Continuous Learning of HPC Infrastructure Models using Big Data Analytics and In-Memory processing Tools

open4siThis work was supported, in parts, by the FP7 ERC Advance project MULTITHERMAN (g.a. 291125), by the EU H2020 FETHPC project ANTAREX (g.a. 67623) and by the EU H2020 FETHPC project Exanode (g.a. 671578).Exascale computing represents the next leap in the HPC race. Reaching this level of performance is subject to several engineering challenges such as energy consumption, equipment-cooling, reliability and massive parallelism. Model-based optimization is an essential tool in the design process and control of energy efficient, reliable and thermally constrained systems. However, in the Exascale domain, model learning techniques tailored to the specific supercomputer require real measurements and must therefore handle and analyze a massive amount of data coming from the HPC monitoring infrastructure. This becomes rapidly a 'big data' scale problem. The common approach where measurements are first stored in large databases and then processed is no more affordable due to the increasingly storage costs and lack of real-time support. Nowadays instead, cloud-based machine learning techniques aim to build on-line models using real-time approaches such as 'stream processing' and 'in-memory' computing, that avoid storage costs and enable fastdata processing. Moreover, the fast delivery and adaptation of the models to the quick data variations, make the decision stage of the optimization loop more effective and reliable. In this paper we leverage scalable, lightweight and flexible IoT technologies, such as the MQTT protocol, to build a highly scalable HPC monitoring infrastructure able to handle the massive sensor data produced by next-gen HPC components. We then show how state-of-the art tools for big data computing and analysis, such as Apache Spark, can be used to manage the huge amount of data delivered by the monitoring layer and to build adaptive models in real-time using on-line machine learning techniques.openBeneventi, Francesco; Bartolini, Andrea; Cavazzoni, Carlo; Benini, LucaBeneventi, Francesco; Bartolini, Andrea; Cavazzoni, Carlo; Benini, Luc

Influence of Manufacturing Constraints on the Topology Optimization of an Automotive Dashboard

Topology Optimization (TO) methods optimize material layout to design light-weight and high-performance products. However, TO methods, applied for components or assembly with high complexity shape or for structures with copious number of parts respectively, do not usually take into account the manufacturability of the optimized geometries, then a heavy further work is required to engineer the product, risking to compromise the mass reduction achieved. Within an Industry 4.0 approach, we propose to evaluate manufacturing constraints since early stages of the conceptual design to perform a TO coherent with the manufacturing technology chosen. Several approaches of TO with different manufacturing constraints such as casting and extrusion are proposed and each solution is compared. The optimum conceptual design is determined in order to minimize the component weight while satisfying both the structural targets and the manufacturing constraints; a case study on a high-performance sport car dashboard is finally presented

User Plane Function Offloading in P4 switches for enhanced 5G Mobile Edge Computing

This demo shows a 5G X-haul testbed enhanced with P4 switches implementing the offloading of the User Plane Function module. The P4 code includes GTP protocol encapsulation/decapsulation function, fully configurable N3-N6-N9 steering, and advanced online monitoring of the experienced latency metadata

Hypertonic Stress and Amino Acid Deprivation Both Increase Expression of mRNA for Amino Acid Transport System A

Amino acid transport system A (SNAT2) in Chinese hamster ovary (CHO-K1) cells was stimulated when the cells were depleted of amino acids or subjected to hypertonic stress. Each of these stresses also caused increases in the abundance of SNAT2 mRNA in these cells. Similar results were obtained with Madin-Darby canine kidney (MDCK) cells, porcine pulmonary artery endothelial cells and human WI-38 fibroblasts. We conclude that the cellular signal transduction pathways for hypertonic stress and amino acid starvation must converge at or before transcription of the message for SNAT2

Metabolism of the EGFR tyrosin kinase inhibitor gefitinib by cytochrome P450 1A1 enzyme in EGFR-wild type non small cell lung cancer cell lines

