with two existing state-of the art heterogeneous schedulers and we track their improvement over a FIFO baseline scheduler. We show that the heterogeneous schedulers improve the baseline by up to 1.45 in a real 8-core asymmetric system and up to 2.1 in a simulated 32-core asymmetric chip. ; This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. The Mont-Blanc project receives funding from the EU's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 610402 and from the EU's H2020 Framework Programme (H2020/2014-2020) under grant agreement no 671697. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). ; Peer Reviewed ; Postprint (author's final draft)

Large scale time-dependent particle simulations can generate massive amounts of data, making it so that storing the results is often the slowest phase and the primary time bottleneck of the simulation. Furthermore, analysing this amount of data with traditional tools has become increasingly challenging, and it is often virtually impossible to have a visual representation of the full set. We propose a novel architecture that integrates an HPC-based multi-physics simulation code, a NoSQL database, and a data analysis and visualisation application. The goals are two: On the one hand, we aim to speed up the simulations taking advantage of the scalability of key-value data stores, while at the same time enabling real-time approximated data visualisation and interactive exploration. On the other hand, we want to make it efficient to explore and analyse the large data base of results produced. Therefore, this work represents a clear example of integrating High Performance Computing with High Performance Data Analytics. Our prototype proves the validity of our approach and shows great performance improvements. Indeed, we reduced by 67.5% the time to store the simulation while we made real-time queries run 52 times faster than alternative solutions. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 720270 (HBP SGA1). It is also partially supported by the grant SEV-2011-00067 of Severo Ochoa Program, the TIN2015-65316-P project, with funding from the Spanish Ministry of Economy and Competitivity, the European Union FEDER funds, and the SGR 2014-SGR-1051. ; Peer Reviewed ; Postprint (published version)

The use of the Python programming language for scientific computing has been gaining momentum in the last years. The fact that it is compact and readable and its complete set of scientific libraries are two important characteristics that favour its adoption. Nevertheless, Python still lacks a solution for easily parallelizing generic scripts on distributed infrastructures, since the current alternatives mostly require the use of APIs for message passing or are restricted to embarrassingly parallel computations. In that sense, this paper presents PyCOMPSs, a framework that facilitates the development of parallel computational workflows in Python. In this approach, the user programs her script in a sequential fashion and decorates the functions to be run as asynchronous parallel tasks. A runtime system is in charge of exploiting the inherent concurrency of the script, detecting the data dependencies between tasks and spawning them to the available resources. Furthermore, we show how this programming model can be built on top of a Big Data storage architecture, where the data stored in the backend is abstracted and accessed from the application in the form of persistent objects. ; This work has been supported by the following institutions: the Spanish Government with grant SEV-2011-00067 of Severo Ochoa Program and contract Computaci´on de Altas Prestaciones VI (TIN2012-34557); by the SGR programme (2014-SGR-1051) of the Catalan Government; by the project The Human Brain Project, funded by the European Commission under contract 604102; and by the Intel-BSC Exascale Lab collaboration. ; Peer Reviewed ; Postprint (author's final draft)

As performance and energy efficiency have become the main challenges for next-generation high-performance computing, asymmetric multi-core architectures can provide solutions to tackle these issues. Parallel programming models need to be able to suit the needs of such systems and keep on increasing the application's portability and efficiency. This paper proposes two task scheduling approaches that target asymmetric systems. These dynamic scheduling policies reduce total execution time either by detecting the longest or the critical path of the dynamic task dependency graph of the application, or by finding the earliest executor of a task. They use dynamic scheduling and information discoverable during execution, fact that makes them implementable and functional without the need of off-line profiling. In our evaluation we compare these scheduling approaches with two existing state-of the art heterogeneous schedulers and we track their improvement over a FIFO baseline scheduler. We show that the heterogeneous schedulers improve the baseline by up to 1.45 in a real 8-core asymmetric system and up to 2.1 in a simulated 32-core asymmetric chip. ; This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. The Mont-Blanc project receives funding from the EU's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 610402 and from the EU's H2020 Framework Programme (H2020/2014-2020) under grant agreement no 671697. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). ; Peer Reviewed ; Postprint (author's final draft)

