Block size estimation for data partitioning in HPC applications using machine learning techniques

The extensive use of HPC infrastructures and frameworks for running data-intensive applications has led to a growing interest in data partitioning techniques and strategies. In fact, finding an effective partitioning, i.e. a suitable size for data blocks, is a key strategy to speed-up parallel data-intensive applications and increase scalability. This paper describes a methodology for data block size estimation in HPC applications, which relies on supervised machine learning techniques. The implementation of the proposed methodology was evaluated using as a testbed dislib, a distributed computing library highly focused on machine learning algorithms built on top of the PyCOMPSs framework. We assessed the effectiveness of our solution through an extensive experimental evaluation considering different algorithms, datasets, and infrastructures, including the MareNostrum 4 supercomputer. The results we obtained show that the methodology is able to efficiently determine a suitable way to split a given dataset, thus enabling the efficient execution of data-parallel applications in high performance environments

Nickel Complexes of Diphosphine Ketone and Imine Ligands : Metal-Ligand Cooperation and Application in Hydrosilylation and Alkyne Cyclotrimerization Reactions

Recent years have witnessed the application of homogenous catalysts in many chemical transformations, impacting chemistry in both industry and academia. The sustainability of the catalysis itself can however still be a point of improvement, as many of the efficient catalytic transformations rely on expensive, rare and generally relatively toxic metals. The transition to more sustainable alternatives such as nickel can benefit from the design of new types of molecular catalysts in which an organic part (a ligand) cooperates with the metal to facilitate chemical reactions. Such metal-ligand cooperation can for example arise when relatively weakly binding π-ligands such as imines (C=N) and ketones (C=O) are covalently tethered to strongly binding phosphorus ligands. In this thesis, the utility of this kind of ligands in Ni-catalyzed reactions is investigated. The role of the side-on π-coordinated C=O and C=N sites is extensively studied by both experimental and computational analysis. First, the metal-ligand hemilability at tethered ketone π-acceptor ligands can be used as a promising strategy in nickel catalysis, improving the activity and selectivity of a particular transformation. The ketone ligand shows versatile coordination at its π-acceptor site of which the binding mode responds to the electronic properties of the metal center and the specific requirements of elementary steps. The adaptive character of the ligand provided by the π-acceptor C=O moiety opens up mechanistic pathways that lead to an enhanced catalytic performance in alkyne cyclotrimerization, as demonstrated by comparison with various related Ni–complexes for which this specific π-hemilabile reactivity is not accessible. In addition, the ability of the ligand to adapt its coordination mode according the nature of the substrate/coligand used is an interesting property to further explore for the development of catalytic protocols involving other types of substrates, potentially opening up new venues in cooperative catalysis. Secondly, the general applicability of a nickel complex of a diphosphine–imine ligand in an industrially relevant process such as alkene hydrosilylation is highlighted, showing compatibility towards a broad range of olefins containing sensitive group functionalities. Furthermore, mechanistic investigations reveal that PPh3 positively affects the selectivity of the hydrosilylation. This finding may have general implications in hydrosilylation reactions: the choice of the coligand can potentially affect the outcome of the reaction, both in term of activity and selectivity. In addition to the contribution of PPh3 in hydrosilylation reactions, the synthesis and reactivity of the diphosphine–aminosilyl derivative of the Ni–catalyst offer a better mechanistic understanding of the reaction. Remarkably, the aminosilyl unit is the reactive site in transformations involving hydrosilane substrates; it suggests that transient Si–N bond formations are of importance in the catalytic hydrosilylation performance of nickel complexes of nitrogen-containing ligands. The findings that both the aminosilyl and the PPh3 fragments plays a role in the catalytic performance of hydrosilylation processes can open up opportunities for the design of new types of metal catalysts of nitrogen-containing ligands. A careful design of organometallic compounds that incorporate both of these concepts can become key features for the optimization of metal catalysts towards a certain reactivity

Nickel-Catalyzed Alkyne Cyclotrimerization Assisted by a Hemilabile Acceptor Ligand: A Computational Study