<p>Abstract</p> <p>Background</p> <p>Gefitinib is a tyrosine kinase inhibitor (TKI) of the epidermal growth factor receptor (EGFR) especially effective in tumors with activating EGFR gene mutations while EGFR wild-type non small cell lung cancer (NSCLC) patients at present do not benefit from this treatment.</p> <p>The primary site of gefitinib metabolism is the liver, nevertheless tumor cell metabolism can significantly affect treatment effectiveness.</p> <p>Results</p> <p>In this study, we investigated the intracellular metabolism of gefitinib in a panel of EGFR wild-type gefitinib-sensitive and -resistant NSCLC cell lines, assessing the role of cytochrome P450 1A1 (CYP1A1) inhibition on gefitinib efficacy. Our results indicate that there is a significant difference in drug metabolism between gefitinib-sensitive and -resistant cell lines. Unexpectedly, only sensitive cells metabolized gefitinib, producing metabolites which were detected both inside and outside the cells. As a consequence of gefitinib metabolism, the intracellular level of gefitinib was markedly reduced after 12-24 h of treatment. Consistent with this observation, RT-PCR analysis and EROD assay showed that mRNA and activity of CYP1A1 were present at significant levels and were induced by gefitinib only in sensitive cells. Gefitinib metabolism was elevated in crowded cells, stimulated by exposure to cigarette smoke extract and prevented by hypoxic condition. It is worth noting that the metabolism of gefitinib in the sensitive cells is a consequence and not the cause of drug responsiveness, indeed treatment with a CYP1A1 inhibitor increased the efficacy of the drug because it prevented the fall in intracellular gefitinib level and significantly enhanced the inhibition of EGFR autophosphorylation, MAPK and PI3K/AKT/mTOR signalling pathways and cell proliferation.</p> <p>Conclusion</p> <p>Our findings suggest that gefitinib metabolism in lung cancer cells, elicited by CYP1A1 activity, might represent an early assessment of gefitinib responsiveness in NSCLC cells lacking activating mutations. On the other hand, in metabolizing cells, the inhibition of CYP1A1 might lead to increased local exposure to the active drug and thus increase gefitinib potency.</p

Combined Inhibition of CDK4/6 and PI3K/AKT/mTOR Pathways Induces a Synergistic Anti-Tumor Effect in Malignant Pleural Mesothelioma Cells.

Malignant pleural mesothelioma (MPM) is a progressive malignancy associated to the exposure of asbestos fibers. The most frequently inactivated tumor suppressor gene in MPM is CDKN2A/ARF, encoding for the cell cycle inhibitors p16INK4a and p14ARF, deleted in about 70% of MPM cases. Considering the high frequency of alterations of this gene, we tested in MPM cells the efficacy of palbociclib (PD-0332991), a highly selective inhibitor of cyclin-dependent kinase (CDK) 4/6. The analyses were performed on a panel of MPM cell lines and on two primary culture cells from pleural effusion of patients with MPM. All the MPM cell lines, as well as the primary cultures, were sensitive to palbociclib with a significant blockade in G0/G1 phase of the cell cycle and with the acquisition of a senescent phenotype. Palbociclib reduced the phosphorylation levels of CDK6 and Rb, the expression of myc with a concomitant increased phosphorylation of AKT. Based on these results, we tested the efficacy of the combination of palbociclib with the PI3K inhibitors NVP-BEZ235 or NVP-BYL719. After palbociclib treatment, the sequential association with PI3K inhibitors synergistically hampered cell proliferation and strongly increased the percentage of senescent cells. In addition, AKT activation was repressed while p53 and p21 were up-regulated. Interestingly, two cycles of sequential drug administration produced irreversible growth arrest and senescent phenotype that were maintained even after drug withdrawal. These findings suggest that the sequential association of palbociclib with PI3K inhibitors may represent a valuable therapeutic option for the treatment of MPM

Epidermal Growth Factor Receptor Intron-1 Polymorphism Predicts Gefitinib Outcome in Advanced Non-small Cell Lung Cancer

IntroductionEpidermal growth factor receptor (EGFR) gene intron 1 contains a polymorphic single sequence dinucleotide repeat (CA)n whose length has been found to inversely correlate with transcriptional activity. This study was designed to assess the role of (CA)n polymorphism in predicting the outcome of gefitinib treatment in advanced non-small cell lung cancer (NSCLC).MethodsBlood and tumor tissue from 58 patients with advanced NSCLC submitted to gefitinib were collected. EGFR intron 1 gene polymorphism, along with EGFR gene mutation, gene copy number and immunohistochemistry expression were determined. Moreover, a panel of lung cancer cell lines characterized for EGFR intron 1 polymorphism was also studied.ResultsEGFR intron 1 polymorphism showed a statistically significant correlation with the gefitinib response (response rate 25 versus 0%, for patients with a (CA)16 and with a (CA)else genotype, respectively; p = 0.044). Patients with a (CA)16 genotype had a longer survival compared with those with a (CA)else genotype (11.4 versus 4.8 months, respectively; p = 0.037). In addition, cell lines lacking the (CA)16 allele showed a statistically significant higher IC50 compared with cell lines bearing at least one (CA)16 allele (p = 0.003).ConclusionsThis study supports a potential role of EGFR intron 1 polymorphism in predicting the outcome of gefitinib treatment in advanced NSCLC