Deep neural networks are representation learning techniques. During training, a deep net is capable of generating a descriptive language of unprecedented size and detail in machine learning. Extracting the descriptive language coded within a trained CNN model (in the case of image data), and reusing it for other purposes is a field of interest, as it provides access to the visual descriptors previously learnt by the CNN after processing millions of images, without requiring an expensive training phase. Contributions to this field (commonly known as feature representation transfer or transfer learning) have been purely empirical so far, extracting all CNN features from a single layer close to the output and testing their performance by feeding them to a classifier. This approach has provided consistent results, although its relevance is limited to classification tasks. In a completely different approach, in this paper we statistically measure the discriminative power of every single feature found within a deep CNN, when used for characterizing every class of 11 datasets. We seek to provide new insights into the behavior of CNN features, particularly the ones from convolutional layers, as this can be relevant for their application to knowledge representation and reasoning. Our results confirm that low and middle level features may behave differently to high level features, but only under certain conditions. We find that all CNN features can be used for knowledge representation purposes both by their presence or by their absence, doubling the information a single CNN feature may provide. We also study how much noise these features may include, and propose a thresholding approach to discard most of it. All these insights have a direct application to the generation of CNN embedding spaces. ; This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of ...

Energy efficiency has become the main challenge for high performance computing (HPC). The use of mobile asymmetric multi-core architectures to build future multi-core systems is an approach towards energy savings while keeping high performance. However, it is not known yet whether such systems are ready to handle parallel applications. This paper fills this gap by evaluating emerging parallel applications on an asymmetric multi-core. We make use of the PARSEC benchmark suite and a processor that implements the ARM big.LITTLE architecture. We conclude that these applications are not mature enough to run on such systems, as they suffer from load imbalance. Furthermore, we explore the behaviour of dynamic scheduling solutions on either the Operating System (OS) or the runtime level. Comparing these approaches shows us that the most efficient scheduling takes place in the runtime level, influencing the future research towards such solutions. ; This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. The Mont-Blanc project receives funding from the EU's Seventh Framework Programme (FP7/2007-2013) under grant agreement number 610402 and from the EU's H2020 Framework Programme (H2020/2014-2020) under grant agreement number 671697. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). ; Peer Reviewed ; Postprint (author's final draft)

Energy efficiency has become the main challenge for high performance computing (HPC). The use of mobile asymmetric multi-core architectures to build future multi-core systems is an approach towards energy savings while keeping high performance. However, it is not known yet whether such systems are ready to handle parallel applications. This paper fills this gap by evaluating emerging parallel applications on an asymmetric multi-core. We make use of the PARSEC benchmark suite and a processor that implements the ARM big.LITTLE architecture. We conclude that these applications are not mature enough to run on such systems, as they suffer from load imbalance. Furthermore, we explore the behaviour of dynamic scheduling solutions on either the Operating System (OS) or the runtime level. Comparing these approaches shows us that the most efficient scheduling takes place in the runtime level, influencing the future research towards such solutions. ; This work has been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P), by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), by the RoMoL ERC Advanced Grant (GA 321253) and the European HiPEAC Network of Excellence. The Mont-Blanc project receives funding from the EU's Seventh Framework Programme (FP7/2007-2013) under grant agreement number 610402 and from the EU's H2020 Framework Programme (H2020/2014-2020) under grant agreement number 671697. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP B 00243). ; Peer Reviewed ; Postprint (author's final draft)

Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes. Typically, deep network representations are implemented within vector embedding spaces, which enables the use of traditional machine learning algorithms on top of them. In this short paper we propose the construction of a graph embedding space instead, introducing a methodology to transform the knowledge coded within a deep convolutional network into a topological space (i.e. a network). We outline how such graph can hold data instances, data features, relations between instances and features, and relations among features. Finally, we introduce some preliminary experiments to illustrate how the resultant graph embedding space can be exploited through graph analytics algorithms ; This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015- 0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the Core Research for Evolutional Science and Technology (CREST) program of Japan Science and Technology Agency (JST). ; Peer Reviewed ; Postprint (published version)