π-coordinating units incorporated in the supporting ligand of an organometallic complex may open up specific reactive pathways. The diphosphine ketone supported nickel complex [(p-tolL1)Ni(BPI)] (p-tol1; p-tolL1 = 2,2′-bis(di-p-tolylphosphino)benzophenone; BPI = benzophenone imine) has previously been shown to act as an active and selective alkyne cyclotrimerization catalyst. Herein, DFT calculations support an adaptive behavior of the ligand throughout the catalytic cycle, several elementary steps being assisted by coordination or decoordination of the C═O moiety. A comparison with related bi- and tridentate phosphine ligands reveals the key role of the hemilabile π-acceptor moiety for the catalytic activity and selectivity of p-tol1 in alkyne cyclotrimerization

Exploiting Machine Learning for Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines

Workflows are largely used to orchestrate complex sets of operations required to handle and process huge amounts of data. Parallel processing is often vital to reduce execution time when complex data-intensive workflows must be run efficiently, and at the same time, in-memory processing can bring important benefits to accelerate execution. However, optimization techniques are necessary to fully exploit in-memory processing, avoiding performance drops due to memory saturation events. This paper proposed a novel solution, called the Intelligent In-memory Workflow Manager (IIWM), for optimizing the in-memory execution of data-intensive workflows on parallel machines. IIWM is based on two complementary strategies: (1) a machine learning strategy for predicting the memory occupancy and execution time of workflow tasks; (2) a scheduling strategy that allocates tasks to a computing node, taking into account the (predicted) memory occupancy and execution time of each task and the memory available on that node. The effectiveness of the machine learning-based predictor and the scheduling strategy were demonstrated experimentally using as a testbed, Spark, a high-performance Big Data processing framework that exploits in-memory computing to speed up the execution of large-scale applications. In particular, two synthetic workflows were prepared for testing the robustness of the IIWM in scenarios characterized by a high level of parallelism and a limited amount of memory reserved for execution. Furthermore, a real data analysis workflow was used as a case study, for better assessing the benefits of the proposed approach. Thanks to high accuracy in predicting resources used at runtime, the IIWM was able to avoid disk writes caused by memory saturation, outperforming a traditional strategy in which only dependencies among tasks are taken into account. Specifically, the IIWM achieved up to a 31% and a 40% reduction of makespan and a performance improvement up to 1.45× and 1.66× on the synthetic workflows and the real case study, respectively

Enhanced Catalytic Activity of Nickel Complexes of an Adaptive Diphosphine–Benzophenone Ligand in Alkyne Cyclotrimerization

Adaptive ligands, which can adapt their coordination mode to the electronic structure of various catalytic intermediates, offer the potential to develop improved homogeneous catalysts in terms of activity and selectivity. 2,2′-Diphosphinobenzophenones have previously been shown to act as adaptive ligands, the central ketone moiety preferentially coordinating reduced metal centers. Herein, the utility of this scaffold in nickel-catalyzed alkyne cyclotrimerization is investigated. The complex [(p-tolL1)Ni(BPI)] (p-tolL1 = 2,2′-bis(di(para-tolyl)phosphino)-benzophenone; BPI = benzophenone imine) is an active catalyst in the [2 + 2 + 2] cyclotrimerization of terminal alkynes, selectively affording 1,2,4-substituted benzenes from terminal alkynes. In particular, this catalyst outperforms closely related bi- and tridentate phosphine-based Ni catalysts. This suggests a reaction pathway involving a hemilabile interaction of the C═O unit with the nickel center. This is further borne out by a comparative study of the observed resting states and DFT calculations

Enhanced Catalytic Activity of Nickel Complexes of an Adaptive Diphosphine–Benzophenone Ligand in Alkyne Cyclotrimerization

Adaptive ligands, which can adapt their coordination mode to the electronic structure of various catalytic intermediates, offer the potential to develop improved homogeneous catalysts in terms of activity and selectivity. 2,2′-Diphosphinobenzophenones have previously been shown to act as adaptive ligands, the central ketone moiety preferentially coordinating reduced metal centers. Herein, the utility of this scaffold in nickel-catalyzed alkyne cyclotrimerization is investigated. The complex [(p-tolL1)Ni(BPI)] (p-tolL1 = 2,2′-bis(di(para-tolyl)phosphino)-benzophenone; BPI = benzophenone imine) is an active catalyst in the [2 + 2 + 2] cyclotrimerization of terminal alkynes, selectively affording 1,2,4-substituted benzenes from terminal alkynes. In particular, this catalyst outperforms closely related bi- and tridentate phosphine-based Ni catalysts. This suggests a reaction pathway involving a hemilabile interaction of the C═O unit with the nickel center. This is further borne out by a comparative study of the observed resting states and DFT calculations

Nickel Complexes of Diphosphine Ketone and Imine Ligands: Metal-Ligand Cooperation and Application in Hydrosilylation and Alkyne Cyclotrimerization Reactions

Recent years have witnessed the application of homogenous catalysts in many chemical transformations, impacting chemistry in both industry and academia. The sustainability of the catalysis itself can however still be a point of improvement, as many of the efficient catalytic transformations rely on expensive, rare and generally relatively toxic metals. The transition to more sustainable alternatives such as nickel can benefit from the design of new types of molecular catalysts in which an organic part (a ligand) cooperates with the metal to facilitate chemical reactions. Such metal-ligand cooperation can for example arise when relatively weakly binding π-ligands such as imines (C=N) and ketones (C=O) are covalently tethered to strongly binding phosphorus ligands. In this thesis, the utility of this kind of ligands in Ni-catalyzed reactions is investigated. The role of the side-on π-coordinated C=O and C=N sites is extensively studied by both experimental and computational analysis. First, the metal-ligand hemilability at tethered ketone π-acceptor ligands can be used as a promising strategy in nickel catalysis, improving the activity and selectivity of a particular transformation. The ketone ligand shows versatile coordination at its π-acceptor site of which the binding mode responds to the electronic properties of the metal center and the specific requirements of elementary steps. The adaptive character of the ligand provided by the π-acceptor C=O moiety opens up mechanistic pathways that lead to an enhanced catalytic performance in alkyne cyclotrimerization, as demonstrated by comparison with various related Ni–complexes for which this specific π-hemilabile reactivity is not accessible. In addition, the ability of the ligand to adapt its coordination mode according the nature of the substrate/coligand used is an interesting property to further explore for the development of catalytic protocols involving other types of substrates, potentially opening up new venues in cooperative catalysis. Secondly, the general applicability of a nickel complex of a diphosphine–imine ligand in an industrially relevant process such as alkene hydrosilylation is highlighted, showing compatibility towards a broad range of olefins containing sensitive group functionalities. Furthermore, mechanistic investigations reveal that PPh3 positively affects the selectivity of the hydrosilylation. This finding may have general implications in hydrosilylation reactions: the choice of the coligand can potentially affect the outcome of the reaction, both in term of activity and selectivity. In addition to the contribution of PPh3 in hydrosilylation reactions, the synthesis and reactivity of the diphosphine–aminosilyl derivative of the Ni–catalyst offer a better mechanistic understanding of the reaction. Remarkably, the aminosilyl unit is the reactive site in transformations involving hydrosilane substrates; it suggests that transient Si–N bond formations are of importance in the catalytic hydrosilylation performance of nickel complexes of nitrogen-containing ligands. The findings that both the aminosilyl and the PPh3 fragments plays a role in the catalytic performance of hydrosilylation processes can open up opportunities for the design of new types of metal catalysts of nitrogen-containing ligands. A careful design of organometallic compounds that incorporate both of these concepts can become key features for the optimization of metal catalysts towards a certain reactivity

Nickel-Catalyzed Alkyne Cyclotrimerization Assisted by a Hemilabile Acceptor Ligand: A Computational Study

π-coordinating units incorporated in the supporting ligand of an organometallic complex may open up specific reactive pathways. The diphosphine ketone supported nickel complex [(p-tolL1)Ni(BPI)] (p-tol1; p-tolL1 = 2,2′-bis(di-p-tolylphosphino)benzophenone; BPI = benzophenone imine) has previously been shown to act as an active and selective alkyne cyclotrimerization catalyst. Herein, DFT calculations support an adaptive behavior of the ligand throughout the catalytic cycle, several elementary steps being assisted by coordination or decoordination of the C═O moiety. A comparison with related bi- and tridentate phosphine ligands reveals the key role of the hemilabile π-acceptor moiety for the catalytic activity and selectivity of p-tol1 in alkyne cyclotrimerization