The current state-of-the-art for image annotation and image retrieval tasks is obtained through deep neural networks, which combine an image representation and a text representation into a shared embedding space. In this paper we evaluate the impact of using the Full-Network embedding in this setting, replacing the original image representation in a competitive multimodal embedding generation scheme. Unlike the one-layer image embeddings typically used by most approaches, the Full-Network embedding provides a multi-scale representation of images, which results in richer characterizations. To measure the influence of the Full-Network embedding, we evaluate its performance on three different datasets, and compare the results with the original multimodal embedding generation scheme when using a one-layer image embedding, and with the rest of the state-of-the-art. Results for image annotation and image retrieval tasks indicate that the Full-Network embedding is consistently superior to the one-layer embedding. These results motivate the integration of the Full-Network embedding on any multimodal embedding generation scheme, something feasible thanks to the flexibility of the approach ; This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015- 0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the Core Research for Evolutional Science and Technology (CREST) program of Japan Science and Technology Agency (JST). ; Peer Reviewed ; Postprint (published version)

Convolutional Neural Networks (CNN) are the most popular of deep network models due to their applicability and success in image processing. Although plenty of effort has been made in designing and training better discriminative CNNs, little is yet known about the internal features these models learn. Questions like, what specific knowledge is coded within CNN layers, and how can it be used for other purposes besides discrimination, remain to be answered. To advance in the resolution of these questions, in this work we extract features from CNN layers, building vector representations from CNN activations. The resultant vector embedding is used to represent first images and then known image classes. On those representations we perform an unsupervised clustering process, with the goal of studying the hidden semantics captured in the embedding space. Several abstract entities untaught to the network emerge in this process, effectively defining a taxonomy of knowledge as perceived by the CNN. We evaluate and interpret these sets using WordNet, while studying the different behaviours exhibited by the layers of a CNN model according to their depth. Our results indicate that, while top (i.e., deeper) layers provide the most representative space, low layers also define descriptive dimensions. ; This work was partially supported by the IBM/BSC Technology Center for Supercomputing (Joint Study Agreement, No. W156463), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051). ; Peer Reviewed ; Postprint (author's final draft)

Convolutional Neural Networks (CNN) are the most popular of deep network models due to their applicability and success in image processing. Although plenty of effort has been made in designing and training better discriminative CNNs, little is yet known about the internal features these models learn. Questions like, what specific knowledge is coded within CNN layers, and how can it be used for other purposes besides discrimination, remain to be answered. To advance in the resolution of these questions, in this work we extract features from CNN layers, building vector representations from CNN activations. The resultant vector embedding is used to represent first images and then known image classes. On those representations we perform an unsupervised clustering process, with the goal of studying the hidden semantics captured in the embedding space. Several abstract entities untaught to the network emerge in this process, effectively defining a taxonomy of knowledge as perceived by the CNN. We evaluate and interpret these sets using WordNet, while studying the different behaviours exhibited by the layers of a CNN model according to their depth. Our results indicate that, while top (i.e., deeper) layers provide the most representative space, low layers also define descriptive dimensions. ; This work was partially supported by the IBM/BSC Technology Center for Supercomputing (Joint Study Agreement, No. W156463), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051). ; Peer Reviewed ; Postprint (author's final draft)

This paper presents the new features of the OmpSs@FPGA framework. OmpSs is a data-flow programming model that supports task nesting and dependencies to target asynchronous parallelism and heterogeneity. OmpSs@FPGA is the extension of the programming model addressed specifically to FPGAs. OmpSs environment is built on top of Mercurium source to source compiler and Nanos++ runtime system. To address FPGA specifics Mercurium compiler implements several FPGA related features as local variable caching, wide memory accesses or accelerator replication. In addition, part of the Nanos++ runtime has been ported to hardware. Driven by the compiler this new hardware runtime adds new features to FPGA codes, such as task creation and dependence management, providing both performance increases and ease of programming. To demonstrate these new capabilities, different high performance benchmarks have been evaluated over different FPGA platforms using the OmpSs programming model. The results demonstrate that programs that use the OmpSs programming model achieve very competitive performance with low to moderate porting effort compared to other FPGA implementations. ; This work has received funding from EuroEXA project (European Union's Horizon 2020 Research and Innovation Programme, under grant agreement No 754337), from Spanish Government (projects PID2019-107255GB and SEV-2015- 0493, grant BES-2016-078046), and Generalitat de Catalunya (contracts 2017-SGR-1414 and 2017-SGR-1328). ; Peer Reviewed ; Postprint (author's final draft)